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"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__A : int = logging.get_logger(__name__)
__A : Optional[int] = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__A : Optional[int] = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Dict ,snake_case_ : Tuple ,snake_case_ : Optional[int] ,snake_case_ : List[Any] ):
'''simple docstring'''
for attribute in key.split(""".""" ):
UpperCamelCase : str = getattr(snake_case_ ,snake_case_ )
if weight_type is not None:
UpperCamelCase : Dict = getattr(snake_case_ ,snake_case_ ).shape
else:
UpperCamelCase : List[str] = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
UpperCamelCase : int = value
elif weight_type == "weight_g":
UpperCamelCase : str = value
elif weight_type == "weight_v":
UpperCamelCase : Union[str, Any] = value
elif weight_type == "bias":
UpperCamelCase : Union[str, Any] = value
else:
UpperCamelCase : int = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def A_ ( snake_case_ : List[str] ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = []
UpperCamelCase : str = fairseq_model.state_dict()
UpperCamelCase : List[str] = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
UpperCamelCase : str = None
for name, value in fairseq_dict.items():
UpperCamelCase : Optional[Any] = False
if "conv_layers" in name:
load_conv_layer(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,hf_model.config.feat_extract_norm == """group""" ,)
UpperCamelCase : List[str] = True
elif name.split(""".""" )[0] == "proj":
UpperCamelCase : Optional[Any] = fairseq_model.proj
UpperCamelCase : Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
UpperCamelCase : Optional[int] = True
if "*" in mapped_key:
UpperCamelCase : Optional[Any] = name.split(snake_case_ )[0].split(""".""" )[-2]
UpperCamelCase : List[str] = mapped_key.replace("""*""" ,snake_case_ )
if "weight_g" in name:
UpperCamelCase : str = """weight_g"""
elif "weight_v" in name:
UpperCamelCase : Optional[int] = """weight_v"""
elif "bias" in name:
UpperCamelCase : List[str] = """bias"""
elif "weight" in name:
UpperCamelCase : List[str] = """weight"""
else:
UpperCamelCase : int = None
set_recursively(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
continue
if not is_used:
unused_weights.append(snake_case_ )
logger.warning(f'Unused weights: {unused_weights}' )
return proj_weight
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict ,snake_case_ : List[str] ,snake_case_ : Any ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : List[str] = full_name.split("""conv_layers.""" )[-1]
UpperCamelCase : int = name.split(""".""" )
UpperCamelCase : Any = int(items[0] )
UpperCamelCase : Union[str, Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
UpperCamelCase : List[Any] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
UpperCamelCase : List[str] = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
UpperCamelCase : Optional[int] = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
UpperCamelCase : Tuple = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(snake_case_ )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : str = emb.weight.shape
UpperCamelCase : Union[str, Any] = nn.Linear(snake_case_ ,snake_case_ ,bias=snake_case_ )
UpperCamelCase : List[str] = emb.weight.data
return lin_layer
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ,encoding="""utf-8""" ) as f:
UpperCamelCase : int = f.readlines()
UpperCamelCase : List[Any] = [line.split(""" """ )[0] for line in lines]
UpperCamelCase : int = len(snake_case_ )
UpperCamelCase : List[str] = {
"""<s>""": 0,
"""<pad>""": 1,
"""</s>""": 2,
"""<unk>""": 3,
}
vocab_dict.update(dict(zip(snake_case_ ,range(4 ,num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[str] ,snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : Any ,snake_case_ : Optional[Any] ,snake_case_ : Optional[Any] ,):
'''simple docstring'''
UpperCamelCase : List[Any] = WavaVecaConfig.from_pretrained(snake_case_ )
UpperCamelCase : Tuple = SpeechaTextaConfig.from_pretrained(
snake_case_ ,vocab_size=snake_case_ ,decoder_layers=snake_case_ ,do_stable_layer_norm=snake_case_ )
UpperCamelCase : List[str] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_6_0_0_0 ,padding_value=0 ,do_normalize=snake_case_ ,return_attention_mask=snake_case_ ,)
UpperCamelCase , UpperCamelCase , UpperCamelCase : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
UpperCamelCase : Any = model[0].eval()
# set weights for wav2vec2 encoder
UpperCamelCase : Optional[int] = WavaVecaModel(snake_case_ )
UpperCamelCase : List[str] = recursively_load_weights_wavaveca(model.encoder ,snake_case_ )
UpperCamelCase : List[Any] = SpeechaTextaForCausalLM(snake_case_ )
UpperCamelCase , UpperCamelCase : Tuple = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,strict=snake_case_ )
# set output linear layer
unexpected_keys.remove("""embed_out""" )
UpperCamelCase : Any = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}' )
logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}' )
UpperCamelCase : Tuple = SpeechEncoderDecoderModel(encoder=snake_case_ ,decoder=snake_case_ )
UpperCamelCase : Dict = False
# add projection layer
UpperCamelCase : Optional[Any] = nn.Parameter(projection_layer.weight )
UpperCamelCase : int = nn.Parameter(projection_layer.bias )
UpperCamelCase : Optional[Any] = create_vocab_dict(snake_case_ )
with open(os.path.join(snake_case_ ,"""vocab.json""" ) ,"""w""" ) as fp:
json.dump(snake_case_ ,snake_case_ )
UpperCamelCase : List[str] = SpeechaTextaTokenizer(os.path.join(snake_case_ ,"""vocab.json""" ) )
tokenizer.save_pretrained(snake_case_ )
UpperCamelCase : Dict = hf_wavavec.config.to_dict()
UpperCamelCase : str = tokenizer.pad_token_id
UpperCamelCase : List[str] = tokenizer.bos_token_id
UpperCamelCase : List[Any] = tokenizer.eos_token_id
UpperCamelCase : int = """speech_to_text_2"""
UpperCamelCase : Any = """wav2vec2"""
UpperCamelCase : str = SpeechEncoderDecoderConfig.from_dict(snake_case_ )
hf_wavavec.save_pretrained(snake_case_ )
feature_extractor.save_pretrained(snake_case_ )
if __name__ == "__main__":
__A : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-large-lv60''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/s2t-small-mustc-en-fr-st''',
type=str,
help='''Path to hf decoder s2t checkpoint config''',
)
parser.add_argument('''--vocab_size''', default=10224, type=int, help='''Vocab size of decoder''')
parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')
__A : List[str] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 27
|
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27
| 1
|
"""simple docstring"""
import math
import os
import sys
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """"""
try:
with open(snake_case_ ,"""rb""" ) as binary_file:
UpperCamelCase : Any = binary_file.read()
for dat in data:
UpperCamelCase : int = f'{dat:08b}'
result += curr_byte
return result
except OSError:
print("""File not accessible""" )
sys.exit()
def A_ ( snake_case_ : dict[str, str] ,snake_case_ : str ,snake_case_ : int ,snake_case_ : str ):
'''simple docstring'''
lexicon.pop(snake_case_ )
UpperCamelCase : Union[str, Any] = last_match_id
if math.loga(snake_case_ ).is_integer():
for curr_key in lexicon:
UpperCamelCase : Any = """0""" + lexicon[curr_key]
UpperCamelCase : int = bin(snake_case_ )[2:]
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = {"""0""": """0""", """1""": """1"""}
UpperCamelCase , UpperCamelCase : Dict = """""", """"""
UpperCamelCase : Optional[int] = len(snake_case_ )
for i in range(len(snake_case_ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCamelCase : Tuple = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
index += 1
UpperCamelCase : Any = """"""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
UpperCamelCase : Dict = lexicon[curr_string]
result += last_match_id
return result
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = os.path.getsize(snake_case_ )
UpperCamelCase : Union[str, Any] = bin(snake_case_ )[2:]
UpperCamelCase : List[str] = len(snake_case_ )
return "0" * (length_length - 1) + file_length_binary + compressed
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : int = 8
try:
with open(snake_case_ ,"""wb""" ) as opened_file:
UpperCamelCase : Dict = [
to_write[i : i + byte_length]
for i in range(0 ,len(snake_case_ ) ,snake_case_ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("""10000000""" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(snake_case_ ,2 ).to_bytes(1 ,byteorder="""big""" ) )
except OSError:
print("""File not accessible""" )
sys.exit()
def A_ ( snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = read_file_binary(snake_case_ )
UpperCamelCase : Optional[int] = compress_data(snake_case_ )
UpperCamelCase : Any = add_file_length(snake_case_ ,snake_case_ )
write_file_binary(snake_case_ ,snake_case_ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 27
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27
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|
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if n == 1 or not isinstance(snake_case_ ,snake_case_ ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase : Optional[Any] = [0, 1]
for i in range(2 ,n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : str = 2
while digits < n:
index += 1
UpperCamelCase : Dict = len(str(fibonacci(snake_case_ ) ) )
return index
def A_ ( snake_case_ : int = 1_0_0_0 ):
'''simple docstring'''
return fibonacci_digits_index(snake_case_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 27
|
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
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| 1
|
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
__A : int = ['''gpt2''']
__A : List[str] = '''gpt2'''
if is_tf_available():
class lowerCamelCase ( tf.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ ):
super().__init__()
UpperCamelCase : List[str] = tokenizer
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = TFGPTaLMHeadModel.from_config(SCREAMING_SNAKE_CASE_ )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text""" ),) )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = self.tokenizer(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = tokenized["""input_ids"""].to_tensor()
UpperCamelCase : List[str] = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
UpperCamelCase : int = self.model(input_ids=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().setUp()
UpperCamelCase : Union[str, Any] = [GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
UpperCamelCase : Optional[Any] = [TFGPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
UpperCamelCase : Dict = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
UpperCamelCase : Dict = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def a_ ( self ):
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
UpperCamelCase : Dict = tokenizer([test_inputs] , return_tensors="""tf""" )
UpperCamelCase : Any = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
UpperCamelCase : List[Any] = python_outputs[key].numpy()
UpperCamelCase : Dict = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(SCREAMING_SNAKE_CASE_ , tf.intaa ) == tf_outputs_values ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
UpperCamelCase : str = tf.function(SCREAMING_SNAKE_CASE_ )
for test_inputs in self.test_sentences:
UpperCamelCase : int = tf.constant(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = compiled_tokenizer(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = tf_tokenizer(SCREAMING_SNAKE_CASE_ )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
UpperCamelCase : Optional[Any] = ModelToSave(tokenizer=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCamelCase : Union[str, Any] = model.serving(SCREAMING_SNAKE_CASE_ ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
UpperCamelCase : Any = Path(SCREAMING_SNAKE_CASE_ ) / """saved.model"""
tf.saved_model.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , signatures={"""serving_default""": model.serving} )
UpperCamelCase : Optional[Any] = tf.saved_model.load(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = loaded_model.signatures["""serving_default"""](SCREAMING_SNAKE_CASE_ )["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
UpperCamelCase : int = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCamelCase : int = tf_tokenizer(SCREAMING_SNAKE_CASE_ ) # Build model with some sample inputs
UpperCamelCase : Tuple = tf_tokenizer.get_config()
UpperCamelCase : Tuple = TFGPTaTokenizer.from_config(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model_from_config(SCREAMING_SNAKE_CASE_ )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def a_ ( self ):
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
UpperCamelCase : int = 12_3123
for max_length in [3, 5, 1024]:
UpperCamelCase : Optional[int] = tf.convert_to_tensor([self.test_sentences[0]] )
UpperCamelCase : str = tf_tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 27
|
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27
| 1
|
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
__A : Optional[Any] = random.Random()
def A_ ( snake_case_ : str ,snake_case_ : List[str]=1.0 ,snake_case_ : Any=None ,snake_case_ : Optional[Any]=None ):
'''simple docstring'''
if rng is None:
UpperCamelCase : Any = global_rng
UpperCamelCase : int = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class lowerCamelCase ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=400 , SCREAMING_SNAKE_CASE_=2000 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=1_6000 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="hann_window" , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=7600 , SCREAMING_SNAKE_CASE_=1e-10 , SCREAMING_SNAKE_CASE_=True , ):
UpperCamelCase : List[Any] = parent
UpperCamelCase : Any = batch_size
UpperCamelCase : List[Any] = min_seq_length
UpperCamelCase : List[str] = max_seq_length
UpperCamelCase : Union[str, Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase : Tuple = feature_size
UpperCamelCase : int = padding_value
UpperCamelCase : Dict = sampling_rate
UpperCamelCase : Tuple = do_normalize
UpperCamelCase : List[Any] = num_mel_bins
UpperCamelCase : Dict = hop_length
UpperCamelCase : Optional[Any] = win_length
UpperCamelCase : Union[str, Any] = win_function
UpperCamelCase : Optional[int] = fmin
UpperCamelCase : str = fmax
UpperCamelCase : Optional[int] = mel_floor
UpperCamelCase : Any = return_attention_mask
def a_ ( self ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def a_ ( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ):
def _flatten(SCREAMING_SNAKE_CASE_ ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE_ ) )
if equal_length:
UpperCamelCase : Tuple = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
UpperCamelCase : Any = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase : Tuple = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
def a_ ( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ):
if equal_length:
UpperCamelCase : List[str] = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase : List[Any] = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
UpperCamelCase : Tuple = [np.asarray(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : List[Any] = SpeechTaFeatureExtractor
def a_ ( self ):
UpperCamelCase : Optional[Any] = SpeechTaFeatureExtractionTester(self )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE_ , axis=0 ) - 1 ) < 1e-3 ) )
def a_ ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCamelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : Optional[int] = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test not batched input
UpperCamelCase : Optional[Any] = feat_extract(speech_inputs[0] , return_tensors="""np""" ).input_values
UpperCamelCase : Dict = feat_extract(np_speech_inputs[0] , return_tensors="""np""" ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test batched
UpperCamelCase : Optional[Any] = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
UpperCamelCase : Dict = feat_extract(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : List[Any] = ["""longest""", """max_length""", """do_not_pad"""]
UpperCamelCase : Union[str, Any] = [None, 1600, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = feat_extract(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
UpperCamelCase : Dict = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def a_ ( self ):
UpperCamelCase : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Optional[Any] = range(800 , 1400 , 200 )
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in lengths]
UpperCamelCase : int = ["""longest""", """max_length""", """do_not_pad"""]
UpperCamelCase : str = [None, 1600, None]
for max_length, padding in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = feat_extract(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def a_ ( self ):
UpperCamelCase : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : str = feat_extract(
SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=1000 , padding="""max_length""" , return_tensors="""np""" )
UpperCamelCase : int = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def a_ ( self ):
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : int = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : str = feat_extract(
SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=1000 , padding="""longest""" , return_tensors="""np""" )
UpperCamelCase : Optional[int] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : Dict = feat_extract(
SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=2000 , padding="""longest""" , return_tensors="""np""" )
UpperCamelCase : Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase : Dict = np.random.rand(100 ).astype(np.floataa )
UpperCamelCase : Any = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase : Any = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
UpperCamelCase : int = feature_extractor.pad([{"""input_values""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def a_ ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCamelCase : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
UpperCamelCase : Tuple = [np.asarray(SCREAMING_SNAKE_CASE_ ) for speech_input in speech_inputs]
# Test feature size
UpperCamelCase : Optional[Any] = feature_extractor(audio_target=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
UpperCamelCase : List[str] = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_values
UpperCamelCase : List[str] = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_values
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test batched
UpperCamelCase : int = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
UpperCamelCase : Tuple = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase : Optional[Any] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase : int = np.asarray(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
UpperCamelCase : List[str] = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" ).input_values
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 ) )
def a_ ( self ):
UpperCamelCase : Dict = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_dict )
UpperCamelCase : List[str] = feat_extract.model_input_names[0]
UpperCamelCase : Optional[int] = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) for x, y in zip(SCREAMING_SNAKE_CASE_ , processed_features[input_name] ) ) )
UpperCamelCase : str = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
UpperCamelCase : str = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCamelCase : Union[str, Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def a_ ( self ):
UpperCamelCase : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
UpperCamelCase : List[Any] = feat_extract.model_input_names[0]
UpperCamelCase : Any = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
UpperCamelCase : Dict = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
UpperCamelCase : str = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def a_ ( self ):
UpperCamelCase : Any = self.feature_extraction_class(**self.feat_extract_dict )
UpperCamelCase : int = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : str = feat_extract.model_input_names[0]
UpperCamelCase : str = BatchFeature({input_name: speech_inputs} )
UpperCamelCase : Optional[int] = feat_extract.num_mel_bins # hack!
UpperCamelCase : int = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding="""longest""" , return_tensors="""np""" )[input_name]
UpperCamelCase : Dict = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def a_ ( self ):
UpperCamelCase : List[Any] = self.feat_extract_dict
UpperCamelCase : List[Any] = True
UpperCamelCase : Optional[Any] = self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : str = [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
UpperCamelCase : str = feat_extract.model_input_names[0]
UpperCamelCase : List[Any] = BatchFeature({input_name: speech_inputs} )
UpperCamelCase : Tuple = feat_extract.num_mel_bins # hack!
UpperCamelCase : Optional[int] = feat_extract.pad(SCREAMING_SNAKE_CASE_ , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.feat_extract_dict
UpperCamelCase : Union[str, Any] = True
UpperCamelCase : List[str] = self.feature_extraction_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = self.feat_extract_tester.prepare_inputs_for_target()
UpperCamelCase : Any = [len(SCREAMING_SNAKE_CASE_ ) for x in speech_inputs]
UpperCamelCase : int = feat_extract.model_input_names[0]
UpperCamelCase : Any = BatchFeature({input_name: speech_inputs} )
UpperCamelCase : str = min(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = feat_extract.num_mel_bins # hack!
UpperCamelCase : Optional[Any] = feat_extract.pad(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
self.assertIn("""attention_mask""" , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
from datasets import load_dataset
UpperCamelCase : List[str] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
UpperCamelCase : Union[str, Any] = ds.sort("""id""" ).select(range(SCREAMING_SNAKE_CASE_ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def a_ ( self ):
# fmt: off
UpperCamelCase : int = torch.tensor(
[2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03] )
# fmt: on
UpperCamelCase : List[Any] = self._load_datasamples(1 )
UpperCamelCase : int = SpeechTaFeatureExtractor()
UpperCamelCase : str = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).input_values
self.assertEquals(input_values.shape , (1, 9_3680) )
self.assertTrue(torch.allclose(input_values[0, :30] , SCREAMING_SNAKE_CASE_ , atol=1e-6 ) )
def a_ ( self ):
# fmt: off
UpperCamelCase : Dict = torch.tensor(
[-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
-3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
-3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
-3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998] )
# fmt: on
UpperCamelCase : Tuple = self._load_datasamples(1 )
UpperCamelCase : Optional[Any] = SpeechTaFeatureExtractor()
UpperCamelCase : Optional[int] = feature_extractor(audio_target=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
__A : Dict = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
__A : List[Any] = {value: key for key, value in MORSE_CODE_DICT.items()}
def A_ ( snake_case_ : str ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def A_ ( snake_case_ : str ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : List[str] = """Morse code here!"""
print(snake_case_ )
UpperCamelCase : List[str] = encrypt(snake_case_ )
print(snake_case_ )
UpperCamelCase : Optional[Any] = decrypt(snake_case_ )
print(snake_case_ )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__A : Union[str, Any] = 16
__A : List[Any] = 32
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
return int(x / 2**2_0 )
class lowerCamelCase :
def __enter__( self ):
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
UpperCamelCase : Tuple = torch.cuda.memory_allocated()
return self
def __exit__( self , *SCREAMING_SNAKE_CASE_ ):
gc.collect()
torch.cuda.empty_cache()
UpperCamelCase : Optional[int] = torch.cuda.memory_allocated()
UpperCamelCase : List[Any] = torch.cuda.max_memory_allocated()
UpperCamelCase : Any = bamb(self.end - self.begin )
UpperCamelCase : Optional[int] = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ,snake_case_ : str = "bert-base-cased" ,snake_case_ : int = 3_2_0 ,snake_case_ : int = 1_6_0 ,):
'''simple docstring'''
UpperCamelCase : Optional[int] = AutoTokenizer.from_pretrained(snake_case_ )
UpperCamelCase : Tuple = load_dataset(
"""glue""" ,"""mrpc""" ,split={"""train""": f'train[:{n_train}]', """validation""": f'validation[:{n_val}]'} )
def tokenize_function(snake_case_ : Dict ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Any = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCamelCase : int = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,load_from_cache_file=snake_case_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : int ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(snake_case_ ,padding="""max_length""" ,max_length=1_2_8 ,return_tensors="""pt""" )
return tokenizer.pad(snake_case_ ,padding="""longest""" ,return_tensors="""pt""" )
# Instantiate dataloaders.
UpperCamelCase : Any = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : int = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
def A_ ( snake_case_ : int ,snake_case_ : Any ):
'''simple docstring'''
# Initialize accelerator
UpperCamelCase : Tuple = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Optional[int] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : Optional[Any] = int(config["""seed"""] )
UpperCamelCase : str = int(config["""batch_size"""] )
UpperCamelCase : str = args.model_name_or_path
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : str = get_dataloaders(snake_case_ ,snake_case_ ,snake_case_ ,args.n_train ,args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(snake_case_ ,return_dict=snake_case_ )
# Instantiate optimizer
UpperCamelCase : List[Any] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCamelCase : List[Any] = optimizer_cls(params=model.parameters() ,lr=snake_case_ )
if accelerator.state.deepspeed_plugin is not None:
UpperCamelCase : Tuple = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
UpperCamelCase : Any = 1
UpperCamelCase : Union[str, Any] = (len(snake_case_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
UpperCamelCase : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=0 ,num_training_steps=snake_case_ ,)
else:
UpperCamelCase : Tuple = DummyScheduler(snake_case_ ,total_num_steps=snake_case_ ,warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : List[Any] = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# We need to keep track of how many total steps we have iterated over
UpperCamelCase : Optional[Any] = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCamelCase : Dict = 0
# Now we train the model
UpperCamelCase : List[str] = {}
for epoch in range(snake_case_ ,snake_case_ ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Dict = outputs.loss
UpperCamelCase : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(snake_case_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin ) ) )
accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used ) )
accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked ) )
accelerator.print(
"""Total Peak Memory consumed during the train (max): {}""".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
UpperCamelCase : int = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f'epoch-{epoch}'] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir ,"""peak_memory_utilization.json""" ) ,"""w""" ) as f:
json.dump(snake_case_ ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" ,type=snake_case_ ,default="""bert-base-cased""" ,help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,required=snake_case_ ,)
parser.add_argument(
"""--output_dir""" ,type=snake_case_ ,default=""".""" ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,)
parser.add_argument(
"""--peak_memory_upper_bound""" ,type=snake_case_ ,default=snake_case_ ,help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.""" ,)
parser.add_argument(
"""--n_train""" ,type=snake_case_ ,default=3_2_0 ,help="""Number of training examples to use.""" ,)
parser.add_argument(
"""--n_val""" ,type=snake_case_ ,default=1_6_0 ,help="""Number of validation examples to use.""" ,)
parser.add_argument(
"""--num_epochs""" ,type=snake_case_ ,default=1 ,help="""Number of train epochs.""" ,)
UpperCamelCase : List[str] = parser.parse_args()
UpperCamelCase : str = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27
| 1
|
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
__A : List[str] = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def A_ ( snake_case_ : str ):
'''simple docstring'''
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Any ,snake_case_ : List[str] ):
'''simple docstring'''
return max(metric_fn(snake_case_ ,snake_case_ ) for gt in ground_truths )
def A_ ( snake_case_ : int ,snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : List[Any] = [line.strip() for line in open(snake_case_ ,"""r""" ).readlines()]
UpperCamelCase : Dict = []
if args.gold_data_mode == "qa":
UpperCamelCase : Dict = pd.read_csv(snake_case_ ,sep="""\t""" ,header=snake_case_ )
for answer_list in data[1]:
UpperCamelCase : Dict = ast.literal_eval(snake_case_ )
answers.append(snake_case_ )
else:
UpperCamelCase : Optional[int] = [line.strip() for line in open(snake_case_ ,"""r""" ).readlines()]
UpperCamelCase : Dict = [[reference] for reference in references]
UpperCamelCase : Dict = 0
for prediction, ground_truths in zip(snake_case_ ,snake_case_ ):
total += 1
em += metric_max_over_ground_truths(snake_case_ ,snake_case_ ,snake_case_ )
fa += metric_max_over_ground_truths(snake_case_ ,snake_case_ ,snake_case_ )
UpperCamelCase : Optional[Any] = 100.0 * em / total
UpperCamelCase : str = 100.0 * fa / total
logger.info(f'F1: {fa:.2f}' )
logger.info(f'EM: {em:.2f}' )
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : str ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Any = args.k
UpperCamelCase : int = [line.strip() for line in open(snake_case_ ,"""r""" ).readlines()]
UpperCamelCase : Dict = [line.strip() for line in open(snake_case_ ,"""r""" ).readlines()]
UpperCamelCase : int = 0
for hypo, reference in zip(snake_case_ ,snake_case_ ):
UpperCamelCase : int = set(hypo.split("""\t""" )[:k] )
UpperCamelCase : Optional[Any] = set(reference.split("""\t""" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
UpperCamelCase : Any = 100.0 * em / total
logger.info(f'Precision@{k}: {em: .2f}' )
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Optional[Any] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
def strip_title(snake_case_ : Optional[Any] ):
if title.startswith("""\"""" ):
UpperCamelCase : Optional[int] = title[1:]
if title.endswith("""\"""" ):
UpperCamelCase : List[str] = title[:-1]
return title
UpperCamelCase : str = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
snake_case_ ,return_tensors="""pt""" ,padding=snake_case_ ,truncation=snake_case_ ,)["""input_ids"""].to(args.device )
UpperCamelCase : int = rag_model.rag.question_encoder(snake_case_ )
UpperCamelCase : Optional[Any] = question_enc_outputs[0]
UpperCamelCase : Union[str, Any] = rag_model.retriever(
snake_case_ ,question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy() ,prefix=rag_model.rag.generator.config.prefix ,n_docs=rag_model.config.n_docs ,return_tensors="""pt""" ,)
UpperCamelCase : Optional[Any] = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
UpperCamelCase : Dict = []
for docs in all_docs:
UpperCamelCase : Tuple = [strip_title(snake_case_ ) for title in docs["""title"""]]
provenance_strings.append("""\t""".join(snake_case_ ) )
return provenance_strings
def A_ ( snake_case_ : Dict ,snake_case_ : Union[str, Any] ,snake_case_ : Optional[int] ):
'''simple docstring'''
with torch.no_grad():
UpperCamelCase : str = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
snake_case_ ,return_tensors="""pt""" ,padding=snake_case_ ,truncation=snake_case_ )
UpperCamelCase : Union[str, Any] = inputs_dict.input_ids.to(args.device )
UpperCamelCase : int = inputs_dict.attention_mask.to(args.device )
UpperCamelCase : Dict = rag_model.generate( # rag_model overwrites generate
snake_case_ ,attention_mask=snake_case_ ,num_beams=args.num_beams ,min_length=args.min_length ,max_length=args.max_length ,early_stopping=snake_case_ ,num_return_sequences=1 ,bad_words_ids=[[0, 0]] ,)
UpperCamelCase : List[Any] = rag_model.retriever.generator_tokenizer.batch_decode(snake_case_ ,skip_special_tokens=snake_case_ )
if args.print_predictions:
for q, a in zip(snake_case_ ,snake_case_ ):
logger.info("""Q: {} - A: {}""".format(snake_case_ ,snake_case_ ) )
return answers
def A_ ( ):
'''simple docstring'''
UpperCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""" ,choices=["""rag_sequence""", """rag_token""", """bart"""] ,type=snake_case_ ,help=(
"""RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"""
""" model_name_or_path"""
) ,)
parser.add_argument(
"""--index_name""" ,default=snake_case_ ,choices=["""exact""", """compressed""", """legacy"""] ,type=snake_case_ ,help="""RAG model retriever type""" ,)
parser.add_argument(
"""--index_path""" ,default=snake_case_ ,type=snake_case_ ,help="""Path to the retrieval index""" ,)
parser.add_argument("""--n_docs""" ,default=5 ,type=snake_case_ ,help="""Number of retrieved docs""" )
parser.add_argument(
"""--model_name_or_path""" ,default=snake_case_ ,type=snake_case_ ,required=snake_case_ ,help="""Path to pretrained checkpoints or model identifier from huggingface.co/models""" ,)
parser.add_argument(
"""--eval_mode""" ,choices=["""e2e""", """retrieval"""] ,default="""e2e""" ,type=snake_case_ ,help=(
"""Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"""
""" precision@k."""
) ,)
parser.add_argument("""--k""" ,default=1 ,type=snake_case_ ,help="""k for the precision@k calculation""" )
parser.add_argument(
"""--evaluation_set""" ,default=snake_case_ ,type=snake_case_ ,required=snake_case_ ,help="""Path to a file containing evaluation samples""" ,)
parser.add_argument(
"""--gold_data_path""" ,default=snake_case_ ,type=snake_case_ ,required=snake_case_ ,help="""Path to a tab-separated file with gold samples""" ,)
parser.add_argument(
"""--gold_data_mode""" ,default="""qa""" ,type=snake_case_ ,choices=["""qa""", """ans"""] ,help=(
"""Format of the gold data file"""
"""qa - a single line in the following format: question [tab] answer_list"""
"""ans - a single line of the gold file contains the expected answer string"""
) ,)
parser.add_argument(
"""--predictions_path""" ,type=snake_case_ ,default="""predictions.txt""" ,help="""Name of the predictions file, to be stored in the checkpoints directory""" ,)
parser.add_argument(
"""--eval_all_checkpoints""" ,action="""store_true""" ,help="""Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number""" ,)
parser.add_argument(
"""--eval_batch_size""" ,default=8 ,type=snake_case_ ,help="""Batch size per GPU/CPU for evaluation.""" ,)
parser.add_argument(
"""--recalculate""" ,help="""Recalculate predictions even if the prediction file exists""" ,action="""store_true""" ,)
parser.add_argument(
"""--num_beams""" ,default=4 ,type=snake_case_ ,help="""Number of beams to be used when generating answers""" ,)
parser.add_argument("""--min_length""" ,default=1 ,type=snake_case_ ,help="""Min length of the generated answers""" )
parser.add_argument("""--max_length""" ,default=5_0 ,type=snake_case_ ,help="""Max length of the generated answers""" )
parser.add_argument(
"""--print_predictions""" ,action="""store_true""" ,help="""If True, prints predictions while evaluating.""" ,)
parser.add_argument(
"""--print_docs""" ,action="""store_true""" ,help="""If True, prints docs retried while generating.""" ,)
UpperCamelCase : str = parser.parse_args()
UpperCamelCase : Any = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
return args
def A_ ( snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : int = {}
if args.model_type is None:
UpperCamelCase : int = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("""rag""" ):
UpperCamelCase : Any = RagTokenForGeneration if args.model_type == """rag_token""" else RagSequenceForGeneration
UpperCamelCase : Tuple = args.n_docs
if args.index_name is not None:
UpperCamelCase : Optional[int] = args.index_name
if args.index_path is not None:
UpperCamelCase : List[Any] = args.index_path
else:
UpperCamelCase : List[Any] = BartForConditionalGeneration
UpperCamelCase : Any = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("""Evaluate the following checkpoints: %s""" ,snake_case_ )
UpperCamelCase : str = get_scores if args.eval_mode == """e2e""" else get_precision_at_k
UpperCamelCase : Optional[int] = evaluate_batch_eae if args.eval_mode == """e2e""" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("""Calculating metrics based on an existing predictions file: {}""".format(args.predictions_path ) )
score_fn(snake_case_ ,args.predictions_path ,args.gold_data_path )
continue
logger.info("""***** Running evaluation for {} *****""".format(snake_case_ ) )
logger.info(""" Batch size = %d""" ,args.eval_batch_size )
logger.info(""" Predictions will be stored under {}""".format(args.predictions_path ) )
if args.model_type.startswith("""rag""" ):
UpperCamelCase : List[Any] = RagRetriever.from_pretrained(snake_case_ ,**snake_case_ )
UpperCamelCase : str = model_class.from_pretrained(snake_case_ ,retriever=snake_case_ ,**snake_case_ )
model.retriever.init_retrieval()
else:
UpperCamelCase : List[Any] = model_class.from_pretrained(snake_case_ ,**snake_case_ )
model.to(args.device )
with open(args.evaluation_set ,"""r""" ) as eval_file, open(args.predictions_path ,"""w""" ) as preds_file:
UpperCamelCase : Union[str, Any] = []
for line in tqdm(snake_case_ ):
questions.append(line.strip() )
if len(snake_case_ ) == args.eval_batch_size:
UpperCamelCase : Optional[int] = evaluate_batch_fn(snake_case_ ,snake_case_ ,snake_case_ )
preds_file.write("""\n""".join(snake_case_ ) + """\n""" )
preds_file.flush()
UpperCamelCase : Any = []
if len(snake_case_ ) > 0:
UpperCamelCase : List[Any] = evaluate_batch_fn(snake_case_ ,snake_case_ ,snake_case_ )
preds_file.write("""\n""".join(snake_case_ ) )
preds_file.flush()
score_fn(snake_case_ ,args.predictions_path ,args.gold_data_path )
if __name__ == "__main__":
__A : Tuple = get_args()
main(args)
| 27
|
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : str = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AutoformerForPrediction''',
'''AutoformerModel''',
'''AutoformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
__A : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27
| 1
|
"""simple docstring"""
__A : dict[tuple[int, int, int], int] = {}
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
UpperCamelCase : str = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
UpperCamelCase : Dict = _calculate(days - 1 ,snake_case_ ,late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
UpperCamelCase : Dict = _calculate(days - 1 ,absent + 1 ,0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
UpperCamelCase : Any = _calculate(days - 1 ,snake_case_ ,0 )
UpperCamelCase : str = state_late + state_absent + state_ontime
UpperCamelCase : int = prizestrings
return prizestrings
def A_ ( snake_case_ : int = 3_0 ):
'''simple docstring'''
return _calculate(snake_case_ ,absent=0 ,late=0 )
if __name__ == "__main__":
print(solution())
| 27
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27
| 1
|
"""simple docstring"""
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[Any] ,snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCamelCase : str = mf_knapsack(i - 1 ,snake_case_ ,snake_case_ ,snake_case_ )
else:
UpperCamelCase : Union[str, Any] = max(
mf_knapsack(i - 1 ,snake_case_ ,snake_case_ ,snake_case_ ) ,mf_knapsack(i - 1 ,snake_case_ ,snake_case_ ,j - wt[i - 1] ) + val[i - 1] ,)
UpperCamelCase : List[str] = val
return f[i][j]
def A_ ( snake_case_ : Any ,snake_case_ : List[Any] ,snake_case_ : Dict ,snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 ,n + 1 ):
for w_ in range(1 ,w + 1 ):
if wt[i - 1] <= w_:
UpperCamelCase : List[Any] = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,dp[i - 1][w_] )
else:
UpperCamelCase : Dict = dp[i - 1][w_]
return dp[n][w_], dp
def A_ ( snake_case_ : int ,snake_case_ : list ,snake_case_ : list ):
'''simple docstring'''
if not (isinstance(snake_case_ ,(list, tuple) ) and isinstance(snake_case_ ,(list, tuple) )):
raise ValueError(
"""Both the weights and values vectors must be either lists or tuples""" )
UpperCamelCase : int = len(snake_case_ )
if num_items != len(snake_case_ ):
UpperCamelCase : Any = (
"""The number of weights must be the same as the number of values.\n"""
f'But got {num_items} weights and {len(snake_case_ )} values'
)
raise ValueError(snake_case_ )
for i in range(snake_case_ ):
if not isinstance(wt[i] ,snake_case_ ):
UpperCamelCase : List[Any] = (
"""All weights must be integers but got weight of """
f'type {type(wt[i] )} at index {i}'
)
raise TypeError(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = knapsack(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
UpperCamelCase : set = set()
_construct_solution(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
return optimal_val, example_optional_set
def A_ ( snake_case_ : list ,snake_case_ : list ,snake_case_ : int ,snake_case_ : int ,snake_case_ : set ):
'''simple docstring'''
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(snake_case_ ,snake_case_ ,i - 1 ,snake_case_ ,snake_case_ )
else:
optimal_set.add(snake_case_ )
_construct_solution(snake_case_ ,snake_case_ ,i - 1 ,j - wt[i - 1] ,snake_case_ )
if __name__ == "__main__":
__A : str = [3, 2, 4, 4]
__A : Optional[int] = [4, 3, 2, 3]
__A : Any = 4
__A : List[str] = 6
__A : Dict = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__A , __A : Any = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__A , __A : List[str] = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print('''optimal_value = ''', optimal_solution)
print('''An optimal subset corresponding to the optimal value''', optimal_subset)
| 27
|
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : str = (1 + 2_4 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def A_ ( snake_case_ : int = 5_0_0_0 ):
'''simple docstring'''
UpperCamelCase : int = [(i * (3 * i - 1)) // 2 for i in range(1 ,snake_case_ )]
for i, pentagonal_i in enumerate(snake_case_ ):
for j in range(snake_case_ ,len(snake_case_ ) ):
UpperCamelCase : Union[str, Any] = pentagonal_nums[j]
UpperCamelCase : Dict = pentagonal_i + pentagonal_j
UpperCamelCase : List[Any] = pentagonal_j - pentagonal_i
if is_pentagonal(snake_case_ ) and is_pentagonal(snake_case_ ):
return b
return -1
if __name__ == "__main__":
print(F'''{solution() = }''')
| 27
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = KandinskyVaaControlnetImgaImgPipeline
lowercase : int = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowercase : Any = ['image_embeds', 'negative_image_embeds', 'image', 'hint']
lowercase : int = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
lowercase : List[Any] = False
@property
def a_ ( self ):
return 32
@property
def a_ ( self ):
return 32
@property
def a_ ( self ):
return self.time_input_dim
@property
def a_ ( self ):
return self.time_input_dim * 4
@property
def a_ ( self ):
return 100
@property
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = {
"""in_channels""": 8,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """image_hint""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
UpperCamelCase : Optional[Any] = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_ )
return model
@property
def a_ ( self ):
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : str = VQModel(**self.dummy_movq_kwargs )
return model
def a_ ( self ):
UpperCamelCase : Tuple = self.dummy_unet
UpperCamelCase : Optional[int] = self.dummy_movq
UpperCamelCase : List[str] = {
"""num_train_timesteps""": 1000,
"""beta_schedule""": """linear""",
"""beta_start""": 0.00085,
"""beta_end""": 0.012,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
UpperCamelCase : str = DDIMScheduler(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : Optional[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
SCREAMING_SNAKE_CASE_ )
# create init_image
UpperCamelCase : Tuple = floats_tensor((1, 3, 64, 64) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase : List[str] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" ).resize((256, 256) )
# create hint
UpperCamelCase : List[str] = floats_tensor((1, 3, 64, 64) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = {
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""hint""": hint,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 10,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def a_ ( self ):
UpperCamelCase : Dict = """cpu"""
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Optional[Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : int = output.images
UpperCamelCase : Dict = pipe(
**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
UpperCamelCase : Union[str, Any] = image[0, -3:, -3:, -1]
UpperCamelCase : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
UpperCamelCase : Tuple = np.array(
[0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self ):
UpperCamelCase : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )
UpperCamelCase : str = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
UpperCamelCase : List[Any] = init_image.resize((512, 512) )
UpperCamelCase : Dict = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/hint_image_cat.png""" )
UpperCamelCase : int = torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_ ) ).float() / 255.0
UpperCamelCase : List[str] = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
UpperCamelCase : Any = """A robot, 4k photo"""
UpperCamelCase : List[str] = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-controlnet-depth""" , torch_dtype=torch.floataa )
UpperCamelCase : int = pipeline.to(SCREAMING_SNAKE_CASE_ )
pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
UpperCamelCase , UpperCamelCase : str = pipe_prior(
SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , strength=0.85 , generator=SCREAMING_SNAKE_CASE_ , negative_prompt="""""" , ).to_tuple()
UpperCamelCase : Tuple = pipeline(
image=SCREAMING_SNAKE_CASE_ , image_embeds=SCREAMING_SNAKE_CASE_ , negative_image_embeds=SCREAMING_SNAKE_CASE_ , hint=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=100 , height=512 , width=512 , strength=0.5 , output_type="""np""" , )
UpperCamelCase : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
from typing import Any
def A_ ( snake_case_ : list ):
'''simple docstring'''
if not postfix_notation:
return 0
UpperCamelCase : Dict = {"""+""", """-""", """*""", """/"""}
UpperCamelCase : list[Any] = []
for token in postfix_notation:
if token in operations:
UpperCamelCase , UpperCamelCase : Optional[int] = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(snake_case_ ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
|
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27
| 1
|
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def A_ ( ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Dict = 9, 1_4 # noqa: F841
UpperCamelCase : Union[str, Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 1_4],
[3, 4, 9],
[5, 4, 1_0],
[1, 7, 1_1],
]
UpperCamelCase : List[Any] = defaultdict(snake_case_ )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
UpperCamelCase : Tuple = mst(snake_case_ )
UpperCamelCase : Tuple = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
UpperCamelCase : Any = tuple(answer[:2] )
UpperCamelCase : Optional[int] = tuple(edge[::-1] )
assert edge in result or reverse in result
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A : Optional[Any] = pd.read_csv('''sample_data.csv''', header=None)
__A : List[Any] = df.shape[:1][0]
# If you're using some other dataset input the target column
__A : Dict = df.iloc[:, 1:2]
__A : List[str] = actual_data.values.reshape(len_data, 1)
__A : List[str] = MinMaxScaler().fit_transform(actual_data)
__A : List[str] = 10
__A : Optional[int] = 5
__A : Optional[int] = 20
__A : Optional[Any] = len_data - periods * look_back
__A : int = actual_data[:division]
__A : int = actual_data[division - look_back :]
__A , __A : Dict = [], []
__A , __A : Tuple = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
__A : List[Any] = np.array(train_x)
__A : str = np.array(test_x)
__A : Dict = np.array([list(i.ravel()) for i in train_y])
__A : int = np.array([list(i.ravel()) for i in test_y])
__A : Optional[Any] = Sequential()
model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(64, input_shape=(128, 1)))
model.add(Dense(forward_days))
model.compile(loss='''mean_squared_error''', optimizer='''adam''')
__A : Union[str, Any] = model.fit(
x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
)
__A : Union[str, Any] = model.predict(x_test)
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27
| 1
|
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Union[str, Any]="shi-labs/oneformer_demo" ):
'''simple docstring'''
with open(hf_hub_download(snake_case_ ,snake_case_ ,repo_type="""dataset""" ) ,"""r""" ) as f:
UpperCamelCase : Optional[int] = json.load(snake_case_ )
UpperCamelCase : Any = {}
UpperCamelCase : Optional[int] = []
UpperCamelCase : int = []
for key, info in class_info.items():
UpperCamelCase : Optional[int] = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(snake_case_ ) )
UpperCamelCase : Any = thing_ids
UpperCamelCase : Any = class_names
return metadata
class lowerCamelCase ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=30 , SCREAMING_SNAKE_CASE_=400 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=255 , SCREAMING_SNAKE_CASE_="shi-labs/oneformer_demo" , SCREAMING_SNAKE_CASE_="ade20k_panoptic.json" , SCREAMING_SNAKE_CASE_=10 , ):
UpperCamelCase : Optional[Any] = parent
UpperCamelCase : List[str] = batch_size
UpperCamelCase : List[Any] = num_channels
UpperCamelCase : Any = min_resolution
UpperCamelCase : List[str] = max_resolution
UpperCamelCase : Tuple = do_resize
UpperCamelCase : Optional[int] = {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
UpperCamelCase : str = do_normalize
UpperCamelCase : Tuple = image_mean
UpperCamelCase : str = image_std
UpperCamelCase : Optional[int] = class_info_file
UpperCamelCase : Dict = prepare_metadata(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = num_text
UpperCamelCase : Optional[int] = repo_path
# for the post_process_functions
UpperCamelCase : str = 2
UpperCamelCase : Union[str, Any] = 10
UpperCamelCase : List[Any] = 10
UpperCamelCase : Dict = 3
UpperCamelCase : str = 4
UpperCamelCase : Any = num_labels
UpperCamelCase : Dict = do_reduce_labels
UpperCamelCase : Union[str, Any] = ignore_index
def a_ ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
if not batched:
UpperCamelCase : int = image_inputs[0]
if isinstance(SCREAMING_SNAKE_CASE_ , Image.Image ):
UpperCamelCase , UpperCamelCase : Union[str, Any] = image.size
else:
UpperCamelCase , UpperCamelCase : List[str] = image.shape[1], image.shape[2]
if w < h:
UpperCamelCase : Tuple = int(self.size["""shortest_edge"""] * h / w )
UpperCamelCase : str = self.size["""shortest_edge"""]
elif w > h:
UpperCamelCase : Tuple = self.size["""shortest_edge"""]
UpperCamelCase : Union[str, Any] = int(self.size["""shortest_edge"""] * w / h )
else:
UpperCamelCase : List[Any] = self.size["""shortest_edge"""]
UpperCamelCase : str = self.size["""shortest_edge"""]
else:
UpperCamelCase : int = []
for image in image_inputs:
UpperCamelCase , UpperCamelCase : Dict = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
UpperCamelCase : List[Any] = max(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
UpperCamelCase : int = max(SCREAMING_SNAKE_CASE_ , key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]
return expected_height, expected_width
def a_ ( self ):
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Union[str, Any] = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
lowercase : str = image_processing_class
def a_ ( self ):
UpperCamelCase : Optional[Any] = OneFormerImageProcessorTester(self )
@property
def a_ ( self ):
return self.image_processing_tester.prepare_image_processor_dict()
def a_ ( self ):
UpperCamelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_mean""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_std""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_normalize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_resize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """size""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """ignore_index""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """class_info_file""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """num_text""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """repo_path""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """metadata""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_reduce_labels""" ) )
def a_ ( self ):
pass
def a_ ( self ):
# Initialize image_processor
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : int = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input
UpperCamelCase : List[str] = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase : str = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase , UpperCamelCase : Tuple = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a_ ( self ):
# Initialize image_processor
UpperCamelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase : List[Any] = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
# Test not batched input
UpperCamelCase : Optional[Any] = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase : Any = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase , UpperCamelCase : Any = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a_ ( self ):
# Initialize image_processor
UpperCamelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase : Dict = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
# Test not batched input
UpperCamelCase : Optional[int] = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
UpperCamelCase , UpperCamelCase : Optional[Any] = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
UpperCamelCase , UpperCamelCase : Optional[Any] = self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a_ ( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_="np" ):
UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
UpperCamelCase : Union[str, Any] = self.image_processing_tester.num_labels
UpperCamelCase : str = None
UpperCamelCase : int = None
UpperCamelCase : Optional[Any] = prepare_image_inputs(self.image_processing_tester , equal_resolution=SCREAMING_SNAKE_CASE_ )
if with_segmentation_maps:
UpperCamelCase : Any = num_labels
if is_instance_map:
UpperCamelCase : Tuple = list(range(SCREAMING_SNAKE_CASE_ ) ) * 2
UpperCamelCase : List[str] = dict(enumerate(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[Any] = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
UpperCamelCase : List[Any] = [Image.fromarray(SCREAMING_SNAKE_CASE_ ) for annotation in annotations]
UpperCamelCase : List[Any] = image_processor(
SCREAMING_SNAKE_CASE_ , ["""semantic"""] * len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , instance_id_to_semantic_id=SCREAMING_SNAKE_CASE_ , pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_ , )
return inputs
def a_ ( self ):
pass
def a_ ( self ):
def common(SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=None ):
UpperCamelCase : Dict = self.comm_get_image_processor_inputs(
with_segmentation_maps=SCREAMING_SNAKE_CASE_ , is_instance_map=SCREAMING_SNAKE_CASE_ , segmentation_type=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = inputs["""mask_labels"""]
UpperCamelCase : Any = inputs["""class_labels"""]
UpperCamelCase : Any = inputs["""pixel_values"""]
UpperCamelCase : Union[str, Any] = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=SCREAMING_SNAKE_CASE_ )
common(is_instance_map=SCREAMING_SNAKE_CASE_ , segmentation_type="""pil""" )
common(is_instance_map=SCREAMING_SNAKE_CASE_ , segmentation_type="""pil""" )
def a_ ( self ):
UpperCamelCase : int = np.zeros((20, 50) )
UpperCamelCase : Dict = 1
UpperCamelCase : List[Any] = 1
UpperCamelCase : Optional[int] = 1
UpperCamelCase : Optional[int] = binary_mask_to_rle(SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def a_ ( self ):
UpperCamelCase : Dict = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
UpperCamelCase : List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCamelCase : int = fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
UpperCamelCase : Optional[Any] = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
UpperCamelCase : Union[str, Any] = fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE_ , target_sizes=SCREAMING_SNAKE_CASE_ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
UpperCamelCase : List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCamelCase : Any = image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE_ , threshold=0 )
self.assertTrue(len(SCREAMING_SNAKE_CASE_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def a_ ( self ):
UpperCamelCase : List[Any] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
UpperCamelCase : Union[str, Any] = self.image_processing_tester.get_fake_oneformer_outputs()
UpperCamelCase : Dict = image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE_ , threshold=0 )
self.assertTrue(len(SCREAMING_SNAKE_CASE_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 27
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27
| 1
|
"""simple docstring"""
from collections import defaultdict
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
UpperCamelCase : int = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(SCREAMING_SNAKE_CASE_ ) )
]
UpperCamelCase : Optional[int] = defaultdict(SCREAMING_SNAKE_CASE_ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
UpperCamelCase : Dict = (1 << len(SCREAMING_SNAKE_CASE_ )) - 1
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
UpperCamelCase : str = self.count_ways_until(SCREAMING_SNAKE_CASE_ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
UpperCamelCase : List[str] = total_ways_util
return self.dp[mask][task_no]
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
# Store the list of persons for each task
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
for j in task_performed[i]:
self.task[j].append(SCREAMING_SNAKE_CASE_ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
__A : int = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
__A : Optional[int] = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 27
|
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27
| 1
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
__A : Optional[int] = logging.get_logger(__name__)
__A : Any = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all BART models at https://huggingface.co/models?filter=bart
__A : Any = {
'''vocab_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
},
'''merges_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
'''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
'''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
'''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
'''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
'''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
},
}
__A : List[str] = {
'''facebook/bart-base''': 1024,
'''facebook/bart-large''': 1024,
'''facebook/bart-large-mnli''': 1024,
'''facebook/bart-large-cnn''': 1024,
'''facebook/bart-large-xsum''': 1024,
'''yjernite/bart_eli5''': 1024,
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = VOCAB_FILES_NAMES
lowercase : Tuple = PRETRAINED_VOCAB_FILES_MAP
lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : str = ['input_ids', 'attention_mask']
lowercase : Dict = BartTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="replace" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="</s>" , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , errors=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , add_prefix_space=SCREAMING_SNAKE_CASE_ , trim_offsets=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , SCREAMING_SNAKE_CASE_ ) != add_prefix_space:
UpperCamelCase : Optional[int] = getattr(SCREAMING_SNAKE_CASE_ , pre_tok_state.pop("""type""" ) )
UpperCamelCase : List[str] = add_prefix_space
UpperCamelCase : Optional[int] = pre_tok_class(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
UpperCamelCase : List[str] = """post_processor"""
UpperCamelCase : str = getattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if tokenizer_component_instance:
UpperCamelCase : Tuple = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
UpperCamelCase : Optional[Any] = tuple(state["""sep"""] )
if "cls" in state:
UpperCamelCase : int = tuple(state["""cls"""] )
UpperCamelCase : Dict = False
if state.get("""add_prefix_space""" , SCREAMING_SNAKE_CASE_ ) != add_prefix_space:
UpperCamelCase : Dict = add_prefix_space
UpperCamelCase : List[Any] = True
if state.get("""trim_offsets""" , SCREAMING_SNAKE_CASE_ ) != trim_offsets:
UpperCamelCase : Dict = trim_offsets
UpperCamelCase : Any = True
if changes_to_apply:
UpperCamelCase : Union[str, Any] = getattr(SCREAMING_SNAKE_CASE_ , state.pop("""type""" ) )
UpperCamelCase : Tuple = component_class(**SCREAMING_SNAKE_CASE_ )
setattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@property
def a_ ( self ):
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else value
UpperCamelCase : List[Any] = value
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = kwargs.get("""is_split_into_words""" , SCREAMING_SNAKE_CASE_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = kwargs.get("""is_split_into_words""" , SCREAMING_SNAKE_CASE_ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Any = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ):
UpperCamelCase : int = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : List[Any] = [self.sep_token_id]
UpperCamelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 27
|
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27
| 1
|
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
__A : Optional[Any] = 2048
__A : List[Any] = 4096
__A : str = 42
__A : Optional[int] = os.environ.pop('''PROCESS_TRAIN''', '''false''')
__A : List[Any] = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def choose_first(snake_case_ : Dict ,snake_case_ : str=False ):
assert isinstance(snake_case_ ,snake_case_ )
if len(snake_case_ ) == 1:
UpperCamelCase : Union[str, Any] = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
UpperCamelCase : Optional[Any] = {k: [a[k]] for k in a}
if len(a["""start_token"""] ) > 0:
break
return a
UpperCamelCase : str = {"""id""": example["""id"""]}
UpperCamelCase : List[str] = example["""annotations"""]
UpperCamelCase : Dict = annotation["""yes_no_answer"""]
if 0 in yes_no_answer or 1 in yes_no_answer:
UpperCamelCase : Optional[int] = ["""yes"""] if 1 in yes_no_answer else ["""no"""]
UpperCamelCase : Optional[int] = []
UpperCamelCase : Optional[Any] = []
UpperCamelCase : Tuple = ["""<cls>"""]
else:
UpperCamelCase : int = ["""short"""]
UpperCamelCase : Union[str, Any] = choose_first(annotation["""short_answers"""] )
if len(out["""start_token"""] ) == 0:
# answer will be long if short is not available
UpperCamelCase : List[str] = ["""long"""]
UpperCamelCase : List[str] = choose_first(annotation["""long_answer"""] ,is_long_answer=snake_case_ )
UpperCamelCase : str = []
answer.update(snake_case_ )
# disregard some samples
if len(answer["""start_token"""] ) > 1 or answer["start_token"] == answer["end_token"]:
UpperCamelCase : str = True
else:
UpperCamelCase : List[Any] = False
UpperCamelCase : Any = ["""start_token""", """end_token""", """start_byte""", """end_byte""", """text"""]
if not all(isinstance(answer[k] ,snake_case_ ) for k in cols ):
raise ValueError("""Issue in ID""" ,example["""id"""] )
return answer
def A_ ( snake_case_ : Any ,snake_case_ : Optional[Any]=False ):
'''simple docstring'''
UpperCamelCase : List[str] = _get_single_answer(snake_case_ )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
UpperCamelCase : Optional[int] = example["""document"""]["""tokens"""]
UpperCamelCase : List[Any] = []
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
return {
"context": " ".join(snake_case_ ),
"answer": {
"start_token": -1_0_0, # ignore index in cross-entropy
"end_token": -1_0_0, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
UpperCamelCase : str = ["""start_token""", """end_token"""]
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
UpperCamelCase : Dict = example["""document"""]["""tokens"""]
UpperCamelCase : List[str] = answer["""start_token"""]
UpperCamelCase : Optional[int] = answer["""end_token"""]
UpperCamelCase : Optional[Any] = []
for i in range(len(doc["""token"""] ) ):
if not doc["is_html"][i]:
context.append(doc["""token"""][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
UpperCamelCase : Optional[Any] = """ """.join(context[start_token:end_token] )
# checking above code
if assertion:
UpperCamelCase : Optional[Any] = doc["""is_html"""][answer["""start_token"""] : answer["""end_token"""]]
UpperCamelCase : Dict = doc["""token"""][answer["""start_token"""] : answer["""end_token"""]]
UpperCamelCase : Optional[Any] = """ """.join([old[i] for i in range(len(snake_case_ ) ) if not is_html[i]] )
if new != old:
print("""ID:""" ,example["""id"""] )
print("""New:""" ,snake_case_ ,end="""\n""" )
print("""Old:""" ,snake_case_ ,end="""\n\n""" )
return {
"context": " ".join(snake_case_ ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def A_ ( snake_case_ : Dict ,snake_case_ : Optional[Any] ,snake_case_ : Tuple=2_0_4_8 ,snake_case_ : str=4_0_9_6 ,snake_case_ : Optional[int]=True ):
'''simple docstring'''
# overlap will be of doc_stride - q_len
UpperCamelCase : List[Any] = get_context_and_ans(snake_case_ ,assertion=snake_case_ )
UpperCamelCase : Optional[Any] = out["""answer"""]
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
UpperCamelCase : Optional[Any] = tokenizer(example["""question"""]["""text"""] ,out["""context"""] ).input_ids
UpperCamelCase : Dict = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
UpperCamelCase : List[Any] = []
UpperCamelCase : List[str] = []
UpperCamelCase : Optional[Any] = input_ids[:q_len]
UpperCamelCase : List[str] = range(snake_case_ ,len(snake_case_ ) ,max_length - doc_stride )
for i in doc_start_indices:
UpperCamelCase : str = i + max_length - q_len
UpperCamelCase : Optional[Any] = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer["""category"""][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-1_0_0] * len(snake_case_ ),
"end_token": [-1_0_0] * len(snake_case_ ),
"category": category,
},
}
UpperCamelCase : int = out["""context"""].split()
UpperCamelCase : Tuple = splitted_context[answer["""end_token"""]]
UpperCamelCase : List[Any] = len(
tokenizer(
""" """.join(splitted_context[: answer["""start_token"""]] ) ,add_special_tokens=snake_case_ ,).input_ids )
UpperCamelCase : List[str] = len(
tokenizer(""" """.join(splitted_context[: answer["""end_token"""]] ) ,add_special_tokens=snake_case_ ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
UpperCamelCase : Any = len(tokenizer(snake_case_ ,add_special_tokens=snake_case_ ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
UpperCamelCase : Tuple = input_ids[answer["""start_token"""] : answer["""end_token"""] + 1] # right & left are inclusive
UpperCamelCase : Any = answer["""start_token"""]
UpperCamelCase : Optional[Any] = answer["""end_token"""]
if assertion:
UpperCamelCase : str = tokenizer.decode(snake_case_ )
if answer["span"] != new:
print("""ISSUE IN TOKENIZATION""" )
print("""OLD:""" ,answer["""span"""] )
print("""NEW:""" ,snake_case_ ,end="""\n\n""" )
if len(snake_case_ ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
UpperCamelCase : Tuple = input_ids[:q_len]
UpperCamelCase : Union[str, Any] = range(snake_case_ ,len(snake_case_ ) ,max_length - doc_stride )
UpperCamelCase : Any = []
UpperCamelCase : str = []
UpperCamelCase : Optional[int] = []
UpperCamelCase : Tuple = [] # null, yes, no, long, short
for i in doc_start_indices:
UpperCamelCase : Tuple = i + max_length - q_len
UpperCamelCase : Optional[Any] = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
UpperCamelCase : Any = start_token - i + q_len
UpperCamelCase : Any = end_token - i + q_len
answers_category.append(answer["""category"""][0] ) # ["short"] -> "short"
else:
UpperCamelCase : Optional[Any] = -1_0_0
UpperCamelCase : List[Any] = -1_0_0
answers_category.append("""null""" )
UpperCamelCase : Optional[int] = inputs[-1][start_token : end_token + 1]
answers_start_token.append(snake_case_ )
answers_end_token.append(snake_case_ )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print("""ISSUE in strided for ID:""" ,example["""id"""] )
print("""New:""" ,tokenizer.decode(snake_case_ ) )
print("""Old:""" ,tokenizer.decode(snake_case_ ) ,end="""\n\n""" )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[str] ,snake_case_ : str=2_0_4_8 ,snake_case_ : List[Any]=4_0_9_6 ,snake_case_ : Any=False ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = get_strided_contexts_and_ans(
snake_case_ ,snake_case_ ,doc_stride=snake_case_ ,max_length=snake_case_ ,assertion=snake_case_ ,)
return example
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Optional[int] ):
'''simple docstring'''
with jsonlines.open(snake_case_ ,"""a""" ) as writer:
for example in tqdm(snake_case_ ,total=len(snake_case_ ) ,desc="""Saving samples ... """ ):
UpperCamelCase : int = example["""labels"""]
for ids, start, end, cat in zip(
example["""input_ids"""] ,labels["""start_token"""] ,labels["""end_token"""] ,labels["""category"""] ,):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
"""input_ids""": ids,
"""start_token""": start,
"""end_token""": end,
"""category""": CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
__A : Union[str, Any] = load_dataset('''natural_questions''')
__A : List[str] = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
__A : Dict = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']
__A : Tuple = {
'''tokenizer''': tokenizer,
'''doc_stride''': DOC_STRIDE,
'''max_length''': MAX_LENGTH,
'''assertion''': False,
}
__A : List[Any] = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
__A : int = data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
print(data)
np.random.seed(SEED)
__A : Union[str, Any] = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
save_to_disk(data, file_name=cache_file_name)
| 27
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27
| 1
|
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=32 * 8 , SCREAMING_SNAKE_CASE_=32 * 8 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : str = is_training
UpperCamelCase : List[str] = use_auxiliary_loss
UpperCamelCase : List[str] = num_queries
UpperCamelCase : str = num_channels
UpperCamelCase : Optional[Any] = min_size
UpperCamelCase : Optional[int] = max_size
UpperCamelCase : Any = num_labels
UpperCamelCase : Optional[int] = hidden_dim
UpperCamelCase : str = hidden_dim
def a_ ( self ):
UpperCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.ones([self.batch_size, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=SCREAMING_SNAKE_CASE_ ) > 0.5
).float()
UpperCamelCase : Optional[Any] = (torch.rand((self.batch_size, self.num_labels) , device=SCREAMING_SNAKE_CASE_ ) > 0.5).long()
UpperCamelCase : Union[str, Any] = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def a_ ( self ):
UpperCamelCase : str = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
UpperCamelCase : int = self.num_queries
UpperCamelCase : int = self.num_labels
UpperCamelCase : Union[str, Any] = [1, 1, 1, 1]
UpperCamelCase : Any = self.num_channels
UpperCamelCase : Union[str, Any] = 64
UpperCamelCase : Optional[int] = 128
UpperCamelCase : Optional[Any] = self.hidden_dim
UpperCamelCase : List[str] = self.hidden_dim
UpperCamelCase : Any = self.hidden_dim
return config
def a_ ( self ):
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : List[Any] = self.prepare_config_and_inputs()
UpperCamelCase : Optional[Any] = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = output.encoder_hidden_states
UpperCamelCase : List[str] = output.pixel_decoder_hidden_states
UpperCamelCase : Dict = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_ ) , config.decoder_layers )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
with torch.no_grad():
UpperCamelCase : Dict = MaskaFormerModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
def comm_check_on_output(SCREAMING_SNAKE_CASE_ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
UpperCamelCase : Optional[Any] = model(pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
comm_check_on_output(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(
pixel_values=SCREAMING_SNAKE_CASE_ , pixel_mask=SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ )
comm_check_on_output(SCREAMING_SNAKE_CASE_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : int = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
lowercase : List[Any] = {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
lowercase : List[Any] = False
lowercase : Any = False
lowercase : int = False
lowercase : Any = False
def a_ ( self ):
UpperCamelCase : Dict = MaskaFormerModelTester(self )
UpperCamelCase : List[str] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , has_text_modality=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase , UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason="""Mask2Former does not use inputs_embeds""" )
def a_ ( self ):
pass
@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method""" )
def a_ ( self ):
pass
@unittest.skip(reason="""Mask2Former is not a generative model""" )
def a_ ( self ):
pass
@unittest.skip(reason="""Mask2Former does not use token embeddings""" )
def a_ ( self ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def a_ ( self ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def a_ ( self ):
pass
def a_ ( self ):
UpperCamelCase , UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase : int = model_class(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase : int = [*signature.parameters.keys()]
UpperCamelCase : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
UpperCamelCase : str = MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = (self.model_tester.min_size,) * 2
UpperCamelCase : Any = {
"""pixel_values""": torch.randn((2, 3, *size) , device=SCREAMING_SNAKE_CASE_ ),
"""mask_labels""": torch.randn((2, 10, *size) , device=SCREAMING_SNAKE_CASE_ ),
"""class_labels""": torch.zeros(2 , 10 , device=SCREAMING_SNAKE_CASE_ ).long(),
}
UpperCamelCase : Optional[Any] = self.model_tester.get_config()
UpperCamelCase : List[str] = MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(**SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.loss is not None )
def a_ ( self ):
UpperCamelCase , UpperCamelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , output_hidden_states=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase , UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase : List[str] = model_class(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = model(**SCREAMING_SNAKE_CASE_ , output_attentions=SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.attentions is not None )
def a_ ( self ):
if not self.model_tester.is_training:
return
UpperCamelCase : int = self.all_model_classes[1]
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
UpperCamelCase : Optional[int] = model_class(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.train()
UpperCamelCase : str = model(SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ ).loss
loss.backward()
def a_ ( self ):
UpperCamelCase : Tuple = self.all_model_classes[1]
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
UpperCamelCase : Optional[int] = True
UpperCamelCase : int = True
UpperCamelCase : Any = model_class(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
model.train()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , mask_labels=SCREAMING_SNAKE_CASE_ , class_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
UpperCamelCase : List[str] = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
UpperCamelCase : List[str] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
UpperCamelCase : Dict = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__A : List[str] = 1e-4
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class lowerCamelCase ( unittest.TestCase ):
@cached_property
def a_ ( self ):
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def a_ ( self ):
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def a_ ( self ):
UpperCamelCase : Optional[int] = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.default_image_processor
UpperCamelCase : str = prepare_img()
UpperCamelCase : Tuple = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(SCREAMING_SNAKE_CASE_ , (1, 3, 384, 384) )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Union[str, Any] = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : List[Any] = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ ).eval()
UpperCamelCase : List[str] = self.default_image_processor
UpperCamelCase : List[Any] = prepare_img()
UpperCamelCase : List[str] = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(SCREAMING_SNAKE_CASE_ , (1, 3, 384, 384) )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(**SCREAMING_SNAKE_CASE_ )
# masks_queries_logits
UpperCamelCase : str = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
UpperCamelCase : Dict = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
UpperCamelCase : Dict = torch.tensor(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
# class_queries_logits
UpperCamelCase : Tuple = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
UpperCamelCase : Dict = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(SCREAMING_SNAKE_CASE_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : Dict = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(SCREAMING_SNAKE_CASE_ ).eval()
UpperCamelCase : Union[str, Any] = self.default_image_processor
UpperCamelCase : Union[str, Any] = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = inputs["""pixel_values"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = [el.to(SCREAMING_SNAKE_CASE_ ) for el in inputs["""mask_labels"""]]
UpperCamelCase : Tuple = [el.to(SCREAMING_SNAKE_CASE_ ) for el in inputs["""class_labels"""]]
with torch.no_grad():
UpperCamelCase : Dict = model(**SCREAMING_SNAKE_CASE_ )
self.assertTrue(outputs.loss is not None )
| 27
|
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Tuple = ['image_processor', 'tokenizer']
lowercase : Any = 'BlipImageProcessor'
lowercase : str = 'AutoTokenizer'
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = False
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = self.image_processor
def __call__( self , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None:
UpperCamelCase : int = self.tokenizer
UpperCamelCase : List[str] = self.tokenizer(
text=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_overflowing_tokens=SCREAMING_SNAKE_CASE_ , return_special_tokens_mask=SCREAMING_SNAKE_CASE_ , return_offsets_mapping=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , return_length=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
return text_encoding
# add pixel_values
UpperCamelCase : int = self.image_processor(SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ )
if text is not None:
UpperCamelCase : List[Any] = self.tokenizer(
text=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , stride=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_overflowing_tokens=SCREAMING_SNAKE_CASE_ , return_special_tokens_mask=SCREAMING_SNAKE_CASE_ , return_offsets_mapping=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , return_length=SCREAMING_SNAKE_CASE_ , verbose=SCREAMING_SNAKE_CASE_ , return_tensors=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
else:
UpperCamelCase : int = None
if text_encoding is not None:
encoding_image_processor.update(SCREAMING_SNAKE_CASE_ )
return encoding_image_processor
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.tokenizer.model_input_names
UpperCamelCase : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 27
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
while b:
UpperCamelCase , UpperCamelCase : Optional[int] = b, a % b
return a
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
return a if b == 0 else euclidean_gcd_recursive(snake_case_ ,a % b )
def A_ ( ):
'''simple docstring'''
print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3 ,5 )}' )
print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5 ,3 )}' )
print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1 ,3 )}' )
print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3 ,6 )}' )
print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6 ,3 )}' )
print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 ,5 )}' )
print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 ,3 )}' )
print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 ,3 )}' )
print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 ,6 )}' )
print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 ,3 )}' )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27
| 1
|
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Tuple = ['image_processor', 'tokenizer']
lowercase : Union[str, Any] = 'AutoImageProcessor'
lowercase : Dict = 'AutoTokenizer'
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = kwargs.pop("""feature_extractor""" )
UpperCamelCase : Optional[Any] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.image_processor
UpperCamelCase : int = False
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""images""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : List[Any] = args[0]
UpperCamelCase : Optional[int] = args[1:]
if images is None and text is None:
raise ValueError("""You need to specify either an `images` or `text` input to process.""" )
if images is not None:
UpperCamelCase : Optional[Any] = self.image_processor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if text is not None:
UpperCamelCase : str = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if text is None:
return inputs
elif images is None:
return encodings
else:
UpperCamelCase : str = encodings["""input_ids"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
@contextmanager
def a_ ( self ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your images inputs, or in a separate call.""" )
UpperCamelCase : Tuple = True
UpperCamelCase : Dict = self.tokenizer
yield
UpperCamelCase : Optional[Any] = self.image_processor
UpperCamelCase : Dict = False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=None ):
if added_vocab is None:
UpperCamelCase : Tuple = self.tokenizer.get_added_vocab()
UpperCamelCase : List[str] = {}
while tokens:
UpperCamelCase : Union[str, Any] = re.search(r"""<s_(.*?)>""" , SCREAMING_SNAKE_CASE_ , re.IGNORECASE )
if start_token is None:
break
UpperCamelCase : int = start_token.group(1 )
UpperCamelCase : int = re.search(rf'</s_{key}>' , SCREAMING_SNAKE_CASE_ , re.IGNORECASE )
UpperCamelCase : str = start_token.group()
if end_token is None:
UpperCamelCase : Union[str, Any] = tokens.replace(SCREAMING_SNAKE_CASE_ , """""" )
else:
UpperCamelCase : Optional[Any] = end_token.group()
UpperCamelCase : Optional[int] = re.escape(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = re.escape(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = re.search(f'{start_token_escaped}(.*?){end_token_escaped}' , SCREAMING_SNAKE_CASE_ , re.IGNORECASE )
if content is not None:
UpperCamelCase : str = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
UpperCamelCase : Any = self.tokenajson(SCREAMING_SNAKE_CASE_ , is_inner_value=SCREAMING_SNAKE_CASE_ , added_vocab=SCREAMING_SNAKE_CASE_ )
if value:
if len(SCREAMING_SNAKE_CASE_ ) == 1:
UpperCamelCase : Any = value[0]
UpperCamelCase : Union[str, Any] = value
else: # leaf nodes
UpperCamelCase : int = []
for leaf in content.split(r"""<sep/>""" ):
UpperCamelCase : List[str] = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
UpperCamelCase : str = leaf[1:-2] # for categorical special tokens
output[key].append(SCREAMING_SNAKE_CASE_ )
if len(output[key] ) == 1:
UpperCamelCase : List[Any] = output[key][0]
UpperCamelCase : Dict = tokens[tokens.find(SCREAMING_SNAKE_CASE_ ) + len(SCREAMING_SNAKE_CASE_ ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=SCREAMING_SNAKE_CASE_ , added_vocab=SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def a_ ( self ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , SCREAMING_SNAKE_CASE_ , )
return self.image_processor_class
@property
def a_ ( self ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , SCREAMING_SNAKE_CASE_ , )
return self.image_processor
| 27
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'''
),
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[str] = 'audio-spectrogram-transformer'
def __init__( self , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-12 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=128 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : List[Any] = num_attention_heads
UpperCamelCase : Any = intermediate_size
UpperCamelCase : Optional[int] = hidden_act
UpperCamelCase : List[str] = hidden_dropout_prob
UpperCamelCase : Optional[int] = attention_probs_dropout_prob
UpperCamelCase : Union[str, Any] = initializer_range
UpperCamelCase : Tuple = layer_norm_eps
UpperCamelCase : Tuple = patch_size
UpperCamelCase : Tuple = qkv_bias
UpperCamelCase : Optional[Any] = frequency_stride
UpperCamelCase : Dict = time_stride
UpperCamelCase : Any = max_length
UpperCamelCase : Any = num_mel_bins
| 27
|
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
import math
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Dict = input("""Enter message: """ )
UpperCamelCase : Optional[int] = int(input(f'Enter key [2-{len(snake_case_ ) - 1}]: ' ) )
UpperCamelCase : Union[str, Any] = input("""Encryption/Decryption [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCamelCase : int = encrypt_message(snake_case_ ,snake_case_ )
elif mode.lower().startswith("""d""" ):
UpperCamelCase : str = decrypt_message(snake_case_ ,snake_case_ )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(f'Output:\n{text + "|"}' )
def A_ ( snake_case_ : int ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = [""""""] * key
for col in range(snake_case_ ):
UpperCamelCase : Optional[Any] = col
while pointer < len(snake_case_ ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(snake_case_ )
def A_ ( snake_case_ : int ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Any = math.ceil(len(snake_case_ ) / key )
UpperCamelCase : Tuple = key
UpperCamelCase : Any = (num_cols * num_rows) - len(snake_case_ )
UpperCamelCase : List[str] = [""""""] * num_cols
UpperCamelCase : Dict = 0
UpperCamelCase : Optional[int] = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
UpperCamelCase : Dict = 0
row += 1
return "".join(snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 27
|
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
__A : str = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[str] = 'facebook/nllb-200-distilled-600M'
lowercase : Union[str, Any] = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
lowercase : List[str] = 'translator'
lowercase : Union[str, Any] = AutoTokenizer
lowercase : List[str] = AutoModelForSeqaSeqLM
lowercase : List[str] = LANGUAGE_CODES
lowercase : int = ['text', 'text', 'text']
lowercase : List[str] = ['text']
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if src_lang not in self.lang_to_code:
raise ValueError(f'{src_lang} is not a supported language.' )
if tgt_lang not in self.lang_to_code:
raise ValueError(f'{tgt_lang} is not a supported language.' )
UpperCamelCase : str = self.lang_to_code[src_lang]
UpperCamelCase : Optional[int] = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , src_lang=SCREAMING_SNAKE_CASE_ , tgt_lang=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.model.generate(**SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__A : List[Any] = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
''' Distillation'''
)
)
parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
parser.add_argument('''--vocab_transform''', action='''store_true''')
__A : int = parser.parse_args()
if args.model_type == "roberta":
__A : int = RobertaForMaskedLM.from_pretrained(args.model_name)
__A : Tuple = '''roberta'''
elif args.model_type == "gpt2":
__A : Optional[int] = GPTaLMHeadModel.from_pretrained(args.model_name)
__A : Dict = '''transformer'''
__A : Tuple = model.state_dict()
__A : Any = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__A : Tuple = state_dict[F'''{prefix}.{param_name}''']
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__A : Tuple = F'''{prefix}.embeddings.{w}.weight'''
__A : Optional[int] = state_dict[param_name]
for w in ["weight", "bias"]:
__A : Optional[Any] = F'''{prefix}.embeddings.LayerNorm.{w}'''
__A : Optional[int] = state_dict[param_name]
# Transformer Blocks #
__A : int = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
__A : int = state_dict[
F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
]
__A : Optional[Any] = state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
__A : Dict = state_dict[
F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
__A : Any = state_dict[F'''{layer}''']
if args.vocab_transform:
for w in ["weight", "bias"]:
__A : Dict = state_dict[F'''lm_head.dense.{w}''']
__A : List[Any] = state_dict[F'''lm_head.layer_norm.{w}''']
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__A : List[Any] = state_dict[F'''{prefix}.ln_f.{w}''']
__A : Tuple = state_dict['''lm_head.weight''']
print(F'''N layers selected for distillation: {std_idx}''')
print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')
print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
torch.save(compressed_sd, args.dump_checkpoint)
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__A : Tuple = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification''',
'''language-modeling''',
'''summarization''',
'''token-classification''',
'''question-answering''',
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
__A : str = logging.getLogger()
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = argparse.ArgumentParser()
parser.add_argument("""-f""" )
UpperCamelCase : Tuple = parser.parse_args()
return args.f
def A_ ( snake_case_ : str ,snake_case_ : Optional[int]="eval" ):
'''simple docstring'''
UpperCamelCase : str = os.path.join(snake_case_ ,f'{split}_results.json' )
if os.path.exists(snake_case_ ):
with open(snake_case_ ,"""r""" ) as f:
return json.load(snake_case_ )
raise ValueError(f'can\'t find {path}' )
__A : Any = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class lowerCamelCase ( _UpperCAmelCase ):
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.get_auto_remove_tmp_dir()
UpperCamelCase : str = f'\n run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --eval_steps=2\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_flax_glue.main()
UpperCamelCase : List[Any] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.75 )
@slow
def a_ ( self ):
UpperCamelCase : Optional[int] = self.get_auto_remove_tmp_dir()
UpperCamelCase : Dict = f'\n run_clm_flax.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --block_size 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_clm_flax.main()
UpperCamelCase : Tuple = get_results(SCREAMING_SNAKE_CASE_ )
self.assertLess(result["""eval_perplexity"""] , 100 )
@slow
def a_ ( self ):
UpperCamelCase : List[Any] = self.get_auto_remove_tmp_dir()
UpperCamelCase : Union[str, Any] = f'\n run_summarization.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --test_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=8\n --do_train\n --do_eval\n --do_predict\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --predict_with_generate\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_summarization_flax.main()
UpperCamelCase : Union[str, Any] = get_results(SCREAMING_SNAKE_CASE_ , split="""test""" )
self.assertGreaterEqual(result["""test_rouge1"""] , 10 )
self.assertGreaterEqual(result["""test_rouge2"""] , 2 )
self.assertGreaterEqual(result["""test_rougeL"""] , 7 )
self.assertGreaterEqual(result["""test_rougeLsum"""] , 7 )
@slow
def a_ ( self ):
UpperCamelCase : Any = self.get_auto_remove_tmp_dir()
UpperCamelCase : Optional[int] = f'\n run_mlm.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --logging_steps 2 --eval_steps 2\n --do_train\n --do_eval\n --num_train_epochs=1\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_mlm_flax.main()
UpperCamelCase : Dict = get_results(SCREAMING_SNAKE_CASE_ )
self.assertLess(result["""eval_perplexity"""] , 42 )
@slow
def a_ ( self ):
UpperCamelCase : Any = self.get_auto_remove_tmp_dir()
UpperCamelCase : Optional[Any] = f'\n run_t5_mlm_flax.py\n --model_name_or_path t5-small\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --do_train\n --do_eval\n --max_seq_length 128\n --per_device_train_batch_size 4\n --per_device_eval_batch_size 4\n --num_train_epochs 2\n --logging_steps 2 --eval_steps 2\n --output_dir {tmp_dir}\n --overwrite_output_dir\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_ta_mlm_flax.main()
UpperCamelCase : int = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.42 )
@slow
def a_ ( self ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
UpperCamelCase : List[str] = 7 if get_gpu_count() > 1 else 2
UpperCamelCase : Tuple = self.get_auto_remove_tmp_dir()
UpperCamelCase : Optional[int] = f'\n run_flax_ner.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --do_train\n --do_eval\n --warmup_steps=2\n --learning_rate=2e-4\n --logging_steps 2 --eval_steps 2\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_flax_ner.main()
UpperCamelCase : Optional[Any] = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result["""eval_accuracy"""] , 0.75 )
self.assertGreaterEqual(result["""eval_f1"""] , 0.3 )
@slow
def a_ ( self ):
UpperCamelCase : List[str] = self.get_auto_remove_tmp_dir()
UpperCamelCase : Union[str, Any] = f'\n run_qa.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --num_train_epochs=3\n --warmup_steps=2\n --do_train\n --do_eval\n --logging_steps 2 --eval_steps 2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n '.split()
with patch.object(SCREAMING_SNAKE_CASE_ , """argv""" , SCREAMING_SNAKE_CASE_ ):
run_qa.main()
UpperCamelCase : int = get_results(SCREAMING_SNAKE_CASE_ )
self.assertGreaterEqual(result["""eval_f1"""] , 30 )
self.assertGreaterEqual(result["""eval_exact"""] , 30 )
| 27
|
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__A : List[Any] = [8, 5, 9, 7]
__A : Union[str, Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__A : str = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = claim_vector
UpperCamelCase : Dict = allocated_resources_table
UpperCamelCase : Tuple = maximum_claim_table
def a_ ( self ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def a_ ( self ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def a_ ( self ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(SCREAMING_SNAKE_CASE_ ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def a_ ( self ):
return {self.__need().index(SCREAMING_SNAKE_CASE_ ): i for i in self.__need()}
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = self.__need()
UpperCamelCase : Any = self.__allocated_resources_table
UpperCamelCase : Tuple = self.__available_resources()
UpperCamelCase : Union[str, Any] = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
UpperCamelCase : Optional[Any] = False
for each_need in need_list:
UpperCamelCase : Any = True
for index, need in enumerate(SCREAMING_SNAKE_CASE_ ):
if need > available_resources[index]:
UpperCamelCase : Any = False
break
if execution:
UpperCamelCase : Dict = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
UpperCamelCase : Tuple = original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(SCREAMING_SNAKE_CASE_ )
# update available/freed resources stack
UpperCamelCase : Dict = np.array(SCREAMING_SNAKE_CASE_ ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(SCREAMING_SNAKE_CASE_ ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def a_ ( self ):
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(SCREAMING_SNAKE_CASE_ ) + 1}'
+ """ """.join(f'{it:>8}' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(SCREAMING_SNAKE_CASE_ ) + 1}'
+ """ """.join(f'{it:>8}' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(SCREAMING_SNAKE_CASE_ ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(SCREAMING_SNAKE_CASE_ ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
|
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
def A_ ( snake_case_ : str ):
'''simple docstring'''
if not all(char in """01""" for char in bin_string ):
raise ValueError("""Non-binary value was passed to the function""" )
if not bin_string:
raise ValueError("""Empty string was passed to the function""" )
UpperCamelCase : Any = """"""
while len(snake_case_ ) % 3 != 0:
UpperCamelCase : List[str] = """0""" + bin_string
UpperCamelCase : Tuple = [
bin_string[index : index + 3]
for index in range(len(snake_case_ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
UpperCamelCase : int = 0
for index, val in enumerate(snake_case_ ):
oct_val += int(2 ** (2 - index) * int(snake_case_ ) )
oct_string += str(snake_case_ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod()
| 27
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27
| 1
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27
| 1
|
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Optional[Any] = {'''vocab_file''': '''spiece.model'''}
__A : Any = {
'''vocab_file''': {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
'''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
'''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
'''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
'''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
'''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
'''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
'''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
}
}
__A : int = {
'''albert-base-v1''': 512,
'''albert-large-v1''': 512,
'''albert-xlarge-v1''': 512,
'''albert-xxlarge-v1''': 512,
'''albert-base-v2''': 512,
'''albert-large-v2''': 512,
'''albert-xlarge-v2''': 512,
'''albert-xxlarge-v2''': 512,
}
__A : List[str] = '''▁'''
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : int = VOCAB_FILES_NAMES
lowercase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
lowercase : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_="[CLS]" , SCREAMING_SNAKE_CASE_="[SEP]" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="[SEP]" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="[CLS]" , SCREAMING_SNAKE_CASE_="[MASK]" , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
UpperCamelCase : Any = (
AddedToken(SCREAMING_SNAKE_CASE_ , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ , normalized=SCREAMING_SNAKE_CASE_ )
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else mask_token
)
UpperCamelCase : str = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=SCREAMING_SNAKE_CASE_ , remove_space=SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ , bos_token=SCREAMING_SNAKE_CASE_ , eos_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = do_lower_case
UpperCamelCase : Any = remove_space
UpperCamelCase : str = keep_accents
UpperCamelCase : Union[str, Any] = vocab_file
UpperCamelCase : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(SCREAMING_SNAKE_CASE_ )
@property
def a_ ( self ):
return len(self.sp_model )
def a_ ( self ):
UpperCamelCase : Tuple = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
UpperCamelCase : List[Any] = self.__dict__.copy()
UpperCamelCase : int = None
return state
def __setstate__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
UpperCamelCase : Optional[Any] = {}
UpperCamelCase : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
if self.remove_space:
UpperCamelCase : Tuple = """ """.join(inputs.strip().split() )
else:
UpperCamelCase : List[str] = inputs
UpperCamelCase : List[Any] = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
UpperCamelCase : List[Any] = unicodedata.normalize("""NFKD""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """""".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE_ )] )
if self.do_lower_case:
UpperCamelCase : Any = outputs.lower()
return outputs
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = self.preprocess_text(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.sp_model.encode(SCREAMING_SNAKE_CASE_ , out_type=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = []
for piece in pieces:
if len(SCREAMING_SNAKE_CASE_ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
UpperCamelCase : Union[str, Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE_ , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
UpperCamelCase : List[Any] = cur_pieces[1:]
else:
UpperCamelCase : Optional[int] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(SCREAMING_SNAKE_CASE_ )
else:
new_pieces.append(SCREAMING_SNAKE_CASE_ )
return new_pieces
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = []
UpperCamelCase : Optional[int] = """"""
UpperCamelCase : Optional[Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_ ) + token
UpperCamelCase : Dict = True
UpperCamelCase : Union[str, Any] = []
else:
current_sub_tokens.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = False
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_ )
return out_string.strip()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : List[Any] = [self.sep_token_id]
UpperCamelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is not None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : List[Any] = [self.sep_token_id]
UpperCamelCase : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
UpperCamelCase : str = os.path.join(
SCREAMING_SNAKE_CASE_ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE_ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as fi:
UpperCamelCase : Tuple = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 27
|
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
import requests
__A : List[Any] = '''''' # <-- Put your OpenWeatherMap appid here!
__A : Tuple = '''https://api.openweathermap.org/data/2.5/'''
def A_ ( snake_case_ : str = "Chicago" ,snake_case_ : str = APPID ):
'''simple docstring'''
return requests.get(URL_BASE + """weather""" ,params=locals() ).json()
def A_ ( snake_case_ : str = "Kolkata, India" ,snake_case_ : str = APPID ):
'''simple docstring'''
return requests.get(URL_BASE + """forecast""" ,params=locals() ).json()
def A_ ( snake_case_ : float = 55.68 ,snake_case_ : float = 12.57 ,snake_case_ : str = APPID ):
'''simple docstring'''
return requests.get(URL_BASE + """onecall""" ,params=locals() ).json()
if __name__ == "__main__":
from pprint import pprint
while True:
__A : Optional[int] = input('''Enter a location:''').strip()
if location:
pprint(current_weather(location))
else:
break
| 27
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
| 1
|
"""simple docstring"""
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__A : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase :
lowercase : List[str] = None
@experimental
def A_ ( snake_case_ : Dict ,snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : str ,snake_case_ : Union[str, Any] ,snake_case_ : List[Any] ,snake_case_ : Optional[int] ):
'''simple docstring'''
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
return _map_with_joblib(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
def A_ ( snake_case_ : List[str] ,snake_case_ : str ,snake_case_ : str ,snake_case_ : List[str] ,snake_case_ : Optional[int] ,snake_case_ : Optional[Any] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : Optional[int] = num_proc if num_proc <= len(snake_case_ ) else len(snake_case_ )
UpperCamelCase : List[Any] = [] # We organize the splits ourselve (contiguous splits)
for index in range(snake_case_ ):
UpperCamelCase : Union[str, Any] = len(snake_case_ ) // num_proc
UpperCamelCase : Optional[Any] = len(snake_case_ ) % num_proc
UpperCamelCase : str = div * index + min(snake_case_ ,snake_case_ )
UpperCamelCase : int = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(snake_case_ ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
f'Error dividing inputs iterable among processes. '
f'Total number of objects {len(snake_case_ )}, '
f'length: {sum(len(i[1] ) for i in split_kwds )}' )
logger.info(
f'Spawning {num_proc} processes for {len(snake_case_ )} objects in slices of {[len(i[1] ) for i in split_kwds]}' )
UpperCamelCase , UpperCamelCase : Optional[int] = None, None
if not disable_tqdm:
UpperCamelCase , UpperCamelCase : List[str] = (RLock(),), tqdm.set_lock
with Pool(snake_case_ ,initargs=snake_case_ ,initializer=snake_case_ ) as pool:
UpperCamelCase : Optional[int] = pool.map(snake_case_ ,snake_case_ )
logger.info(f'Finished {num_proc} processes' )
UpperCamelCase : Optional[int] = [obj for proc_res in mapped for obj in proc_res]
logger.info(f'Unpacked {len(snake_case_ )} objects' )
return mapped
def A_ ( snake_case_ : Tuple ,snake_case_ : int ,snake_case_ : str ,snake_case_ : List[Any] ,snake_case_ : Any ,snake_case_ : Tuple ,snake_case_ : int ):
'''simple docstring'''
# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
# and it requires monkey-patching joblib internal classes which is subject to change
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name ,n_jobs=snake_case_ ):
return joblib.Parallel()(
joblib.delayed(snake_case_ )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : List[str] = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
UpperCamelCase : List[Any] = None
| 27
|
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : list[Any] = []
UpperCamelCase : int = 0
UpperCamelCase : int = 0
def a_ ( self ):
return self.head == self.tail
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.data.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.tail + 1
def a_ ( self ):
UpperCamelCase : List[str] = self.data[self.head]
UpperCamelCase : Optional[Any] = self.head + 1
return ret
def a_ ( self ):
return self.tail - self.head
def a_ ( self ):
print(self.data )
print("""**************""" )
print(self.data[self.head : self.tail] )
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = data
UpperCamelCase : MyNode | None = None
UpperCamelCase : MyNode | None = None
UpperCamelCase : int = 1
def a_ ( self ):
return self.data
def a_ ( self ):
return self.left
def a_ ( self ):
return self.right
def a_ ( self ):
return self.height
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = node
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = node
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = height
def A_ ( snake_case_ : MyNode | None ):
'''simple docstring'''
if node is None:
return 0
return node.get_height()
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
if a > b:
return a
return b
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
print("""left rotation node:""" ,node.get_data() )
UpperCamelCase : Optional[int] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(snake_case_ )
UpperCamelCase : int = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(snake_case_ )
UpperCamelCase : List[Any] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case_ )
return ret
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
print("""right rotation node:""" ,node.get_data() )
UpperCamelCase : Tuple = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(snake_case_ )
UpperCamelCase : Union[str, Any] = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(snake_case_ )
UpperCamelCase : Optional[int] = my_max(get_height(ret.get_right() ) ,get_height(ret.get_left() ) ) + 1
ret.set_height(snake_case_ )
return ret
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
UpperCamelCase : Any = node.get_left()
assert left_child is not None
node.set_left(left_rotation(snake_case_ ) )
return right_rotation(snake_case_ )
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
UpperCamelCase : List[Any] = node.get_right()
assert right_child is not None
node.set_right(right_rotation(snake_case_ ) )
return left_rotation(snake_case_ )
def A_ ( snake_case_ : MyNode | None ,snake_case_ : Any ):
'''simple docstring'''
if node is None:
return MyNode(snake_case_ )
if data < node.get_data():
node.set_left(insert_node(node.get_left() ,snake_case_ ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
UpperCamelCase : Any = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
UpperCamelCase : Optional[int] = right_rotation(snake_case_ )
else:
UpperCamelCase : str = lr_rotation(snake_case_ )
else:
node.set_right(insert_node(node.get_right() ,snake_case_ ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
UpperCamelCase : int = node.get_right()
assert right_child is not None
if data < right_child.get_data():
UpperCamelCase : str = rl_rotation(snake_case_ )
else:
UpperCamelCase : Union[str, Any] = left_rotation(snake_case_ )
UpperCamelCase : str = my_max(get_height(node.get_right() ) ,get_height(node.get_left() ) ) + 1
node.set_height(snake_case_ )
return node
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
while True:
UpperCamelCase : List[str] = root.get_right()
if right_child is None:
break
UpperCamelCase : int = right_child
return root.get_data()
def A_ ( snake_case_ : MyNode ):
'''simple docstring'''
while True:
UpperCamelCase : str = root.get_left()
if left_child is None:
break
UpperCamelCase : str = left_child
return root.get_data()
def A_ ( snake_case_ : MyNode ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Any = root.get_left()
UpperCamelCase : str = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
UpperCamelCase : Tuple = get_left_most(snake_case_ )
root.set_data(snake_case_ )
root.set_right(del_node(snake_case_ ,snake_case_ ) )
elif left_child is not None:
UpperCamelCase : List[str] = left_child
elif right_child is not None:
UpperCamelCase : Optional[Any] = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print("""No such data""" )
return root
else:
root.set_left(del_node(snake_case_ ,snake_case_ ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(snake_case_ ,snake_case_ ) )
if get_height(snake_case_ ) - get_height(snake_case_ ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
UpperCamelCase : str = left_rotation(snake_case_ )
else:
UpperCamelCase : List[Any] = rl_rotation(snake_case_ )
elif get_height(snake_case_ ) - get_height(snake_case_ ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
UpperCamelCase : Optional[Any] = right_rotation(snake_case_ )
else:
UpperCamelCase : str = lr_rotation(snake_case_ )
UpperCamelCase : Any = my_max(get_height(root.get_right() ) ,get_height(root.get_left() ) ) + 1
root.set_height(snake_case_ )
return root
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : MyNode | None = None
def a_ ( self ):
return get_height(self.root )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
print("""insert:""" + str(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Any = insert_node(self.root , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
print("""delete:""" + str(SCREAMING_SNAKE_CASE_ ) )
if self.root is None:
print("""Tree is empty!""" )
return
UpperCamelCase : Tuple = del_node(self.root , SCREAMING_SNAKE_CASE_ )
def __str__( self , ): # a level traversale, gives a more intuitive look on the tree
UpperCamelCase : Union[str, Any] = """"""
UpperCamelCase : Any = MyQueue()
q.push(self.root )
UpperCamelCase : str = self.get_height()
if layer == 0:
return output
UpperCamelCase : Any = 0
while not q.is_empty():
UpperCamelCase : Tuple = q.pop()
UpperCamelCase : List[str] = """ """ * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(SCREAMING_SNAKE_CASE_ )
q.push(SCREAMING_SNAKE_CASE_ )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
UpperCamelCase : str = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , SCREAMING_SNAKE_CASE_ ) - 1:
UpperCamelCase : Tuple = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def A_ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
__A : str = AVLtree()
__A : List[str] = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t))
| 27
|
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27
| 1
|
"""simple docstring"""
import json
import sys
def A_ ( snake_case_ : List[Any] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : Optional[int] = json.load(snake_case_ )
UpperCamelCase : Dict = ["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]
for benchmark_name in sorted(snake_case_ ):
UpperCamelCase : Tuple = results[benchmark_name]
UpperCamelCase : Optional[Any] = benchmark_name.split("""/""" )[-1]
output_md.append(f'### Benchmark: {benchmark_file_name}' )
UpperCamelCase : List[str] = """| metric |"""
UpperCamelCase : Optional[Any] = """|--------|"""
UpperCamelCase : int = """| new / old (diff) |"""
for metric_name in sorted(snake_case_ ):
UpperCamelCase : Dict = benchmark_res[metric_name]
UpperCamelCase : Any = metric_vals["""new"""]
UpperCamelCase : Tuple = metric_vals.get("""old""" ,snake_case_ )
UpperCamelCase : Optional[Any] = metric_vals.get("""diff""" ,snake_case_ )
UpperCamelCase : Tuple = f' {new_val:f}' if isinstance(snake_case_ ,(int, float) ) else """None"""
if old_val is not None:
val_str += f' / {old_val:f}' if isinstance(snake_case_ ,(int, float) ) else "None"
if dif_val is not None:
val_str += f' ({dif_val:f})' if isinstance(snake_case_ ,(int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append("""</details>""" )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.writelines("""\n""".join(snake_case_ ) )
if __name__ == "__main__":
__A : Optional[Any] = sys.argv[1]
__A : List[str] = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def A_ ( *snake_case_ : Optional[int] ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as fh:
fcntl.flock(snake_case_ ,fcntl.LOCK_EX )
try:
print(*snake_case_ )
finally:
fcntl.flock(snake_case_ ,fcntl.LOCK_UN )
__A : Optional[Any] = int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
__A : Optional[int] = torch.device('''cuda''', local_rank)
__A : List[str] = socket.gethostname()
__A : Optional[Any] = F'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group('''nccl''')
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
__A : List[Any] = dist.get_rank()
__A : List[str] = dist.get_world_size()
printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(F'''{gpu} is broken''')
raise
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def A_ ( snake_case_ : Union[str, Any] ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : int = create_tensor(snake_case_ )
UpperCamelCase : str = gather(snake_case_ )
assert gathered_tensor.tolist() == list(range(1 ,state.num_processes**2 + 1 ) )
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : int = [state.process_index]
UpperCamelCase : Optional[int] = gather_object(snake_case_ )
assert len(snake_case_ ) == state.num_processes, f'{gathered_obj}, {len(snake_case_ )} != {state.num_processes}'
assert gathered_obj == list(range(state.num_processes ) ), f'{gathered_obj} != {list(range(state.num_processes ) )}'
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : List[Any] = create_tensor(snake_case_ )
UpperCamelCase : Tuple = broadcast(snake_case_ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 ,state.num_processes + 1 ) )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
UpperCamelCase : Tuple = torch.arange(state.num_processes + 1 ).to(state.device )
else:
UpperCamelCase : Union[str, Any] = torch.arange(state.num_processes ).to(state.device )
UpperCamelCase : Dict = pad_across_processes(snake_case_ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 ,state.num_processes ) ) + [0]
def A_ ( snake_case_ : str ):
'''simple docstring'''
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCamelCase : Tuple = create_tensor(snake_case_ )
UpperCamelCase : Dict = reduce(snake_case_ ,"""sum""" )
UpperCamelCase : Dict = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(snake_case_ ,snake_case_ ), f'{reduced_tensor} != {truth_tensor}'
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCamelCase : Optional[Any] = create_tensor(snake_case_ )
UpperCamelCase : int = reduce(snake_case_ ,"""mean""" )
UpperCamelCase : List[Any] = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(snake_case_ ,snake_case_ ), f'{reduced_tensor} != {truth_tensor}'
def A_ ( snake_case_ : str ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Any = PartialState()
state.print(f'State: {state}' )
state.print("""testing gather""" )
test_gather(snake_case_ )
state.print("""testing gather_object""" )
test_gather_object(snake_case_ )
state.print("""testing broadcast""" )
test_broadcast(snake_case_ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(snake_case_ )
state.print("""testing reduce_sum""" )
test_reduce_sum(snake_case_ )
state.print("""testing reduce_mean""" )
test_reduce_mean(snake_case_ )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class lowerCamelCase ( unittest.TestCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=18 , SCREAMING_SNAKE_CASE_=30 , SCREAMING_SNAKE_CASE_=400 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=[0.48145466, 0.4578275, 0.40821073] , SCREAMING_SNAKE_CASE_=[0.26862954, 0.26130258, 0.27577711] , SCREAMING_SNAKE_CASE_=True , ):
UpperCamelCase : List[str] = size if size is not None else {"""height""": 224, """width""": 224}
UpperCamelCase : str = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
UpperCamelCase : Tuple = parent
UpperCamelCase : Any = batch_size
UpperCamelCase : Optional[int] = num_channels
UpperCamelCase : Optional[Any] = image_size
UpperCamelCase : str = min_resolution
UpperCamelCase : Any = max_resolution
UpperCamelCase : List[Any] = do_resize
UpperCamelCase : str = size
UpperCamelCase : Union[str, Any] = do_center_crop
UpperCamelCase : Optional[int] = crop_size
UpperCamelCase : Any = do_normalize
UpperCamelCase : Optional[Any] = image_mean
UpperCamelCase : str = image_std
UpperCamelCase : List[str] = do_convert_rgb
def a_ ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def a_ ( self , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ):
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
UpperCamelCase : Tuple = []
for i in range(self.batch_size ):
image_inputs.append(
np.random.randint(
255 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta ) )
else:
UpperCamelCase : str = []
for i in range(self.batch_size ):
UpperCamelCase , UpperCamelCase : Tuple = np.random.choice(np.arange(self.min_resolution , self.max_resolution ) , 2 )
image_inputs.append(np.random.randint(255 , size=(self.num_channels, width, height) , dtype=np.uinta ) )
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
UpperCamelCase : List[str] = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_ , 0 , -1 ) ) for x in image_inputs]
if torchify:
UpperCamelCase : Optional[int] = [torch.from_numpy(SCREAMING_SNAKE_CASE_ ) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : str = ChineseCLIPImageProcessor if is_vision_available() else None
def a_ ( self ):
UpperCamelCase : Any = ChineseCLIPImageProcessingTester(self , do_center_crop=SCREAMING_SNAKE_CASE_ )
@property
def a_ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def a_ ( self ):
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_resize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """size""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_center_crop""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """center_crop""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_normalize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_mean""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_std""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_convert_rgb""" ) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 224, """width""": 224} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
UpperCamelCase : Any = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def a_ ( self ):
pass
def a_ ( self ):
# Initialize image_processing
UpperCamelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input
UpperCamelCase : List[Any] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
UpperCamelCase : List[Any] = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def a_ ( self ):
# Initialize image_processing
UpperCamelCase : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase : Dict = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
# Test not batched input
UpperCamelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
UpperCamelCase : str = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def a_ ( self ):
# Initialize image_processing
UpperCamelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase : Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
# Test not batched input
UpperCamelCase : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
UpperCamelCase : Union[str, Any] = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
@require_torch
@require_vision
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : List[Any] = ChineseCLIPImageProcessor if is_vision_available() else None
def a_ ( self ):
UpperCamelCase : List[str] = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 3
@property
def a_ ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_resize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """size""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_center_crop""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """center_crop""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_normalize""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_mean""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """image_std""" ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , """do_convert_rgb""" ) )
def a_ ( self ):
pass
def a_ ( self ):
# Initialize image_processing
UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : int = self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input
UpperCamelCase : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
UpperCamelCase : str = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 27
|
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27
| 1
|
"""simple docstring"""
import numpy as np
def A_ ( snake_case_ : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
|
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27
| 1
|
"""simple docstring"""
import argparse
import struct
import unittest
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = data
# Initialize hash values
UpperCamelCase : Union[str, Any] = [
0X6a_09_e6_67,
0Xbb_67_ae_85,
0X3c_6e_f3_72,
0Xa5_4f_f5_3a,
0X51_0e_52_7f,
0X9b_05_68_8c,
0X1f_83_d9_ab,
0X5b_e0_cd_19,
]
# Initialize round constants
UpperCamelCase : List[Any] = [
0X42_8a_2f_98,
0X71_37_44_91,
0Xb5_c0_fb_cf,
0Xe9_b5_db_a5,
0X39_56_c2_5b,
0X59_f1_11_f1,
0X92_3f_82_a4,
0Xab_1c_5e_d5,
0Xd8_07_aa_98,
0X12_83_5b_01,
0X24_31_85_be,
0X55_0c_7d_c3,
0X72_be_5d_74,
0X80_de_b1_fe,
0X9b_dc_06_a7,
0Xc1_9b_f1_74,
0Xe4_9b_69_c1,
0Xef_be_47_86,
0X0f_c1_9d_c6,
0X24_0c_a1_cc,
0X2d_e9_2c_6f,
0X4a_74_84_aa,
0X5c_b0_a9_dc,
0X76_f9_88_da,
0X98_3e_51_52,
0Xa8_31_c6_6d,
0Xb0_03_27_c8,
0Xbf_59_7f_c7,
0Xc6_e0_0b_f3,
0Xd5_a7_91_47,
0X06_ca_63_51,
0X14_29_29_67,
0X27_b7_0a_85,
0X2e_1b_21_38,
0X4d_2c_6d_fc,
0X53_38_0d_13,
0X65_0a_73_54,
0X76_6a_0a_bb,
0X81_c2_c9_2e,
0X92_72_2c_85,
0Xa2_bf_e8_a1,
0Xa8_1a_66_4b,
0Xc2_4b_8b_70,
0Xc7_6c_51_a3,
0Xd1_92_e8_19,
0Xd6_99_06_24,
0Xf4_0e_35_85,
0X10_6a_a0_70,
0X19_a4_c1_16,
0X1e_37_6c_08,
0X27_48_77_4c,
0X34_b0_bc_b5,
0X39_1c_0c_b3,
0X4e_d8_aa_4a,
0X5b_9c_ca_4f,
0X68_2e_6f_f3,
0X74_8f_82_ee,
0X78_a5_63_6f,
0X84_c8_78_14,
0X8c_c7_02_08,
0X90_be_ff_fa,
0Xa4_50_6c_eb,
0Xbe_f9_a3_f7,
0Xc6_71_78_f2,
]
UpperCamelCase : Dict = self.preprocessing(self.data )
self.final_hash()
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = b"""\x80""" + (b"""\x00""" * (63 - (len(SCREAMING_SNAKE_CASE_ ) + 8) % 64))
UpperCamelCase : Any = struct.pack(""">Q""" , (len(SCREAMING_SNAKE_CASE_ ) * 8) )
return data + padding + big_endian_integer
def a_ ( self ):
# Convert into blocks of 64 bytes
UpperCamelCase : int = [
self.preprocessed_data[x : x + 64]
for x in range(0 , len(self.preprocessed_data ) , 64 )
]
for block in self.blocks:
# Convert the given block into a list of 4 byte integers
UpperCamelCase : Tuple = list(struct.unpack(""">16L""" , SCREAMING_SNAKE_CASE_ ) )
# add 48 0-ed integers
words += [0] * 48
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = self.hashes
for index in range(0 , 64 ):
if index > 15:
# modify the zero-ed indexes at the end of the array
UpperCamelCase : Tuple = (
self.ror(words[index - 15] , 7 )
^ self.ror(words[index - 15] , 18 )
^ (words[index - 15] >> 3)
)
UpperCamelCase : str = (
self.ror(words[index - 2] , 17 )
^ self.ror(words[index - 2] , 19 )
^ (words[index - 2] >> 10)
)
UpperCamelCase : int = (
words[index - 16] + sa + words[index - 7] + sa
) % 0X1_00_00_00_00
# Compression
UpperCamelCase : Optional[int] = self.ror(SCREAMING_SNAKE_CASE_ , 6 ) ^ self.ror(SCREAMING_SNAKE_CASE_ , 11 ) ^ self.ror(SCREAMING_SNAKE_CASE_ , 25 )
UpperCamelCase : Optional[int] = (e & f) ^ ((~e & 0Xff_ff_ff_ff) & g)
UpperCamelCase : Any = (
h + sa + ch + self.round_constants[index] + words[index]
) % 0X1_00_00_00_00
UpperCamelCase : Optional[Any] = self.ror(SCREAMING_SNAKE_CASE_ , 2 ) ^ self.ror(SCREAMING_SNAKE_CASE_ , 13 ) ^ self.ror(SCREAMING_SNAKE_CASE_ , 22 )
UpperCamelCase : Any = (a & b) ^ (a & c) ^ (b & c)
UpperCamelCase : Dict = (sa + maj) % 0X1_00_00_00_00
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : str = (
g,
f,
e,
((d + tempa) % 0X1_00_00_00_00),
c,
b,
a,
((tempa + tempa) % 0X1_00_00_00_00),
)
UpperCamelCase : List[Any] = [a, b, c, d, e, f, g, h]
# Modify final values
UpperCamelCase : Union[str, Any] = [
((element + mutated_hash_values[index]) % 0X1_00_00_00_00)
for index, element in enumerate(self.hashes )
]
UpperCamelCase : Dict = """""".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(8 ) for value in self.hashes] )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return 0Xff_ff_ff_ff & (value << (32 - rotations)) | (value >> rotations)
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
import hashlib
UpperCamelCase : str = bytes("""Test String""" , """utf-8""" )
self.assertEqual(SHAaaa(SCREAMING_SNAKE_CASE_ ).hash , hashlib.shaaaa(SCREAMING_SNAKE_CASE_ ).hexdigest() )
def A_ ( ):
'''simple docstring'''
import doctest
doctest.testmod()
UpperCamelCase : str = argparse.ArgumentParser()
parser.add_argument(
"""-s""" ,"""--string""" ,dest="""input_string""" ,default="""Hello World!! Welcome to Cryptography""" ,help="""Hash the string""" ,)
parser.add_argument(
"""-f""" ,"""--file""" ,dest="""input_file""" ,help="""Hash contents of a file""" )
UpperCamelCase : int = parser.parse_args()
UpperCamelCase : int = args.input_string
# hash input should be a bytestring
if args.input_file:
with open(args.input_file ,"""rb""" ) as f:
UpperCamelCase : int = f.read()
else:
UpperCamelCase : str = bytes(snake_case_ ,"""utf-8""" )
print(SHAaaa(snake_case_ ).hash )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : List[str] = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : str = ['''ConvNextFeatureExtractor''']
__A : int = ['''ConvNextImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = [
'''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvNextForImageClassification''',
'''ConvNextModel''',
'''ConvNextPreTrainedModel''',
'''ConvNextBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = [
'''TFConvNextForImageClassification''',
'''TFConvNextModel''',
'''TFConvNextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__A : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 27
|
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
| 1
|
"""simple docstring"""
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Tuple = BertTokenizer
lowercase : int = BertTokenizerFast
lowercase : Any = True
lowercase : List[str] = True
lowercase : List[Any] = filter_non_english
def a_ ( self ):
super().setUp()
UpperCamelCase : List[str] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
UpperCamelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = """UNwant\u00E9d,running"""
UpperCamelCase : Tuple = """unwanted, running"""
return input_text, output_text
def a_ ( self ):
UpperCamelCase : List[Any] = self.tokenizer_class(self.vocab_file )
UpperCamelCase : List[str] = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [9, 6, 7, 12, 10, 11] )
def a_ ( self ):
if not self.test_rust_tokenizer:
return
UpperCamelCase : Any = self.get_tokenizer()
UpperCamelCase : Dict = self.get_rust_tokenizer()
UpperCamelCase : List[str] = """UNwant\u00E9d,running"""
UpperCamelCase : Union[str, Any] = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_rust_tokenizer()
UpperCamelCase : Tuple = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# With lower casing
UpperCamelCase : List[str] = self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_rust_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = """UNwant\u00E9d,running"""
UpperCamelCase : List[str] = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = self.get_rust_tokenizer()
UpperCamelCase : Any = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = rust_tokenizer.encode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def a_ ( self ):
UpperCamelCase : Optional[Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def a_ ( self ):
UpperCamelCase : Tuple = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , strip_accents=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def a_ ( self ):
UpperCamelCase : List[Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def a_ ( self ):
UpperCamelCase : Any = BasicTokenizer()
UpperCamelCase : Any = """a\n'll !!to?'d of, can't."""
UpperCamelCase : Dict = ["""a""", """'""", """ll""", """!""", """!""", """to""", """?""", """'""", """d""", """of""", """,""", """can""", """'""", """t""", """."""]
self.assertListEqual(tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
UpperCamelCase : int = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = i
UpperCamelCase : Dict = WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def a_ ( self ):
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def a_ ( self ):
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def a_ ( self ):
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def a_ ( self ):
UpperCamelCase : str = self.get_tokenizer()
UpperCamelCase : Dict = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.tokenizer_class.from_pretrained("""bert-base-uncased""" )
UpperCamelCase : List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = tokenizer.encode("""multi-sequence build""" , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def a_ ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
UpperCamelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
UpperCamelCase : Any = tokenizer_r.encode_plus(
SCREAMING_SNAKE_CASE_ , return_attention_mask=SCREAMING_SNAKE_CASE_ , return_token_type_ids=SCREAMING_SNAKE_CASE_ , return_offsets_mapping=SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[Any] = tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE_ , """do_lower_case""" ) else False
UpperCamelCase : Dict = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = ["""的""", """人""", """有"""]
UpperCamelCase : Tuple = """""".join(SCREAMING_SNAKE_CASE_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
UpperCamelCase : List[Any] = True
UpperCamelCase : Any = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = False
UpperCamelCase : str = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = tokenizer_r.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_p.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
# it is expected that only the first Chinese character is not preceded by "##".
UpperCamelCase : Tuple = [
f'##{token}' if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE_ )
]
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
__A : List[Any] = '''platform'''
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def A_ ( snake_case_ : str ,snake_case_ : Optional[Any] ,snake_case_ : Union[str, Any]=None ,snake_case_ : List[str]=None ,snake_case_ : List[str]=None ,snake_case_ : Tuple=None ,snake_case_ : str=None ,snake_case_ : Any=None ,):
'''simple docstring'''
if attention_mask is None:
UpperCamelCase : List[str] = np.where(input_ids != config.pad_token_id ,1 ,0 )
if decoder_attention_mask is None:
UpperCamelCase : List[Any] = np.where(decoder_input_ids != config.pad_token_id ,1 ,0 )
if head_mask is None:
UpperCamelCase : int = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCamelCase : int = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCamelCase : int = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=0.02 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Dict = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Any = is_training
UpperCamelCase : int = use_labels
UpperCamelCase : str = vocab_size
UpperCamelCase : str = hidden_size
UpperCamelCase : Optional[int] = num_hidden_layers
UpperCamelCase : Union[str, Any] = num_attention_heads
UpperCamelCase : str = intermediate_size
UpperCamelCase : Optional[int] = hidden_act
UpperCamelCase : List[str] = hidden_dropout_prob
UpperCamelCase : List[str] = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : int = eos_token_id
UpperCamelCase : Any = pad_token_id
UpperCamelCase : int = bos_token_id
UpperCamelCase : Any = initializer_range
def a_ ( self ):
UpperCamelCase : Optional[int] = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
UpperCamelCase : Optional[int] = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
UpperCamelCase : Tuple = shift_tokens_right(SCREAMING_SNAKE_CASE_ , 1 , 2 )
UpperCamelCase : int = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Union[str, Any] = prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return config, inputs_dict
def a_ ( self ):
UpperCamelCase , UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
return config, inputs_dict
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = 20
UpperCamelCase : Tuple = model_class_name(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model.encode(inputs_dict["""input_ids"""] )
UpperCamelCase , UpperCamelCase : Any = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCamelCase : Dict = model.init_cache(decoder_input_ids.shape[0] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
UpperCamelCase : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase : Union[str, Any] = model.decode(
decoder_input_ids[:, :-1] , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
UpperCamelCase : Any = model.decode(
decoder_input_ids[:, -1:] , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=outputs_cache.past_key_values , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = model.decode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = 20
UpperCamelCase : int = model_class_name(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model.encode(inputs_dict["""input_ids"""] )
UpperCamelCase , UpperCamelCase : Dict = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
UpperCamelCase : List[Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
UpperCamelCase : Optional[Any] = model.init_cache(decoder_input_ids.shape[0] , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
UpperCamelCase : Optional[Any] = model.decode(
decoder_input_ids[:, :-1] , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , past_key_values=SCREAMING_SNAKE_CASE_ , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
UpperCamelCase : List[str] = model.decode(
decoder_input_ids[:, -1:] , SCREAMING_SNAKE_CASE_ , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , decoder_position_ids=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model.decode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'Max diff is {diff}' )
@require_flax
class lowerCamelCase ( unittest.TestCase ):
lowercase : int = 9_9
def a_ ( self ):
UpperCamelCase : Dict = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
UpperCamelCase : List[str] = input_ids.shape[0]
UpperCamelCase : int = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def a_ ( self ):
UpperCamelCase , UpperCamelCase , UpperCamelCase : Tuple = self._get_config_and_data()
UpperCamelCase : Union[str, Any] = FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = lm_model(input_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
UpperCamelCase : Tuple = FlaxBlenderbotSmallForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
UpperCamelCase : Dict = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
UpperCamelCase : Dict = lm_model(input_ids=SCREAMING_SNAKE_CASE_ , decoder_input_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
UpperCamelCase : Optional[Any] = shift_tokens_right(SCREAMING_SNAKE_CASE_ , 1 , 2 )
UpperCamelCase : Dict = np.equal(SCREAMING_SNAKE_CASE_ , 1 ).astype(np.floataa ).sum()
UpperCamelCase : Tuple = np.equal(SCREAMING_SNAKE_CASE_ , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(SCREAMING_SNAKE_CASE_ , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase , _UpperCAmelCase ):
lowercase : Union[str, Any] = True
lowercase : List[str] = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowercase : int = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def a_ ( self ):
UpperCamelCase : List[str] = FlaxBlenderbotSmallModelTester(self )
def a_ ( self ):
UpperCamelCase , UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase , UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase , UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase : str = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model_class(SCREAMING_SNAKE_CASE_ )
@jax.jit
def encode_jitted(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ):
return model.encode(input_ids=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )
with self.subTest("""JIT Enabled""" ):
UpperCamelCase : Union[str, Any] = encode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCamelCase : Optional[Any] = encode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(jitted_output.shape , output.shape )
def a_ ( self ):
UpperCamelCase , UpperCamelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
UpperCamelCase : List[Any] = model_class(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
UpperCamelCase : Union[str, Any] = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return model.decode(
decoder_input_ids=SCREAMING_SNAKE_CASE_ , decoder_attention_mask=SCREAMING_SNAKE_CASE_ , encoder_outputs=SCREAMING_SNAKE_CASE_ , )
with self.subTest("""JIT Enabled""" ):
UpperCamelCase : Dict = decode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
UpperCamelCase : List[Any] = decode_jitted(**SCREAMING_SNAKE_CASE_ ).to_tuple()
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
for jitted_output, output in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def a_ ( self ):
for model_class_name in self.all_model_classes:
UpperCamelCase : Optional[int] = model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
UpperCamelCase : Union[str, Any] = np.ones((1, 1) ) * model.config.eos_token_id
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27
| 1
|
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : List[str] = StableDiffusionDiffEditPipeline
lowercase : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
lowercase : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
lowercase : List[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase : Union[str, Any] = frozenset([] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : List[str] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Union[str, Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = DDIMInverseScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_zero=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Any = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
UpperCamelCase : List[str] = CLIPTextModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
UpperCamelCase : Any = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = floats_tensor((1, 16, 16) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Dict = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase : Optional[int] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : Optional[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase : List[Any] = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert("""RGB""" )
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : Any = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Optional[Any] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self ):
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
UpperCamelCase : Optional[Any] = self.get_dummy_components()
UpperCamelCase : str = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = pipe(**SCREAMING_SNAKE_CASE_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = self.pipeline_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
pipe_loaded.to(SCREAMING_SNAKE_CASE_ )
pipe_loaded.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) is None , f'`{optional_component}` did not stay set to None after loading.' , )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = pipe_loaded(**SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Dict = np.abs(output - output_loaded ).max()
self.assertLess(SCREAMING_SNAKE_CASE_ , 1e-4 )
def a_ ( self ):
UpperCamelCase : Dict = """cpu"""
UpperCamelCase : Dict = self.get_dummy_components()
UpperCamelCase : Dict = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_mask_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = pipe.generate_mask(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
UpperCamelCase : List[Any] = np.array([0] * 9 )
UpperCamelCase : List[Any] = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def a_ ( self ):
UpperCamelCase : Dict = """cpu"""
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Any = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.get_dummy_inversion_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = pipe.invert(**SCREAMING_SNAKE_CASE_ ).images
UpperCamelCase : str = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCamelCase : Union[str, Any] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCamelCase : Dict = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
def a_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def a_ ( self ):
UpperCamelCase : str = """cpu"""
UpperCamelCase : int = self.get_dummy_components()
UpperCamelCase : str = {"""beta_start""": 0.00085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
UpperCamelCase : List[Any] = DPMSolverMultistepScheduler(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = DPMSolverMultistepInverseScheduler(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inversion_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = pipe.invert(**SCREAMING_SNAKE_CASE_ ).images
UpperCamelCase : Optional[Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
UpperCamelCase : Tuple = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
UpperCamelCase : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
@require_torch_gpu
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def a_ ( cls ):
UpperCamelCase : Any = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
UpperCamelCase : List[str] = raw_image.convert("""RGB""" ).resize((768, 768) )
UpperCamelCase : Optional[int] = raw_image
def a_ ( self ):
UpperCamelCase : List[Any] = torch.manual_seed(0 )
UpperCamelCase : List[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=SCREAMING_SNAKE_CASE_ , torch_dtype=torch.floataa )
UpperCamelCase : List[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
UpperCamelCase : List[Any] = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = """a bowl of fruit"""
UpperCamelCase : int = """a bowl of pears"""
UpperCamelCase : Tuple = pipe.generate_mask(
image=self.raw_image , source_prompt=SCREAMING_SNAKE_CASE_ , target_prompt=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = pipe.invert(
prompt=SCREAMING_SNAKE_CASE_ , image=self.raw_image , inpaint_strength=0.7 , generator=SCREAMING_SNAKE_CASE_ ).latents
UpperCamelCase : Optional[int] = pipe(
prompt=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , image_latents=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
UpperCamelCase : Dict = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
def a_ ( self ):
UpperCamelCase : Union[str, Any] = torch.manual_seed(0 )
UpperCamelCase : int = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=SCREAMING_SNAKE_CASE_ , torch_dtype=torch.floataa )
UpperCamelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
UpperCamelCase : Dict = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = """a bowl of fruit"""
UpperCamelCase : List[Any] = """a bowl of pears"""
UpperCamelCase : List[str] = pipe.generate_mask(
image=self.raw_image , source_prompt=SCREAMING_SNAKE_CASE_ , target_prompt=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = pipe.invert(
prompt=SCREAMING_SNAKE_CASE_ , image=self.raw_image , inpaint_strength=0.7 , generator=SCREAMING_SNAKE_CASE_ , num_inference_steps=25 , ).latents
UpperCamelCase : Any = pipe(
prompt=SCREAMING_SNAKE_CASE_ , mask_image=SCREAMING_SNAKE_CASE_ , image_latents=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
UpperCamelCase : Any = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 27
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
__A : Dict = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_trocr''': ['''TrOCRProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TrOCRForCausalLM''',
'''TrOCRPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
__A : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
|
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : List[str] = fname.split(os.path.sep )[-1]
return re.search(R"""^(.*)_\d+\.jpg$""" ,snake_case_ ).groups()[0]
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ):
UpperCamelCase : Union[str, Any] = file_names
UpperCamelCase : Any = image_transform
UpperCamelCase : Tuple = label_to_id
def __len__( self ):
return len(self.file_names )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.file_names[idx]
UpperCamelCase : Optional[int] = PIL.Image.open(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = raw_image.convert("""RGB""" )
if self.image_transform is not None:
UpperCamelCase : List[str] = self.image_transform(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = extract_label(SCREAMING_SNAKE_CASE_ )
if self.label_to_id is not None:
UpperCamelCase : Tuple = self.label_to_id[label]
return {"image": image, "label": label}
def A_ ( snake_case_ : List[str] ,snake_case_ : Any ):
'''simple docstring'''
# Initialize accelerator
if args.with_tracking:
UpperCamelCase : List[Any] = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,log_with="""all""" ,project_dir=args.project_dir )
else:
UpperCamelCase : Dict = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : int = config["""lr"""]
UpperCamelCase : Any = int(config["""num_epochs"""] )
UpperCamelCase : Any = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = config["""image_size"""]
if not isinstance(snake_case_ ,(list, tuple) ):
UpperCamelCase : List[Any] = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps ,"""isdigit""" ):
if args.checkpointing_steps == "epoch":
UpperCamelCase : int = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
UpperCamelCase : List[str] = int(args.checkpointing_steps )
else:
raise ValueError(
f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
else:
UpperCamelCase : int = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
UpperCamelCase : List[Any] = os.path.split(snake_case_ )[-1].split(""".""" )[0]
accelerator.init_trackers(snake_case_ ,snake_case_ )
# Grab all the image filenames
UpperCamelCase : Optional[Any] = [os.path.join(args.data_dir ,snake_case_ ) for fname in os.listdir(args.data_dir ) if fname.endswith(""".jpg""" )]
# Build the label correspondences
UpperCamelCase : int = [extract_label(snake_case_ ) for fname in file_names]
UpperCamelCase : int = list(set(snake_case_ ) )
id_to_label.sort()
UpperCamelCase : Optional[int] = {lbl: i for i, lbl in enumerate(snake_case_ )}
# Set the seed before splitting the data.
np.random.seed(snake_case_ )
torch.manual_seed(snake_case_ )
torch.cuda.manual_seed_all(snake_case_ )
# Split our filenames between train and validation
UpperCamelCase : Any = np.random.permutation(len(snake_case_ ) )
UpperCamelCase : Any = int(0.8 * len(snake_case_ ) )
UpperCamelCase : List[str] = random_perm[:cut]
UpperCamelCase : int = random_perm[cut:]
# For training we use a simple RandomResizedCrop
UpperCamelCase : Any = Compose([RandomResizedCrop(snake_case_ ,scale=(0.5, 1.0) ), ToTensor()] )
UpperCamelCase : Dict = PetsDataset(
[file_names[i] for i in train_split] ,image_transform=snake_case_ ,label_to_id=snake_case_ )
# For evaluation, we use a deterministic Resize
UpperCamelCase : str = Compose([Resize(snake_case_ ), ToTensor()] )
UpperCamelCase : int = PetsDataset([file_names[i] for i in eval_split] ,image_transform=snake_case_ ,label_to_id=snake_case_ )
# Instantiate dataloaders.
UpperCamelCase : Optional[Any] = DataLoader(snake_case_ ,shuffle=snake_case_ ,batch_size=snake_case_ ,num_workers=4 )
UpperCamelCase : Tuple = DataLoader(snake_case_ ,shuffle=snake_case_ ,batch_size=snake_case_ ,num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : List[Any] = create_model("""resnet50d""" ,pretrained=snake_case_ ,num_classes=len(snake_case_ ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : str = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
UpperCamelCase : int = False
for param in model.get_classifier().parameters():
UpperCamelCase : List[Any] = True
# We normalize the batches of images to be a bit faster.
UpperCamelCase : Union[str, Any] = torch.tensor(model.default_cfg["""mean"""] )[None, :, None, None].to(accelerator.device )
UpperCamelCase : Optional[int] = torch.tensor(model.default_cfg["""std"""] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : Any = torch.optim.Adam(params=model.parameters() ,lr=lr / 2_5 )
# Instantiate learning rate scheduler
UpperCamelCase : Optional[int] = OneCycleLR(optimizer=snake_case_ ,max_lr=snake_case_ ,epochs=snake_case_ ,steps_per_epoch=len(snake_case_ ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Tuple = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# We need to keep track of how many total steps we have iterated over
UpperCamelCase : Optional[Any] = 0
# We also need to keep track of the starting epoch so files are named properly
UpperCamelCase : Optional[int] = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}' )
accelerator.load_state(args.resume_from_checkpoint )
UpperCamelCase : int = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
UpperCamelCase : Dict = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
UpperCamelCase : str = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
UpperCamelCase : List[Any] = os.path.splitext(snake_case_ )[0]
if "epoch" in training_difference:
UpperCamelCase : Tuple = int(training_difference.replace("""epoch_""" ,"""""" ) ) + 1
UpperCamelCase : Dict = None
else:
UpperCamelCase : int = int(training_difference.replace("""step_""" ,"""""" ) )
UpperCamelCase : Optional[int] = resume_step // len(snake_case_ )
resume_step -= starting_epoch * len(snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ,snake_case_ ):
model.train()
if args.with_tracking:
UpperCamelCase : List[str] = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
UpperCamelCase : Any = accelerator.skip_first_batches(snake_case_ ,snake_case_ )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
UpperCamelCase : str = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
UpperCamelCase : Optional[Any] = {k: v.to(accelerator.device ) for k, v in batch.items()}
UpperCamelCase : Union[str, Any] = (batch["""image"""] - mean) / std
UpperCamelCase : str = model(snake_case_ )
UpperCamelCase : List[Any] = torch.nn.functional.cross_entropy(snake_case_ ,batch["""label"""] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : Union[str, Any] = f'step_{overall_step}'
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
UpperCamelCase : int = os.path.join(args.output_dir ,snake_case_ )
accelerator.save_state(snake_case_ )
model.eval()
UpperCamelCase : int = 0
UpperCamelCase : List[Any] = 0
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
UpperCamelCase : str = {k: v.to(accelerator.device ) for k, v in batch.items()}
UpperCamelCase : Optional[int] = (batch["""image"""] - mean) / std
with torch.no_grad():
UpperCamelCase : Any = model(snake_case_ )
UpperCamelCase : List[str] = outputs.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""label"""]) )
UpperCamelCase : Dict = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
UpperCamelCase : int = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}: {1_0_0 * eval_metric:.2f}' )
if args.with_tracking:
accelerator.log(
{
"""accuracy""": 1_0_0 * eval_metric,
"""train_loss""": total_loss.item() / len(snake_case_ ),
"""epoch""": epoch,
} ,step=snake_case_ ,)
if checkpointing_steps == "epoch":
UpperCamelCase : Any = f'epoch_{epoch}'
if args.output_dir is not None:
UpperCamelCase : int = os.path.join(args.output_dir ,snake_case_ )
accelerator.save_state(snake_case_ )
if args.with_tracking:
accelerator.end_training()
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Dict = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument("""--data_dir""" ,required=snake_case_ ,help="""The data folder on disk.""" )
parser.add_argument("""--fp16""" ,action="""store_true""" ,help="""If passed, will use FP16 training.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
parser.add_argument(
"""--checkpointing_steps""" ,type=snake_case_ ,default=snake_case_ ,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""" ,)
parser.add_argument(
"""--output_dir""" ,type=snake_case_ ,default=""".""" ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,)
parser.add_argument(
"""--resume_from_checkpoint""" ,type=snake_case_ ,default=snake_case_ ,help="""If the training should continue from a checkpoint folder.""" ,)
parser.add_argument(
"""--with_tracking""" ,action="""store_true""" ,help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" ,)
parser.add_argument(
"""--project_dir""" ,type=snake_case_ ,default="""logs""" ,help="""Location on where to store experiment tracking logs` and relevent project information""" ,)
UpperCamelCase : int = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 6_4, """image_size""": 2_2_4}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27
| 1
|
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def A_ ( snake_case_ : Tuple ,snake_case_ : Union[str, Any] ,snake_case_ : str ):
'''simple docstring'''
# Initialise PyTorch model
UpperCamelCase : List[str] = AlbertConfig.from_json_file(snake_case_ )
print(f'Building PyTorch model from configuration: {config}' )
UpperCamelCase : int = AlbertForPreTraining(snake_case_ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(snake_case_ ,snake_case_ ,snake_case_ )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() ,snake_case_ )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--albert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained ALBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
__A : str = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
import cva
import numpy as np
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if k in (0.04, 0.06):
UpperCamelCase : Tuple = k
UpperCamelCase : Any = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ):
return str(self.k )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = cva.imread(SCREAMING_SNAKE_CASE_ , 0 )
UpperCamelCase , UpperCamelCase : int = img.shape
UpperCamelCase : list[list[int]] = []
UpperCamelCase : Optional[int] = img.copy()
UpperCamelCase : Tuple = cva.cvtColor(SCREAMING_SNAKE_CASE_ , cva.COLOR_GRAY2RGB )
UpperCamelCase , UpperCamelCase : Any = np.gradient(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = dx**2
UpperCamelCase : List[str] = dy**2
UpperCamelCase : List[str] = dx * dy
UpperCamelCase : Optional[Any] = 0.04
UpperCamelCase : Optional[Any] = self.window_size // 2
for y in range(SCREAMING_SNAKE_CASE_ , h - offset ):
for x in range(SCREAMING_SNAKE_CASE_ , w - offset ):
UpperCamelCase : Tuple = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
UpperCamelCase : Union[str, Any] = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
UpperCamelCase : str = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
UpperCamelCase : Optional[Any] = (wxx * wyy) - (wxy**2)
UpperCamelCase : List[str] = wxx + wyy
UpperCamelCase : Any = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 255 )
return color_img, corner_list
if __name__ == "__main__":
__A : List[Any] = HarrisCorner(0.04, 3)
__A , __A : Tuple = edge_detect.detect('''path_to_image''')
cva.imwrite('''detect.png''', color_img)
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : Optional[int] = torch.nn.Linear(10 , 10 )
UpperCamelCase : Tuple = torch.optim.SGD(model.parameters() , 0.1 )
UpperCamelCase : str = Accelerator()
UpperCamelCase : Union[str, Any] = accelerator.prepare(SCREAMING_SNAKE_CASE_ )
try:
pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE_ ) )
except Exception as e:
self.fail(f'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 27
|
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCamelCase :
lowercase : int
lowercase : int
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : list[list[Edge]] = [[] for _ in range(SCREAMING_SNAKE_CASE_ )]
UpperCamelCase : str = size
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
return iter(self._graph[vertex] )
@property
def a_ ( self ):
return self._size
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if weight not in (0, 1):
raise ValueError("""Edge weight must be either 0 or 1.""" )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError("""Vertex indexes must be in [0; size).""" )
self._graph[from_vertex].append(Edge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = deque([start_vertex] )
UpperCamelCase : list[int | None] = [None] * self.size
UpperCamelCase : str = 0
while queue:
UpperCamelCase : str = queue.popleft()
UpperCamelCase : Optional[Any] = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
UpperCamelCase : Dict = current_distance + edge.weight
UpperCamelCase : Dict = distances[edge.destination_vertex]
if (
isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
and new_distance >= dest_vertex_distance
):
continue
UpperCamelCase : int = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError("""No path from start_vertex to finish_vertex.""" )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
|
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__A : str = logging.getLogger(__name__)
__A : List[Any] = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__A : Tuple = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCamelCase :
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_UpperCAmelCase )} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
lowercase : str = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def a_ ( self ):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"""--config_overrides can't be used in combination with --config_name or --model_name_or_path""" )
@dataclass
class lowerCamelCase :
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
lowercase : Optional[str] = field(default=_UpperCAmelCase , metadata={'help': 'The input training data file (a text file).'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'} , )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
lowercase : Optional[int] = field(
default=5 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
lowercase : Optional[int] = field(
default=_UpperCAmelCase , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated. Default to the max input length of the model.'
)
} , )
lowercase : Optional[int] = field(
default=_UpperCAmelCase , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
lowercase : float = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
lowercase : bool = field(
default=_UpperCAmelCase , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
def a_ ( self ):
if self.train_file is not None:
UpperCamelCase : List[Any] = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
UpperCamelCase : str = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def A_ ( snake_case_ : List[str] ,snake_case_ : Tuple ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ,encoding="""utf-8""" ) as f:
UpperCamelCase : Optional[int] = [json.loads(snake_case_ ) for line in f.read().splitlines() if (len(snake_case_ ) > 0 and not line.isspace())]
assert len(snake_case_ ) == len(snake_case_ )
UpperCamelCase : Tuple = {c: dataset[c] for c in dataset.column_names}
UpperCamelCase : int = refs
return Dataset.from_dict(snake_case_ )
def A_ ( ):
'''simple docstring'''
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCamelCase : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[int] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCamelCase , UpperCamelCase , UpperCamelCase : Dict = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
UpperCamelCase : int = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
UpperCamelCase : List[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,datefmt="""%m/%d/%Y %H:%M:%S""" ,handlers=[logging.StreamHandler(sys.stdout )] ,)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" ,snake_case_ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
UpperCamelCase : List[str] = load_dataset(data_args.dataset_name ,data_args.dataset_config_name )
if "validation" not in datasets.keys():
UpperCamelCase : List[str] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=f'train[:{data_args.validation_split_percentage}%]' ,)
UpperCamelCase : List[str] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=f'train[{data_args.validation_split_percentage}%:]' ,)
else:
UpperCamelCase : List[str] = {}
if data_args.train_file is not None:
UpperCamelCase : Any = data_args.train_file
if data_args.validation_file is not None:
UpperCamelCase : Dict = data_args.validation_file
UpperCamelCase : Any = data_args.train_file.split(""".""" )[-1]
if extension == "txt":
UpperCamelCase : int = """text"""
UpperCamelCase : List[Any] = load_dataset(snake_case_ ,data_files=snake_case_ )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCamelCase : Dict = {
"""cache_dir""": model_args.cache_dir,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.config_name:
UpperCamelCase : Tuple = AutoConfig.from_pretrained(model_args.config_name ,**snake_case_ )
elif model_args.model_name_or_path:
UpperCamelCase : Any = AutoConfig.from_pretrained(model_args.model_name_or_path ,**snake_case_ )
else:
UpperCamelCase : str = CONFIG_MAPPING[model_args.model_type]()
logger.warning("""You are instantiating a new config instance from scratch.""" )
if model_args.config_overrides is not None:
logger.info(f'Overriding config: {model_args.config_overrides}' )
config.update_from_string(model_args.config_overrides )
logger.info(f'New config: {config}' )
UpperCamelCase : Dict = {
"""cache_dir""": model_args.cache_dir,
"""use_fast""": model_args.use_fast_tokenizer,
"""revision""": model_args.model_revision,
"""use_auth_token""": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
UpperCamelCase : int = AutoTokenizer.from_pretrained(model_args.tokenizer_name ,**snake_case_ )
elif model_args.model_name_or_path:
UpperCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(model_args.model_name_or_path ,**snake_case_ )
else:
raise ValueError(
"""You are instantiating a new tokenizer from scratch. This is not supported by this script."""
"""You can do it from another script, save it, and load it from here, using --tokenizer_name.""" )
if model_args.model_name_or_path:
UpperCamelCase : Dict = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) ,config=snake_case_ ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
else:
logger.info("""Training new model from scratch""" )
UpperCamelCase : Dict = AutoModelForMaskedLM.from_config(snake_case_ )
model.resize_token_embeddings(len(snake_case_ ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
UpperCamelCase : Tuple = datasets["""train"""].column_names
else:
UpperCamelCase : Any = datasets["""validation"""].column_names
UpperCamelCase : str = """text""" if """text""" in column_names else column_names[0]
UpperCamelCase : Optional[Any] = """max_length""" if data_args.pad_to_max_length else False
def tokenize_function(snake_case_ : Optional[int] ):
# Remove empty lines
UpperCamelCase : Optional[Any] = [line for line in examples["""text"""] if len(snake_case_ ) > 0 and not line.isspace()]
return tokenizer(examples["""text"""] ,padding=snake_case_ ,truncation=snake_case_ ,max_length=data_args.max_seq_length )
UpperCamelCase : List[str] = datasets.map(
snake_case_ ,batched=snake_case_ ,num_proc=data_args.preprocessing_num_workers ,remove_columns=[text_column_name] ,load_from_cache_file=not data_args.overwrite_cache ,)
# Add the chinese references if provided
if data_args.train_ref_file is not None:
UpperCamelCase : List[Any] = add_chinese_references(tokenized_datasets["""train"""] ,data_args.train_ref_file )
if data_args.validation_ref_file is not None:
UpperCamelCase : Any = add_chinese_references(
tokenized_datasets["""validation"""] ,data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
UpperCamelCase : List[Any] = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
UpperCamelCase : List[str] = False
# Data collator
# This one will take care of randomly masking the tokens.
UpperCamelCase : Dict = DataCollatorForWholeWordMask(tokenizer=snake_case_ ,mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
UpperCamelCase : Any = Trainer(
model=snake_case_ ,args=snake_case_ ,train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None ,eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None ,tokenizer=snake_case_ ,data_collator=snake_case_ ,)
# Training
if training_args.do_train:
if last_checkpoint is not None:
UpperCamelCase : List[Any] = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
UpperCamelCase : Optional[Any] = model_args.model_name_or_path
else:
UpperCamelCase : int = None
UpperCamelCase : Any = trainer.train(resume_from_checkpoint=snake_case_ )
trainer.save_model() # Saves the tokenizer too for easy upload
UpperCamelCase : List[str] = os.path.join(training_args.output_dir ,"""train_results.txt""" )
if trainer.is_world_process_zero():
with open(snake_case_ ,"""w""" ) as writer:
logger.info("""***** Train results *****""" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(f' {key} = {value}' )
writer.write(f'{key} = {value}\n' )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir ,"""trainer_state.json""" ) )
# Evaluation
UpperCamelCase : str = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
UpperCamelCase : List[str] = trainer.evaluate()
UpperCamelCase : List[str] = math.exp(eval_output["""eval_loss"""] )
UpperCamelCase : Any = perplexity
UpperCamelCase : Optional[int] = os.path.join(training_args.output_dir ,"""eval_results_mlm_wwm.txt""" )
if trainer.is_world_process_zero():
with open(snake_case_ ,"""w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in sorted(results.items() ):
logger.info(f' {key} = {value}' )
writer.write(f'{key} = {value}\n' )
return results
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27
| 1
|
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
__A : Optional[int] = logging.get_logger(__name__)
__A : int = OrderedDict(
[
('''audio-spectrogram-transformer''', '''ASTFeatureExtractor'''),
('''beit''', '''BeitFeatureExtractor'''),
('''chinese_clip''', '''ChineseCLIPFeatureExtractor'''),
('''clap''', '''ClapFeatureExtractor'''),
('''clip''', '''CLIPFeatureExtractor'''),
('''clipseg''', '''ViTFeatureExtractor'''),
('''conditional_detr''', '''ConditionalDetrFeatureExtractor'''),
('''convnext''', '''ConvNextFeatureExtractor'''),
('''cvt''', '''ConvNextFeatureExtractor'''),
('''data2vec-audio''', '''Wav2Vec2FeatureExtractor'''),
('''data2vec-vision''', '''BeitFeatureExtractor'''),
('''deformable_detr''', '''DeformableDetrFeatureExtractor'''),
('''deit''', '''DeiTFeatureExtractor'''),
('''detr''', '''DetrFeatureExtractor'''),
('''dinat''', '''ViTFeatureExtractor'''),
('''donut-swin''', '''DonutFeatureExtractor'''),
('''dpt''', '''DPTFeatureExtractor'''),
('''encodec''', '''EncodecFeatureExtractor'''),
('''flava''', '''FlavaFeatureExtractor'''),
('''glpn''', '''GLPNFeatureExtractor'''),
('''groupvit''', '''CLIPFeatureExtractor'''),
('''hubert''', '''Wav2Vec2FeatureExtractor'''),
('''imagegpt''', '''ImageGPTFeatureExtractor'''),
('''layoutlmv2''', '''LayoutLMv2FeatureExtractor'''),
('''layoutlmv3''', '''LayoutLMv3FeatureExtractor'''),
('''levit''', '''LevitFeatureExtractor'''),
('''maskformer''', '''MaskFormerFeatureExtractor'''),
('''mctct''', '''MCTCTFeatureExtractor'''),
('''mobilenet_v1''', '''MobileNetV1FeatureExtractor'''),
('''mobilenet_v2''', '''MobileNetV2FeatureExtractor'''),
('''mobilevit''', '''MobileViTFeatureExtractor'''),
('''nat''', '''ViTFeatureExtractor'''),
('''owlvit''', '''OwlViTFeatureExtractor'''),
('''perceiver''', '''PerceiverFeatureExtractor'''),
('''poolformer''', '''PoolFormerFeatureExtractor'''),
('''regnet''', '''ConvNextFeatureExtractor'''),
('''resnet''', '''ConvNextFeatureExtractor'''),
('''segformer''', '''SegformerFeatureExtractor'''),
('''sew''', '''Wav2Vec2FeatureExtractor'''),
('''sew-d''', '''Wav2Vec2FeatureExtractor'''),
('''speech_to_text''', '''Speech2TextFeatureExtractor'''),
('''speecht5''', '''SpeechT5FeatureExtractor'''),
('''swiftformer''', '''ViTFeatureExtractor'''),
('''swin''', '''ViTFeatureExtractor'''),
('''swinv2''', '''ViTFeatureExtractor'''),
('''table-transformer''', '''DetrFeatureExtractor'''),
('''timesformer''', '''VideoMAEFeatureExtractor'''),
('''tvlt''', '''TvltFeatureExtractor'''),
('''unispeech''', '''Wav2Vec2FeatureExtractor'''),
('''unispeech-sat''', '''Wav2Vec2FeatureExtractor'''),
('''van''', '''ConvNextFeatureExtractor'''),
('''videomae''', '''VideoMAEFeatureExtractor'''),
('''vilt''', '''ViltFeatureExtractor'''),
('''vit''', '''ViTFeatureExtractor'''),
('''vit_mae''', '''ViTFeatureExtractor'''),
('''vit_msn''', '''ViTFeatureExtractor'''),
('''wav2vec2''', '''Wav2Vec2FeatureExtractor'''),
('''wav2vec2-conformer''', '''Wav2Vec2FeatureExtractor'''),
('''wavlm''', '''Wav2Vec2FeatureExtractor'''),
('''whisper''', '''WhisperFeatureExtractor'''),
('''xclip''', '''CLIPFeatureExtractor'''),
('''yolos''', '''YolosFeatureExtractor'''),
]
)
__A : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def A_ ( snake_case_ : str ):
'''simple docstring'''
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
UpperCamelCase : List[str] = model_type_to_module_name(snake_case_ )
UpperCamelCase : Union[str, Any] = importlib.import_module(f'.{module_name}' ,"""transformers.models""" )
try:
return getattr(snake_case_ ,snake_case_ )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(snake_case_ ,"""__name__""" ,snake_case_ ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
UpperCamelCase : Optional[Any] = importlib.import_module("""transformers""" )
if hasattr(snake_case_ ,snake_case_ ):
return getattr(snake_case_ ,snake_case_ )
return None
def A_ ( snake_case_ : Union[str, os.PathLike] ,snake_case_ : Optional[Union[str, os.PathLike]] = None ,snake_case_ : bool = False ,snake_case_ : bool = False ,snake_case_ : Optional[Dict[str, str]] = None ,snake_case_ : Optional[Union[bool, str]] = None ,snake_case_ : Optional[str] = None ,snake_case_ : bool = False ,**snake_case_ : str ,):
'''simple docstring'''
UpperCamelCase : Tuple = get_file_from_repo(
snake_case_ ,snake_case_ ,cache_dir=snake_case_ ,force_download=snake_case_ ,resume_download=snake_case_ ,proxies=snake_case_ ,use_auth_token=snake_case_ ,revision=snake_case_ ,local_files_only=snake_case_ ,)
if resolved_config_file is None:
logger.info(
"""Could not locate the feature extractor configuration file, will try to use the model config instead.""" )
return {}
with open(snake_case_ ,encoding="""utf-8""" ) as reader:
return json.load(snake_case_ )
class lowerCamelCase :
def __init__( self ):
raise EnvironmentError(
"""AutoFeatureExtractor is designed to be instantiated """
"""using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.""" )
@classmethod
@replace_list_option_in_docstrings(SCREAMING_SNAKE_CASE_ )
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = kwargs.pop("""config""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""trust_remote_code""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = True
UpperCamelCase , UpperCamelCase : Union[str, Any] = FeatureExtractionMixin.get_feature_extractor_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = config_dict.get("""feature_extractor_type""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = None
if "AutoFeatureExtractor" in config_dict.get("""auto_map""" , {} ):
UpperCamelCase : List[Any] = config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
# It could be in `config.feature_extractor_type``
UpperCamelCase : int = getattr(SCREAMING_SNAKE_CASE_ , """feature_extractor_type""" , SCREAMING_SNAKE_CASE_ )
if hasattr(SCREAMING_SNAKE_CASE_ , """auto_map""" ) and "AutoFeatureExtractor" in config.auto_map:
UpperCamelCase : List[str] = config.auto_map["""AutoFeatureExtractor"""]
if feature_extractor_class is not None:
UpperCamelCase : Optional[int] = feature_extractor_class_from_name(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = feature_extractor_auto_map is not None
UpperCamelCase : Optional[int] = feature_extractor_class is not None or type(SCREAMING_SNAKE_CASE_ ) in FEATURE_EXTRACTOR_MAPPING
UpperCamelCase : Optional[Any] = resolve_trust_remote_code(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if has_remote_code and trust_remote_code:
UpperCamelCase : Union[str, Any] = get_class_from_dynamic_module(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = kwargs.pop("""code_revision""" , SCREAMING_SNAKE_CASE_ )
if os.path.isdir(SCREAMING_SNAKE_CASE_ ):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(SCREAMING_SNAKE_CASE_ ) in FEATURE_EXTRACTOR_MAPPING:
UpperCamelCase : Tuple = FEATURE_EXTRACTOR_MAPPING[type(SCREAMING_SNAKE_CASE_ )]
return feature_extractor_class.from_dict(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
raise ValueError(
f'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
f'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}' )
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
FEATURE_EXTRACTOR_MAPPING.register(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27
| 1
|
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__A : Tuple = False
try:
__A : List[Any] = _is_package_available('''google.colab''')
except ModuleNotFoundError:
pass
@input.register
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = [] ):
UpperCamelCase : Optional[Any] = 0
UpperCamelCase : List[str] = choices
UpperCamelCase : Union[str, Any] = prompt
if sys.platform == "win32":
UpperCamelCase : List[Any] = """*"""
else:
UpperCamelCase : List[Any] = """➔ """
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = "" ):
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , SCREAMING_SNAKE_CASE_ )
else:
forceWrite(self.choices[index] , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
if index == self.position:
forceWrite(f' {self.arrow_char} ' )
self.write_choice(SCREAMING_SNAKE_CASE_ )
else:
forceWrite(f' {self.choices[index]}' )
reset_cursor()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 1 ):
UpperCamelCase : str = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(SCREAMING_SNAKE_CASE_ )
move_cursor(SCREAMING_SNAKE_CASE_ , direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def a_ ( self ):
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def a_ ( self ):
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def a_ ( self ):
move_cursor(len(self.choices ) - self.position , """DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def a_ ( self ):
move_cursor(len(self.choices ) - self.position , """DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(SCREAMING_SNAKE_CASE_ )] for number in range(10 )] )
def a_ ( self ):
UpperCamelCase : Dict = int(chr(self.current_selection ) )
UpperCamelCase : Tuple = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP , -movement )
elif self.position < index:
self.move_direction(Direction.DOWN , SCREAMING_SNAKE_CASE_ )
else:
return
else:
return
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if self.prompt:
linebreak()
forceWrite(self.prompt , """\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" , """\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" , """\n""" )
UpperCamelCase : Optional[int] = default_choice
for i in range(len(self.choices ) ):
self.print_choice(SCREAMING_SNAKE_CASE_ )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position , """UP""" )
with cursor.hide():
while True:
if in_colab:
try:
UpperCamelCase : Dict = int(builtins.input() )
except ValueError:
UpperCamelCase : Tuple = default_choice
else:
UpperCamelCase : Optional[int] = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 , """UP""" )
clear_line()
self.write_choice(SCREAMING_SNAKE_CASE_ , """\n""" )
return choice
| 27
|
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
__A : Dict = '''\
Text data.
Second line of data.'''
__A : Optional[Any] = '''file'''
@pytest.fixture(scope="""session""" )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : str = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
UpperCamelCase : List[Any] = bytes(snake_case_ ,"""utf-8""" )
with zstd.open(snake_case_ ,"""wb""" ) as f:
f.write(snake_case_ )
return path
@pytest.fixture
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
with open(os.path.join(tmpfs.local_root_dir ,snake_case_ ) ,"""w""" ) as f:
f.write(snake_case_ )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" ,["""gzip""", """xz""", """zstd"""] )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : Optional[int] ,snake_case_ : List[Any] ,snake_case_ : str ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
UpperCamelCase : str = input_paths[compression_format]
UpperCamelCase : Union[str, Any] = tmp_path / """cache"""
UpperCamelCase : List[Any] = DownloadConfig(cache_dir=snake_case_ ,extract_compressed_file=snake_case_ )
UpperCamelCase : Tuple = cached_path(snake_case_ ,download_config=snake_case_ )
with open(snake_case_ ) as f:
UpperCamelCase : Dict = f.read()
with open(snake_case_ ) as f:
UpperCamelCase : Optional[int] = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" ,[True, False] )
@pytest.mark.parametrize("""default_cache_dir""" ,[True, False] )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[Any] ,snake_case_ : Optional[int] ,snake_case_ : Dict ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = """custom_cache"""
UpperCamelCase : str = """custom_extracted_dir"""
UpperCamelCase : Optional[Any] = tmp_path / """custom_extracted_path"""
if default_extracted:
UpperCamelCase : Optional[Any] = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" ,snake_case_ )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" ,str(snake_case_ ) )
UpperCamelCase : str = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
UpperCamelCase : int = xz_file
UpperCamelCase : List[str] = (
DownloadConfig(extract_compressed_file=snake_case_ )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir ,extract_compressed_file=snake_case_ )
)
UpperCamelCase : List[Any] = cached_path(snake_case_ ,download_config=snake_case_ )
assert Path(snake_case_ ).parent.parts[-2:] == expected
def A_ ( snake_case_ : int ):
'''simple docstring'''
# absolute path
UpperCamelCase : Tuple = str(Path(snake_case_ ).resolve() )
assert cached_path(snake_case_ ) == text_file
# relative path
UpperCamelCase : str = str(Path(snake_case_ ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(snake_case_ ) == text_file
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
# absolute path
UpperCamelCase : Any = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(snake_case_ ):
cached_path(snake_case_ )
# relative path
UpperCamelCase : Any = """./__missing_file__.txt"""
with pytest.raises(snake_case_ ):
cached_path(snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : str = get_from_cache(f'tmp://{tmpfs_file}' )
with open(snake_case_ ) as f:
UpperCamelCase : Any = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( ):
'''simple docstring'''
with pytest.raises(snake_case_ ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Any = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(snake_case_ ):
http_get("""https://huggingface.co""" ,temp_file=snake_case_ )
with pytest.raises(snake_case_ ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(snake_case_ ):
ftp_get("""ftp://huggingface.co""" ,temp_file=snake_case_ )
with pytest.raises(snake_case_ ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" ,snake_case_ )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Any = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(snake_case_ ):
fsspec_get("""s3://huggingface.co""" ,temp_file=snake_case_ )
with pytest.raises(snake_case_ ):
fsspec_head("""s3://huggingface.co""" )
| 27
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
| 1
|
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A : Tuple = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
__A : List[Any] = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
__A : Tuple = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def A_ ( snake_case_ : str ):
'''simple docstring'''
def remove_articles(snake_case_ : Dict ):
UpperCamelCase : str = re.compile(R"""\b(a|an|the)\b""" ,re.UNICODE )
return re.sub(snake_case_ ,""" """ ,snake_case_ )
def white_space_fix(snake_case_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(snake_case_ : Union[str, Any] ):
UpperCamelCase : Union[str, Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(snake_case_ : Optional[Any] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(snake_case_ ) ) ) )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
return int(normalize_answer(snake_case_ ) == normalize_answer(snake_case_ ) )
def A_ ( snake_case_ : Any ,snake_case_ : Union[str, Any] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [any(compute_exact(snake_case_ ,snake_case_ ) for ref in refs ) for pred, refs in zip(snake_case_ ,snake_case_ )]
return (sum(snake_case_ ) / len(snake_case_ )) * 1_0_0
def A_ ( snake_case_ : str ,snake_case_ : Any ,snake_case_ : List[str] ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = [rgram for rgrams in rgramslist for rgram in rgrams]
UpperCamelCase : Dict = Counter(snake_case_ )
UpperCamelCase : Optional[int] = Counter(snake_case_ )
UpperCamelCase : Optional[int] = Counter()
for sgram, scount in sgramcounter.items():
UpperCamelCase : List[str] = scount * numref
UpperCamelCase : Any = Counter(snake_case_ )
UpperCamelCase : Any = Counter()
for cgram, ccount in cgramcounter.items():
UpperCamelCase : Union[str, Any] = ccount * numref
# KEEP
UpperCamelCase : Optional[Any] = sgramcounter_rep & cgramcounter_rep
UpperCamelCase : Optional[int] = keepgramcounter_rep & rgramcounter
UpperCamelCase : int = sgramcounter_rep & rgramcounter
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : Tuple = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCamelCase : List[Any] = 1
UpperCamelCase : List[str] = 1
if len(snake_case_ ) > 0:
UpperCamelCase : List[Any] = keeptmpscorea / len(snake_case_ )
if len(snake_case_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
UpperCamelCase : Tuple = keeptmpscorea / sum(keepgramcounterall_rep.values() )
UpperCamelCase : List[Any] = 0
if keepscore_precision > 0 or keepscore_recall > 0:
UpperCamelCase : str = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
UpperCamelCase : str = sgramcounter_rep - cgramcounter_rep
UpperCamelCase : Optional[int] = delgramcounter_rep - rgramcounter
UpperCamelCase : List[Any] = sgramcounter_rep - rgramcounter
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[Any] = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCamelCase : Tuple = 1
if len(snake_case_ ) > 0:
UpperCamelCase : str = deltmpscorea / len(snake_case_ )
# ADDITION
UpperCamelCase : str = set(snake_case_ ) - set(snake_case_ )
UpperCamelCase : Any = set(snake_case_ ) & set(snake_case_ )
UpperCamelCase : Union[str, Any] = set(snake_case_ ) - set(snake_case_ )
UpperCamelCase : Any = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCamelCase : Any = 1
UpperCamelCase : Tuple = 1
if len(snake_case_ ) > 0:
UpperCamelCase : List[str] = addtmpscore / len(snake_case_ )
if len(snake_case_ ) > 0:
UpperCamelCase : Any = addtmpscore / len(snake_case_ )
UpperCamelCase : Optional[Any] = 0
if addscore_precision > 0 or addscore_recall > 0:
UpperCamelCase : Optional[int] = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Union[str, Any] ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = len(snake_case_ )
UpperCamelCase : Dict = ssent.split(""" """ )
UpperCamelCase : Optional[Any] = csent.split(""" """ )
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : List[str] = []
UpperCamelCase : str = []
UpperCamelCase : Any = []
UpperCamelCase : Optional[Any] = []
UpperCamelCase : str = []
UpperCamelCase : Any = []
UpperCamelCase : Tuple = []
UpperCamelCase : Any = []
UpperCamelCase : Optional[Any] = []
for rsent in rsents:
UpperCamelCase : Any = rsent.split(""" """ )
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : List[str] = []
UpperCamelCase : str = []
ragramslist.append(snake_case_ )
for i in range(0 ,len(snake_case_ ) - 1 ):
if i < len(snake_case_ ) - 1:
UpperCamelCase : str = ragrams[i] + """ """ + ragrams[i + 1]
ragrams.append(snake_case_ )
if i < len(snake_case_ ) - 2:
UpperCamelCase : Optional[int] = ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
ragrams.append(snake_case_ )
if i < len(snake_case_ ) - 3:
UpperCamelCase : Any = ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
ragrams.append(snake_case_ )
ragramslist.append(snake_case_ )
ragramslist.append(snake_case_ )
ragramslist.append(snake_case_ )
for i in range(0 ,len(snake_case_ ) - 1 ):
if i < len(snake_case_ ) - 1:
UpperCamelCase : Dict = sagrams[i] + """ """ + sagrams[i + 1]
sagrams.append(snake_case_ )
if i < len(snake_case_ ) - 2:
UpperCamelCase : Optional[Any] = sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
sagrams.append(snake_case_ )
if i < len(snake_case_ ) - 3:
UpperCamelCase : List[str] = sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
sagrams.append(snake_case_ )
for i in range(0 ,len(snake_case_ ) - 1 ):
if i < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = cagrams[i] + """ """ + cagrams[i + 1]
cagrams.append(snake_case_ )
if i < len(snake_case_ ) - 2:
UpperCamelCase : Optional[Any] = cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
cagrams.append(snake_case_ )
if i < len(snake_case_ ) - 3:
UpperCamelCase : Optional[int] = cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
cagrams.append(snake_case_ )
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : str = SARIngram(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : List[Any] = SARIngram(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : List[Any] = SARIngram(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Any = SARIngram(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
UpperCamelCase : Optional[Any] = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
UpperCamelCase : Any = sum([delascore, delascore, delascore, delascore] ) / 4
UpperCamelCase : List[str] = sum([addascore, addascore, addascore, addascore] ) / 4
UpperCamelCase : Any = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : bool = True ,snake_case_ : str = "13a" ,snake_case_ : bool = True ):
'''simple docstring'''
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
UpperCamelCase : int = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
UpperCamelCase : Any = sacrebleu.metrics.bleu._get_tokenizer(snake_case_ )()(snake_case_ )
else:
UpperCamelCase : List[str] = sacrebleu.TOKENIZERS[tokenizer]()(snake_case_ )
elif tokenizer == "moses":
UpperCamelCase : Any = sacremoses.MosesTokenizer().tokenize(snake_case_ ,return_str=snake_case_ ,escape=snake_case_ )
elif tokenizer == "penn":
UpperCamelCase : str = sacremoses.MosesTokenizer().penn_tokenize(snake_case_ ,return_str=snake_case_ )
else:
UpperCamelCase : List[str] = sentence
if not return_str:
UpperCamelCase : Union[str, Any] = normalized_sent.split()
return normalized_sent
def A_ ( snake_case_ : List[str] ,snake_case_ : List[Any] ,snake_case_ : int ):
'''simple docstring'''
if not (len(snake_case_ ) == len(snake_case_ ) == len(snake_case_ )):
raise ValueError("""Sources length must match predictions and references lengths.""" )
UpperCamelCase : Tuple = 0
for src, pred, refs in zip(snake_case_ ,snake_case_ ,snake_case_ ):
sari_score += SARIsent(normalize(snake_case_ ) ,normalize(snake_case_ ) ,[normalize(snake_case_ ) for sent in refs] )
UpperCamelCase : List[Any] = sari_score / len(snake_case_ )
return 1_0_0 * sari_score
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : List[Any] ,snake_case_ : List[Any]="exp" ,snake_case_ : Dict=None ,snake_case_ : int=False ,snake_case_ : List[str]=False ,snake_case_ : Optional[Any]=False ,):
'''simple docstring'''
UpperCamelCase : Dict = len(references[0] )
if any(len(snake_case_ ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
UpperCamelCase : Dict = [[refs[i] for refs in references] for i in range(snake_case_ )]
UpperCamelCase : Optional[int] = sacrebleu.corpus_bleu(
snake_case_ ,snake_case_ ,smooth_method=snake_case_ ,smooth_value=snake_case_ ,force=snake_case_ ,lowercase=snake_case_ ,use_effective_order=snake_case_ ,)
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase ( datasets.Metric ):
def a_ ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=[
"""https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
"""https://github.com/cocoxu/simplification/blob/master/SARI.py""",
"""https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
"""https://github.com/mjpost/sacreBLEU""",
] , reference_urls=[
"""https://www.aclweb.org/anthology/Q16-1029.pdf""",
"""https://github.com/mjpost/sacreBLEU""",
"""https://en.wikipedia.org/wiki/BLEU""",
"""https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
] , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = {}
result.update({"""sari""": compute_sari(sources=SCREAMING_SNAKE_CASE_ , predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ )} )
result.update({"""sacrebleu""": compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ )} )
result.update({"""exact""": compute_em(predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ )} )
return result
| 27
|
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27
| 1
|
"""simple docstring"""
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__A : Optional[int] = {
'''cola''': 2,
'''mnli''': 3,
'''mrpc''': 2,
'''sst-2''': 2,
'''sts-b''': 1,
'''qqp''': 2,
'''qnli''': 2,
'''rte''': 2,
'''wnli''': 2,
}
logging.set_verbosity_info()
def A_ ( snake_case_ : str ,snake_case_ : str ,snake_case_ : Any ,snake_case_ : Dict=None ):
'''simple docstring'''
# Initialise PyTorch model
UpperCamelCase : Union[str, Any] = XLNetConfig.from_json_file(snake_case_ )
UpperCamelCase : Dict = finetuning_task.lower() if finetuning_task is not None else """"""
if finetuning_task in GLUE_TASKS_NUM_LABELS:
print(f'Building PyTorch XLNetForSequenceClassification model from configuration: {config}' )
UpperCamelCase : Union[str, Any] = finetuning_task
UpperCamelCase : List[str] = GLUE_TASKS_NUM_LABELS[finetuning_task]
UpperCamelCase : Optional[Any] = XLNetForSequenceClassification(snake_case_ )
elif "squad" in finetuning_task:
UpperCamelCase : Dict = finetuning_task
UpperCamelCase : List[str] = XLNetForQuestionAnswering(snake_case_ )
else:
UpperCamelCase : int = XLNetLMHeadModel(snake_case_ )
# Load weights from tf checkpoint
load_tf_weights_in_xlnet(snake_case_ ,snake_case_ ,snake_case_ )
# Save pytorch-model
UpperCamelCase : List[str] = os.path.join(snake_case_ ,snake_case_ )
UpperCamelCase : int = os.path.join(snake_case_ ,snake_case_ )
print(f'Save PyTorch model to {os.path.abspath(snake_case_ )}' )
torch.save(model.state_dict() ,snake_case_ )
print(f'Save configuration file to {os.path.abspath(snake_case_ )}' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--xlnet_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained XLNet model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--finetuning_task''',
default=None,
type=str,
help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
)
__A : Any = parser.parse_args()
print(args)
convert_xlnet_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
)
| 27
|
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27
| 1
|
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
__A : Optional[int] = sys.version_info >= (3, 10)
def A_ ( snake_case_ : Optional[Any]=None ,snake_case_ : List[Any]=None ):
'''simple docstring'''
return field(default_factory=lambda: default ,metadata=snake_case_ )
@dataclass
class lowerCamelCase :
lowercase : int
lowercase : float
lowercase : str
lowercase : bool
@dataclass
class lowerCamelCase :
lowercase : int = 4_2
lowercase : str = field(default='toto' , metadata={'help': 'help message'} )
@dataclass
class lowerCamelCase :
lowercase : bool = False
lowercase : bool = True
lowercase : Optional[bool] = None
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Any = 'titi'
lowercase : Optional[Any] = 'toto'
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Any = 'titi'
lowercase : Optional[Any] = 'toto'
lowercase : int = 4_2
@dataclass
class lowerCamelCase :
lowercase : BasicEnum = "toto"
def a_ ( self ):
UpperCamelCase : str = BasicEnum(self.foo )
@dataclass
class lowerCamelCase :
lowercase : MixedTypeEnum = "toto"
def a_ ( self ):
UpperCamelCase : Dict = MixedTypeEnum(self.foo )
@dataclass
class lowerCamelCase :
lowercase : Optional[int] = None
lowercase : Optional[float] = field(default=_UpperCAmelCase , metadata={'help': 'help message'} )
lowercase : Optional[str] = None
lowercase : Optional[List[str]] = list_field(default=[] )
lowercase : Optional[List[int]] = list_field(default=[] )
@dataclass
class lowerCamelCase :
lowercase : List[int] = list_field(default=[] )
lowercase : List[int] = list_field(default=[1, 2, 3] )
lowercase : List[str] = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
lowercase : List[float] = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class lowerCamelCase :
lowercase : List[int] = field()
lowercase : str = field()
lowercase : BasicEnum = field()
def a_ ( self ):
UpperCamelCase : Dict = BasicEnum(self.required_enum )
@dataclass
class lowerCamelCase :
lowercase : int
lowercase : "BasicEnum" = field()
lowercase : "Optional[bool]" = None
lowercase : "str" = field(default='toto' , metadata={'help': 'help message'} )
lowercase : "List[str]" = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class lowerCamelCase :
lowercase : bool = False
lowercase : bool = True
lowercase : bool | None = None
@dataclass
class lowerCamelCase :
lowercase : int | None = None
lowercase : float | None = field(default=_UpperCAmelCase , metadata={'help': 'help message'} )
lowercase : str | None = None
lowercase : list[str] | None = list_field(default=[] )
lowercase : list[int] | None = list_field(default=[] )
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
UpperCamelCase : List[str] = {k: v for k, v in vars(SCREAMING_SNAKE_CASE_ ).items() if k != """container"""}
UpperCamelCase : int = {k: v for k, v in vars(SCREAMING_SNAKE_CASE_ ).items() if k != """container"""}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get("""choices""" , SCREAMING_SNAKE_CASE_ ) and yy.get("""choices""" , SCREAMING_SNAKE_CASE_ ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx["""type"""](SCREAMING_SNAKE_CASE_ ) , yy["""type"""](SCREAMING_SNAKE_CASE_ ) )
del xx["type"], yy["type"]
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--bar""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--baz""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--flag""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , const=SCREAMING_SNAKE_CASE_ , nargs="""?""" )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = ["""--foo""", """1""", """--baz""", """quux""", """--bar""", """0.5"""]
((UpperCamelCase) , ) : Optional[Any] = parser.parse_args_into_dataclasses(SCREAMING_SNAKE_CASE_ , look_for_args_file=SCREAMING_SNAKE_CASE_ )
self.assertFalse(example.flag )
def a_ ( self ):
UpperCamelCase : Tuple = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=42 , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--baz""" , default="""toto""" , type=SCREAMING_SNAKE_CASE_ , help="""help message""" )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , const=SCREAMING_SNAKE_CASE_ , nargs="""?""" )
expected.add_argument("""--baz""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , const=SCREAMING_SNAKE_CASE_ , nargs="""?""" )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument("""--no_baz""" , action="""store_false""" , default=SCREAMING_SNAKE_CASE_ , dest="""baz""" )
expected.add_argument("""--opt""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(SCREAMING_SNAKE_CASE_ )
for dataclass_type in dataclass_types:
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = parser.parse_args([] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Tuple = parser.parse_args(["""--foo""", """--no_baz"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[int] = parser.parse_args(["""--foo""", """--baz"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Union[str, Any] = parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[int] = parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , opt=SCREAMING_SNAKE_CASE_ ) )
def a_ ( self ):
UpperCamelCase : Optional[int] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=["""titi""", """toto""", 42] , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
UpperCamelCase : Optional[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
UpperCamelCase : int = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
UpperCamelCase : Optional[int] = parser.parse_args_into_dataclasses(["""--foo""", """titi"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
UpperCamelCase : str = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
UpperCamelCase : Optional[int] = parser.parse_args_into_dataclasses(["""--foo""", """42"""] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def a_ ( self ):
@dataclass
class lowerCamelCase :
lowercase : Literal["titi", "toto", 4_2] = "toto"
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = argparse.ArgumentParser()
expected.add_argument(
"""--foo""" , default="""toto""" , choices=("""titi""", """toto""", 42) , type=make_choice_type_function(["""titi""", """toto""", 42] ) , )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , """toto""" )
UpperCamelCase : Optional[int] = parser.parse_args(["""--foo""", """titi"""] )
self.assertEqual(args.foo , """titi""" )
UpperCamelCase : int = parser.parse_args(["""--foo""", """42"""] )
self.assertEqual(args.foo , 42 )
def a_ ( self ):
UpperCamelCase : str = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo_int""" , nargs="""+""" , default=[] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--bar_int""" , nargs="""+""" , default=[1, 2, 3] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--foo_float""" , nargs="""+""" , default=[0.1, 0.2, 0.3] , type=SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = parser.parse_args([] )
self.assertEqual(
SCREAMING_SNAKE_CASE_ , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=["""Hallo""", """Bonjour""", """Hello"""] , foo_float=[0.1, 0.2, 0.3] ) , )
UpperCamelCase : Optional[Any] = parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split() )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=["""a""", """b""", """c"""] , foo_float=[0.1, 0.7] ) )
def a_ ( self ):
UpperCamelCase : Dict = argparse.ArgumentParser()
expected.add_argument("""--foo""" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--bar""" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="""help message""" )
expected.add_argument("""--baz""" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--ces""" , nargs="""+""" , default=[] , type=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--des""" , nargs="""+""" , default=[] , type=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(SCREAMING_SNAKE_CASE_ )
for dataclass_type in dataclass_types:
UpperCamelCase : List[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = parser.parse_args([] )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=SCREAMING_SNAKE_CASE_ , bar=SCREAMING_SNAKE_CASE_ , baz=SCREAMING_SNAKE_CASE_ , ces=[] , des=[] ) )
UpperCamelCase : int = parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split() )
self.assertEqual(SCREAMING_SNAKE_CASE_ , Namespace(foo=12 , bar=3.14 , baz="""42""" , ces=["""a""", """b""", """c"""] , des=[1, 2, 3] ) )
def a_ ( self ):
UpperCamelCase : List[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = argparse.ArgumentParser()
expected.add_argument("""--required_list""" , nargs="""+""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--required_str""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=SCREAMING_SNAKE_CASE_ , )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = argparse.ArgumentParser()
expected.add_argument("""--foo""" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ )
expected.add_argument(
"""--required_enum""" , type=make_choice_type_function(["""titi""", """toto"""] ) , choices=["""titi""", """toto"""] , required=SCREAMING_SNAKE_CASE_ , )
expected.add_argument("""--opt""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ )
expected.add_argument("""--baz""" , default="""toto""" , type=SCREAMING_SNAKE_CASE_ , help="""help message""" )
expected.add_argument("""--foo_str""" , nargs="""+""" , default=["""Hallo""", """Bonjour""", """Hello"""] , type=SCREAMING_SNAKE_CASE_ )
self.argparsersEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
UpperCamelCase : Optional[Any] = parser.parse_dict(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[int] = BasicExample(**SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
"""extra""": 42,
}
self.assertRaises(SCREAMING_SNAKE_CASE_ , parser.parse_dict , SCREAMING_SNAKE_CASE_ , allow_extra_keys=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[Any] = os.path.join(SCREAMING_SNAKE_CASE_ , """temp_json""" )
os.mkdir(SCREAMING_SNAKE_CASE_ )
with open(temp_local_path + """.json""" , """w+""" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = parser.parse_yaml_file(Path(temp_local_path + """.json""" ) )[0]
UpperCamelCase : Optional[Any] = BasicExample(**SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = {
"""foo""": 12,
"""bar""": 3.14,
"""baz""": """42""",
"""flag""": True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[str] = os.path.join(SCREAMING_SNAKE_CASE_ , """temp_yaml""" )
os.mkdir(SCREAMING_SNAKE_CASE_ )
with open(temp_local_path + """.yaml""" , """w+""" ) as f:
yaml.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = parser.parse_yaml_file(Path(temp_local_path + """.yaml""" ) )[0]
UpperCamelCase : str = BasicExample(**SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__A : Optional[int] = get_logger(__name__)
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Any = (
os.path.join(SCREAMING_SNAKE_CASE_ , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
UpperCamelCase : List[Any] = Extractor
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
UpperCamelCase : Tuple = os.path.abspath(SCREAMING_SNAKE_CASE_ )
return os.path.join(self.extract_dir , hash_url_to_filename(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return force_extract or (
not os.path.isfile(SCREAMING_SNAKE_CASE_ ) and not (os.path.isdir(SCREAMING_SNAKE_CASE_ ) and os.listdir(SCREAMING_SNAKE_CASE_ ))
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False ):
UpperCamelCase : Any = self.extractor.infer_extractor_format(SCREAMING_SNAKE_CASE_ )
if not extractor_format:
return input_path
UpperCamelCase : Any = self._get_output_path(SCREAMING_SNAKE_CASE_ )
if self._do_extract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.extractor.extract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return output_path
class lowerCamelCase ( _UpperCAmelCase ):
@classmethod
@abstractmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
...
@staticmethod
@abstractmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
...
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase ):
lowercase : List[bytes] = []
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
with open(SCREAMING_SNAKE_CASE_ , """rb""" ) as f:
return f.read(SCREAMING_SNAKE_CASE_ )
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = b"" ):
if not magic_number:
UpperCamelCase : Tuple = max(len(SCREAMING_SNAKE_CASE_ ) for cls_magic_number in cls.magic_numbers )
try:
UpperCamelCase : List[Any] = cls.read_magic_number(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
except OSError:
return False
return any(magic_number.startswith(SCREAMING_SNAKE_CASE_ ) for cls_magic_number in cls.magic_numbers )
class lowerCamelCase ( _UpperCAmelCase ):
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return tarfile.is_tarfile(SCREAMING_SNAKE_CASE_ )
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
def resolved(SCREAMING_SNAKE_CASE_ ) -> str:
return os.path.realpath(os.path.abspath(SCREAMING_SNAKE_CASE_ ) )
def badpath(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ).startswith(SCREAMING_SNAKE_CASE_ )
def badlink(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
# Links are interpreted relative to the directory containing the link
UpperCamelCase : Any = resolved(os.path.join(SCREAMING_SNAKE_CASE_ , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = resolved(SCREAMING_SNAKE_CASE_ )
for finfo in members:
if badpath(finfo.name , SCREAMING_SNAKE_CASE_ ):
logger.error(f'Extraction of {finfo.name} is blocked (illegal path)' )
elif finfo.issym() and badlink(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
logger.error(f'Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}' )
elif finfo.islnk() and badlink(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
logger.error(f'Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}' )
else:
yield finfo
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = tarfile.open(SCREAMING_SNAKE_CASE_ )
tar_file.extractall(SCREAMING_SNAKE_CASE_ , members=TarExtractor.safemembers(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
tar_file.close()
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[Any] = [b'\x1F\x8B']
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
with gzip.open(SCREAMING_SNAKE_CASE_ , """rb""" ) as gzip_file:
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Tuple = [
b'PK\x03\x04',
b'PK\x05\x06', # empty archive
b'PK\x07\x08', # spanned archive
]
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = b"" ):
if super().is_extractable(SCREAMING_SNAKE_CASE_ , magic_number=SCREAMING_SNAKE_CASE_ ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(SCREAMING_SNAKE_CASE_ , """rb""" ) as fp:
UpperCamelCase : Tuple = _EndRecData(SCREAMING_SNAKE_CASE_ )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
UpperCamelCase : Tuple = fp.read(SCREAMING_SNAKE_CASE_ ) # CD is where we expect it to be
if len(SCREAMING_SNAKE_CASE_ ) == sizeCentralDir:
UpperCamelCase : int = struct.unpack(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """r""" ) as zip_file:
zip_file.extractall(SCREAMING_SNAKE_CASE_ )
zip_file.close()
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[Any] = [b'\xFD\x37\x7A\x58\x5A\x00']
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
with lzma.open(SCREAMING_SNAKE_CASE_ ) as compressed_file:
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[str] = [b'Rar!\x1a\x07\x00', b'Rar!\x1a\x07\x01\x00'] # RAR_ID # RAR5_ID
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not config.RARFILE_AVAILABLE:
raise ImportError("""Please pip install rarfile""" )
import rarfile
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = rarfile.RarFile(SCREAMING_SNAKE_CASE_ )
rf.extractall(SCREAMING_SNAKE_CASE_ )
rf.close()
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[str] = [b'\x28\xb5\x2F\xFD']
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("""Please pip install zstandard""" )
import zstandard as zstd
UpperCamelCase : List[str] = zstd.ZstdDecompressor()
with open(SCREAMING_SNAKE_CASE_ , """rb""" ) as ifh, open(SCREAMING_SNAKE_CASE_ , """wb""" ) as ofh:
dctx.copy_stream(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Any = [b'\x42\x5A\x68']
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
with bza.open(SCREAMING_SNAKE_CASE_ , """rb""" ) as compressed_file:
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = [b'\x37\x7A\xBC\xAF\x27\x1C']
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("""Please pip install py7zr""" )
import pyazr
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with pyazr.SevenZipFile(SCREAMING_SNAKE_CASE_ , """r""" ) as archive:
archive.extractall(SCREAMING_SNAKE_CASE_ )
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = [b'\x04\x22\x4D\x18']
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not config.LZ4_AVAILABLE:
raise ImportError("""Please pip install lz4""" )
import lza.frame
with lza.frame.open(SCREAMING_SNAKE_CASE_ , """rb""" ) as compressed_file:
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as extracted_file:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class lowerCamelCase :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
lowercase : Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def a_ ( cls ):
return max(
len(SCREAMING_SNAKE_CASE_ )
for extractor in cls.extractors.values()
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def a_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
try:
return MagicNumberBaseExtractor.read_magic_number(SCREAMING_SNAKE_CASE_ , magic_number_length=SCREAMING_SNAKE_CASE_ )
except OSError:
return b""
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False ):
warnings.warn(
"""Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'infer_extractor_format' instead.""" , category=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = cls.infer_extractor_format(SCREAMING_SNAKE_CASE_ )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ ): # <Added version="2.4.0"/>
UpperCamelCase : Union[str, Any] = cls._get_magic_number_max_length()
UpperCamelCase : str = cls._read_magic_number(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(SCREAMING_SNAKE_CASE_ , magic_number=SCREAMING_SNAKE_CASE_ ):
return extractor_format
@classmethod
def a_ ( cls , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "deprecated" , ):
os.makedirs(os.path.dirname(SCREAMING_SNAKE_CASE_ ) , exist_ok=SCREAMING_SNAKE_CASE_ )
# Prevent parallel extractions
UpperCamelCase : List[Any] = str(Path(SCREAMING_SNAKE_CASE_ ).with_suffix(""".lock""" ) )
with FileLock(SCREAMING_SNAKE_CASE_ ):
shutil.rmtree(SCREAMING_SNAKE_CASE_ , ignore_errors=SCREAMING_SNAKE_CASE_ )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): # passed as positional arg
warnings.warn(
"""Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'extractor_format' instead.""" , category=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Union[str, Any] = extractor if extractor != """deprecated""" else extractor_format
else:
UpperCamelCase : str = cls.extractors[extractor_format]
return extractor.extract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
warnings.warn(
"""Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an """
"""exception in 3.0.0.""" , category=SCREAMING_SNAKE_CASE_ , )
for extractor in cls.extractors.values():
if extractor.is_extractable(SCREAMING_SNAKE_CASE_ ):
return extractor.extract(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27
| 1
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowerCamelCase :
lowercase : str = field(
metadata={'help': 'The output directory where the model will be written.'} , )
lowercase : str = field(
metadata={
'help': (
'The encoder model checkpoint for weights initialization.'
'Don\'t set if you want to train an encoder model from scratch.'
)
} , )
lowercase : str = field(
metadata={
'help': (
'The decoder model checkpoint for weights initialization.'
'Don\'t set if you want to train a decoder model from scratch.'
)
} , )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'} )
lowercase : Optional[str] = field(
default=_UpperCAmelCase , metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'} )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[int] = HfArgumentParser((ModelArguments,) )
((UpperCamelCase) , ) : List[str] = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
UpperCamelCase : Dict = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
UpperCamelCase : Any = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
UpperCamelCase : List[str] = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
UpperCamelCase : str = True
UpperCamelCase : str = True
UpperCamelCase : List[Any] = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path ,decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path ,encoder_config=snake_case_ ,decoder_config=snake_case_ ,)
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
UpperCamelCase : int = decoder_config.decoder_start_token_id
UpperCamelCase : Optional[int] = decoder_config.pad_token_id
if decoder_start_token_id is None:
UpperCamelCase : Any = decoder_config.bos_token_id
if pad_token_id is None:
UpperCamelCase : List[str] = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
UpperCamelCase : int = decoder_config.eos_token_id
UpperCamelCase : Tuple = decoder_start_token_id
UpperCamelCase : Union[str, Any] = pad_token_id
UpperCamelCase : Union[str, Any] = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
UpperCamelCase : Dict = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
UpperCamelCase : List[str] = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27
| 1
|
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : Optional[Any] = """laion/clap-htsat-unfused"""
UpperCamelCase : Tuple = tempfile.mkdtemp()
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return RobertaTokenizer.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return ClapFeatureExtractor.from_pretrained(self.checkpoint , **SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
shutil.rmtree(self.tmpdirname )
def a_ ( self ):
UpperCamelCase : Any = self.get_tokenizer()
UpperCamelCase : Any = self.get_feature_extractor()
UpperCamelCase : List[Any] = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase : Optional[Any] = ClapProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = ClapProcessor(tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase : Union[str, Any] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
UpperCamelCase : Any = self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
UpperCamelCase : str = ClapProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=SCREAMING_SNAKE_CASE_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , SCREAMING_SNAKE_CASE_ )
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.feature_extractor , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.get_feature_extractor()
UpperCamelCase : int = self.get_tokenizer()
UpperCamelCase : Optional[int] = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = floats_list((3, 1000) )
UpperCamelCase : Optional[Any] = feature_extractor(SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
UpperCamelCase : Union[str, Any] = processor(audios=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def a_ ( self ):
UpperCamelCase : List[Any] = self.get_feature_extractor()
UpperCamelCase : Optional[int] = self.get_tokenizer()
UpperCamelCase : int = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = """This is a test string"""
UpperCamelCase : Dict = processor(text=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = tokenizer(SCREAMING_SNAKE_CASE_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.get_feature_extractor()
UpperCamelCase : List[Any] = self.get_tokenizer()
UpperCamelCase : Any = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
UpperCamelCase : List[str] = processor.batch_decode(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.get_feature_extractor()
UpperCamelCase : Dict = self.get_tokenizer()
UpperCamelCase : Optional[int] = ClapProcessor(tokenizer=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
processor.model_input_names[2:] , feature_extractor.model_input_names , msg="""`processor` and `feature_extractor` model input names do not match""" , )
| 27
|
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27
| 1
|
"""simple docstring"""
from string import ascii_uppercase
__A : str = {str(ord(c) - 55): c for c in ascii_uppercase}
def A_ ( snake_case_ : int ,snake_case_ : int ):
'''simple docstring'''
if isinstance(snake_case_ ,snake_case_ ):
raise TypeError("""int() can't convert non-string with explicit base""" )
if num < 0:
raise ValueError("""parameter must be positive int""" )
if isinstance(snake_case_ ,snake_case_ ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if isinstance(snake_case_ ,snake_case_ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if base in (0, 1):
raise ValueError("""base must be >= 2""" )
if base > 3_6:
raise ValueError("""base must be <= 36""" )
UpperCamelCase : Union[str, Any] = """"""
UpperCamelCase : int = 0
UpperCamelCase : Union[str, Any] = 0
while div != 1:
UpperCamelCase , UpperCamelCase : Union[str, Any] = divmod(snake_case_ ,snake_case_ )
if base >= 1_1 and 9 < mod < 3_6:
UpperCamelCase : int = ALPHABET_VALUES[str(snake_case_ )]
else:
UpperCamelCase : str = str(snake_case_ )
new_value += actual_value
UpperCamelCase : Tuple = num // base
UpperCamelCase : Optional[int] = div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(snake_case_ )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 37):
for num in range(1000):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 27
|
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27
| 1
|
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
__A : int = '''bart'''
__A : List[str] = True
@st.cache(allow_output_mutation=snake_case_ )
def A_ ( ):
'''simple docstring'''
if LOAD_DENSE_INDEX:
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased""" )
UpperCamelCase : str = AutoModel.from_pretrained("""yjernite/retribert-base-uncased""" ).to("""cuda:0""" )
UpperCamelCase : Union[str, Any] = qar_model.eval()
else:
UpperCamelCase , UpperCamelCase : Optional[Any] = (None, None)
if MODEL_TYPE == "bart":
UpperCamelCase : Optional[int] = AutoTokenizer.from_pretrained("""yjernite/bart_eli5""" )
UpperCamelCase : Tuple = AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5""" ).to("""cuda:0""" )
UpperCamelCase : Optional[Any] = torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth""" )
sas_model.load_state_dict(save_dict["""model"""] )
UpperCamelCase : Optional[Any] = sas_model.eval()
else:
UpperCamelCase , UpperCamelCase : List[Any] = make_qa_sas_model(
model_name="""t5-small""" ,from_file="""seq2seq_models/eli5_t5_model_1024_4.pth""" ,device="""cuda:0""" )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=snake_case_ )
def A_ ( ):
'''simple docstring'''
if LOAD_DENSE_INDEX:
UpperCamelCase : Optional[Any] = faiss.StandardGpuResources()
UpperCamelCase : str = datasets.load_dataset(path="""wiki_snippets""" ,name="""wiki40b_en_100_0""" )["""train"""]
UpperCamelCase : Optional[int] = np.memmap(
"""wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat""" ,dtype="""float32""" ,mode="""r""" ,shape=(wikiaab_passages.num_rows, 1_2_8) ,)
UpperCamelCase : Optional[Any] = faiss.IndexFlatIP(1_2_8 )
UpperCamelCase : Optional[Any] = faiss.index_cpu_to_gpu(snake_case_ ,1 ,snake_case_ )
wikiaab_gpu_index_flat.add(snake_case_ ) # TODO fix for larger GPU
else:
UpperCamelCase , UpperCamelCase : List[Any] = (None, None)
UpperCamelCase : Dict = Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Tuple = datasets.load_dataset("""eli5""" ,name="""LFQA_reddit""" )
UpperCamelCase : List[str] = elia["""train_eli5"""]
UpperCamelCase : int = np.memmap(
"""eli5_questions_reps.dat""" ,dtype="""float32""" ,mode="""r""" ,shape=(elia_train.num_rows, 1_2_8) )
UpperCamelCase : Tuple = faiss.IndexFlatIP(1_2_8 )
eli5_train_q_index.add(snake_case_ )
return (elia_train, eli5_train_q_index)
__A , __A , __A : List[str] = load_indexes()
__A , __A , __A , __A : int = load_models()
__A , __A : Dict = load_train_data()
def A_ ( snake_case_ : Optional[int] ,snake_case_ : List[str]=1_0 ):
'''simple docstring'''
UpperCamelCase : str = embed_questions_for_retrieval([question] ,snake_case_ ,snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = eli5_train_q_index.search(snake_case_ ,snake_case_ )
UpperCamelCase : Tuple = [elia_train[int(snake_case_ )] for i in I[0]]
return nn_examples
def A_ ( snake_case_ : List[str] ,snake_case_ : Optional[Any]="wiki40b" ,snake_case_ : Optional[int]="dense" ,snake_case_ : Optional[int]=1_0 ):
'''simple docstring'''
if source == "none":
UpperCamelCase , UpperCamelCase : Any = (""" <P> """.join(["""""" for _ in range(1_1 )] ).strip(), [])
else:
if method == "dense":
UpperCamelCase , UpperCamelCase : List[Any] = query_qa_dense_index(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
else:
UpperCamelCase , UpperCamelCase : Tuple = query_es_index(
snake_case_ ,snake_case_ ,index_name="""english_wiki40b_snippets_100w""" ,n_results=snake_case_ ,)
UpperCamelCase : Any = [
(res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
]
UpperCamelCase : List[str] = """question: {} context: {}""".format(snake_case_ ,snake_case_ )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda snake_case_ : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda snake_case_ : None),
} )
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Optional[int] ,snake_case_ : Any ,snake_case_ : Any=6_4 ,snake_case_ : List[Any]=2_5_6 ,snake_case_ : int=False ,snake_case_ : List[Any]=2 ,snake_case_ : List[Any]=0.95 ,snake_case_ : int=0.8 ):
'''simple docstring'''
with torch.no_grad():
UpperCamelCase : Optional[int] = qa_sas_generate(
snake_case_ ,snake_case_ ,snake_case_ ,num_answers=1 ,num_beams=snake_case_ ,min_len=snake_case_ ,max_len=snake_case_ ,do_sample=snake_case_ ,temp=snake_case_ ,top_p=snake_case_ ,top_k=snake_case_ ,max_input_length=1_0_2_4 ,device="""cuda:0""" ,)[0]
return (answer, support_list)
st.title('''Long Form Question Answering with ELI5''')
# Start sidebar
__A : Union[str, Any] = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__A : List[str] = '''
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class="img-container"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
''' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
__A : List[str] = '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)
__A : str = [
'''Answer the question''',
'''View the retrieved document only''',
'''View the most similar ELI5 question and answer''',
'''Show me everything, please!''',
]
__A : List[str] = st.sidebar.checkbox('''Demo options''')
if demo_options:
__A : str = st.sidebar.selectbox(
'''''',
action_list,
index=3,
)
__A : Union[str, Any] = action_list.index(action_st)
__A : Optional[int] = st.sidebar.selectbox(
'''''',
['''Show full text of passages''', '''Show passage section titles'''],
index=0,
)
__A : Optional[int] = show_type == '''Show full text of passages'''
else:
__A : Optional[int] = 3
__A : int = True
__A : Optional[Any] = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
__A : Optional[Any] = '''
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
'''
st.sidebar.markdown(retriever_info)
__A : Optional[Any] = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
__A : Optional[int] = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
__A : Union[str, Any] = '''wiki40b'''
__A : int = '''dense'''
__A : List[str] = '''beam'''
__A : int = 2
__A : List[Any] = 64
__A : Optional[Any] = 256
__A : Any = None
__A : str = None
__A : Any = st.sidebar.checkbox('''Generation options''')
if generate_options:
__A : List[Any] = '''
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder\'s output probabilities.
'''
st.sidebar.markdown(generate_info)
__A : str = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
__A : Optional[int] = st.sidebar.slider(
'''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
__A : Optional[int] = st.sidebar.slider(
'''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
__A : Tuple = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
__A : Optional[int] = st.sidebar.slider(
'''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
__A : Tuple = st.sidebar.slider(
'''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
__A : int = None
# start main text
__A : List[str] = [
'''<MY QUESTION>''',
'''How do people make chocolate?''',
'''Why do we get a fever when we are sick?''',
'''How can different animals perceive different colors?''',
'''What is natural language processing?''',
'''What\'s the best way to treat a sunburn?''',
'''What exactly are vitamins ?''',
'''How does nuclear energy provide electricity?''',
'''What\'s the difference between viruses and bacteria?''',
'''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
'''Why do people like drinking coffee even though it tastes so bad?''',
'''What happens when wine ages? How does it make the wine taste better?''',
'''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
'''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
'''How does New Zealand have so many large bird predators?''',
]
__A : Optional[int] = st.selectbox(
'''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
__A : List[str] = st.text_input('''Enter your question here:''', '''''')
else:
__A : str = question_s
if st.button('''Show me!'''):
if action in [0, 1, 3]:
if index_type == "mixed":
__A , __A : Union[str, Any] = make_support(question, source=wiki_source, method='''dense''', n_results=10)
__A , __A : str = make_support(question, source=wiki_source, method='''sparse''', n_results=10)
__A : Tuple = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
__A : Any = support_list[:10]
__A : Union[str, Any] = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
else:
__A , __A : Any = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
__A , __A : Dict = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == '''sampled'''),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('''### The model generated answer is:''')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
for i, res in enumerate(support_list):
__A : Union[str, Any] = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
__A : str = res[1].strip()
if sec_titles == "":
__A : List[str] = '''[{}]({})'''.format(res[0], wiki_url)
else:
__A : Any = sec_titles.split(''' & ''')
__A : Any = ''' & '''.join(
['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
)
st.markdown(
'''{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'''.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
)
if action in [2, 3]:
__A : Any = find_nearest_training(question)
__A : Optional[Any] = nn_train_list[0]
st.markdown(
'''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
)
__A : Union[str, Any] = [
'''{}. {}'''.format(i + 1, ''' \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
if i == 0 or sc > 2
]
st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))
__A : int = '''
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 27
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = parent
UpperCamelCase : Union[str, Any] = 13
UpperCamelCase : Optional[int] = 7
UpperCamelCase : List[str] = 30
UpperCamelCase : Optional[Any] = self.seq_length + self.mem_len
UpperCamelCase : Dict = 15
UpperCamelCase : List[str] = True
UpperCamelCase : str = True
UpperCamelCase : int = 99
UpperCamelCase : List[Any] = [10, 50, 80]
UpperCamelCase : Union[str, Any] = 32
UpperCamelCase : str = 32
UpperCamelCase : List[str] = 4
UpperCamelCase : Tuple = 8
UpperCamelCase : str = 128
UpperCamelCase : Any = 2
UpperCamelCase : int = 2
UpperCamelCase : str = None
UpperCamelCase : Union[str, Any] = 1
UpperCamelCase : Tuple = 0
UpperCamelCase : Union[str, Any] = 3
UpperCamelCase : Optional[Any] = self.vocab_size - 1
UpperCamelCase : List[Any] = 0.01
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : List[str] = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def a_ ( self ):
random.seed(self.seed )
tf.random.set_seed(self.seed )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = TFTransfoXLModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ ).to_tuple()
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids_a, """mems""": mems_a}
UpperCamelCase , UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : int = TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ ).to_tuple()
UpperCamelCase : Tuple = {"""input_ids""": input_ids_a, """labels""": lm_labels}
UpperCamelCase , UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ ).to_tuple()
UpperCamelCase , UpperCamelCase : Any = model([input_ids_a, mems_a] ).to_tuple()
UpperCamelCase : Tuple = {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}
UpperCamelCase , UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self ):
UpperCamelCase : Any = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : int = config_and_inputs
UpperCamelCase : int = {"""input_ids""": input_ids_a}
return config, inputs_dict
@require_tf
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : List[str] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
lowercase : List[Any] = () if is_tf_available() else ()
lowercase : List[str] = (
{
'feature-extraction': TFTransfoXLModel,
'text-classification': TFTransfoXLForSequenceClassification,
'text-generation': TFTransfoXLLMHeadModel,
'zero-shot': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
lowercase : List[str] = False
lowercase : Optional[Any] = False
lowercase : Optional[Any] = False
lowercase : Dict = False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def a_ ( self ):
UpperCamelCase : Optional[int] = TFTransfoXLModelTester(self )
UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , d_embed=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
self.model_tester.set_seed()
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self.model_tester.set_seed()
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase , UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase : List[str] = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
UpperCamelCase : List[Any] = model_class(SCREAMING_SNAKE_CASE_ )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
UpperCamelCase : Tuple = model.get_output_embeddings()
assert isinstance(SCREAMING_SNAKE_CASE_ , tf.keras.layers.Layer )
UpperCamelCase : List[Any] = model.get_bias()
assert name is None
else:
UpperCamelCase : Optional[Any] = model.get_output_embeddings()
assert x is None
UpperCamelCase : Tuple = model.get_bias()
assert name is None
def a_ ( self ):
# TODO JP: Make TransfoXL XLA compliant
pass
@slow
def a_ ( self ):
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""" )
def a_ ( self ):
pass
@require_tf
class lowerCamelCase ( unittest.TestCase ):
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def a_ ( self ):
UpperCamelCase : int = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
UpperCamelCase : Optional[Any] = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
UpperCamelCase : List[Any] = [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
UpperCamelCase : List[Any] = model.generate(SCREAMING_SNAKE_CASE_ , max_length=200 , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
| 1
|
"""simple docstring"""
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(snake_case_ : int ,snake_case_ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
UpperCamelCase : Tuple = update_area_of_max_square(snake_case_ ,col + 1 )
UpperCamelCase : Optional[int] = update_area_of_max_square(row + 1 ,col + 1 )
UpperCamelCase : List[str] = update_area_of_max_square(row + 1 ,snake_case_ )
if mat[row][col]:
UpperCamelCase : Dict = 1 + min([right, diagonal, down] )
UpperCamelCase : int = max(largest_square_area[0] ,snake_case_ )
return sub_problem_sol
else:
return 0
UpperCamelCase : Dict = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square_using_dp_array(
snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
UpperCamelCase : Dict = update_area_of_max_square_using_dp_array(snake_case_ ,col + 1 ,snake_case_ )
UpperCamelCase : Union[str, Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,snake_case_ )
UpperCamelCase : Dict = update_area_of_max_square_using_dp_array(row + 1 ,snake_case_ ,snake_case_ )
if mat[row][col]:
UpperCamelCase : Optional[Any] = 1 + min([right, diagonal, down] )
UpperCamelCase : List[str] = max(largest_square_area[0] ,snake_case_ )
UpperCamelCase : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
UpperCamelCase : Tuple = [0]
UpperCamelCase : str = [[-1] * cols for _ in range(snake_case_ )]
update_area_of_max_square_using_dp_array(0 ,0 ,snake_case_ )
return largest_square_area[0]
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
UpperCamelCase : Any = [[0] * (cols + 1) for _ in range(rows + 1 )]
UpperCamelCase : Union[str, Any] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
UpperCamelCase : int = dp_array[row][col + 1]
UpperCamelCase : Union[str, Any] = dp_array[row + 1][col + 1]
UpperCamelCase : List[str] = dp_array[row + 1][col]
if mat[row][col] == 1:
UpperCamelCase : List[Any] = 1 + min(snake_case_ ,snake_case_ ,snake_case_ )
UpperCamelCase : Optional[int] = max(dp_array[row][col] ,snake_case_ )
else:
UpperCamelCase : Optional[int] = 0
return largest_square_area
def A_ ( snake_case_ : int ,snake_case_ : int ,snake_case_ : list[list[int]] ):
'''simple docstring'''
UpperCamelCase : Dict = [0] * (cols + 1)
UpperCamelCase : Union[str, Any] = [0] * (cols + 1)
UpperCamelCase : Tuple = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
UpperCamelCase : int = current_row[col + 1]
UpperCamelCase : int = next_row[col + 1]
UpperCamelCase : Union[str, Any] = next_row[col]
if mat[row][col] == 1:
UpperCamelCase : Dict = 1 + min(snake_case_ ,snake_case_ ,snake_case_ )
UpperCamelCase : List[str] = max(current_row[col] ,snake_case_ )
else:
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 27
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=_UpperCAmelCase ):
lowercase : List[str] = ['flax', 'transformers']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
class lowerCamelCase ( metaclass=_UpperCAmelCase ):
lowercase : List[Any] = ['flax', 'transformers']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
class lowerCamelCase ( metaclass=_UpperCAmelCase ):
lowercase : List[str] = ['flax', 'transformers']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
class lowerCamelCase ( metaclass=_UpperCAmelCase ):
lowercase : Tuple = ['flax', 'transformers']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(self , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
@classmethod
def a_ ( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
requires_backends(cls , ["""flax""", """transformers"""] )
| 27
|
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27
| 1
|
"""simple docstring"""
def A_ ( snake_case_ : int = 2_0_0_0_0_0_0 ):
'''simple docstring'''
UpperCamelCase : Optional[int] = [0 for i in range(n + 1 )]
UpperCamelCase : Dict = 1
UpperCamelCase : Tuple = 1
for i in range(2 ,int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i ,n + 1 ,snake_case_ ):
UpperCamelCase : List[Any] = 1
UpperCamelCase : str = 0
for i in range(snake_case_ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 27
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
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|
"""simple docstring"""
import os
import sys
import unittest
__A : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
__A : Optional[int] = os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
__A : Dict = os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
UpperCamelCase : str = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = {"""BertModelTest""": """BertModelTester"""}
UpperCamelCase : str = {
"""BlipModelTest""": """BlipModelTester""",
"""BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
"""BlipTextModelTest""": """BlipTextModelTester""",
"""BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
"""BlipVQAModelTest""": """BlipVQAModelTester""",
"""BlipVisionModelTest""": """BlipVisionModelTester""",
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = get_model_to_test_mapping(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = get_model_to_test_mapping(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = {
"""BertForMaskedLM""": ["""BertModelTest"""],
"""BertForMultipleChoice""": ["""BertModelTest"""],
"""BertForNextSentencePrediction""": ["""BertModelTest"""],
"""BertForPreTraining""": ["""BertModelTest"""],
"""BertForQuestionAnswering""": ["""BertModelTest"""],
"""BertForSequenceClassification""": ["""BertModelTest"""],
"""BertForTokenClassification""": ["""BertModelTest"""],
"""BertLMHeadModel""": ["""BertModelTest"""],
"""BertModel""": ["""BertModelTest"""],
}
UpperCamelCase : int = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
"""BlipModel""": ["""BlipModelTest"""],
"""BlipTextModel""": ["""BlipTextModelTest"""],
"""BlipVisionModel""": ["""BlipVisionModelTest"""],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""BertForMaskedLM""": ["""BertModelTester"""],
"""BertForMultipleChoice""": ["""BertModelTester"""],
"""BertForNextSentencePrediction""": ["""BertModelTester"""],
"""BertForPreTraining""": ["""BertModelTester"""],
"""BertForQuestionAnswering""": ["""BertModelTester"""],
"""BertForSequenceClassification""": ["""BertModelTester"""],
"""BertForTokenClassification""": ["""BertModelTester"""],
"""BertLMHeadModel""": ["""BertModelTester"""],
"""BertModel""": ["""BertModelTester"""],
}
UpperCamelCase : Union[str, Any] = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
"""BlipModel""": ["""BlipModelTester"""],
"""BlipTextModel""": ["""BlipTextModelTester"""],
"""BlipVisionModel""": ["""BlipVisionModelTester"""],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27
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|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__A : List[Any] = logging.get_logger(__name__)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" , SCREAMING_SNAKE_CASE_ , )
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = parent
UpperCamelCase : Tuple = 13
UpperCamelCase : Optional[Any] = 7
UpperCamelCase : List[Any] = True
UpperCamelCase : List[str] = True
UpperCamelCase : str = False
UpperCamelCase : Dict = True
UpperCamelCase : Tuple = 99
UpperCamelCase : int = 32
UpperCamelCase : Any = 2
UpperCamelCase : Union[str, Any] = 4
UpperCamelCase : Optional[int] = 37
UpperCamelCase : Any = """gelu"""
UpperCamelCase : Any = 0.1
UpperCamelCase : Any = 0.1
UpperCamelCase : Optional[int] = 512
UpperCamelCase : List[Any] = 16
UpperCamelCase : Any = 2
UpperCamelCase : List[Any] = 0.02
UpperCamelCase : List[str] = 3
UpperCamelCase : int = 4
UpperCamelCase : str = None
def a_ ( self ):
UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Any = None
if self.use_input_mask:
UpperCamelCase : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Tuple = None
UpperCamelCase : List[str] = None
UpperCamelCase : Any = None
if self.use_labels:
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : str = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[Any] = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = TFDistilBertModel(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = [input_ids, input_mask]
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = TFDistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = TFDistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
}
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = self.num_labels
UpperCamelCase : Tuple = TFDistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_choices
UpperCamelCase : Tuple = TFDistilBertForMultipleChoice(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Tuple = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Union[str, Any] = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
}
UpperCamelCase : str = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = self.num_labels
UpperCamelCase : str = TFDistilBertForTokenClassification(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : int = config_and_inputs
UpperCamelCase : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Tuple = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
lowercase : Tuple = (
{
'feature-extraction': TFDistilBertModel,
'fill-mask': TFDistilBertForMaskedLM,
'question-answering': TFDistilBertForQuestionAnswering,
'text-classification': TFDistilBertForSequenceClassification,
'token-classification': TFDistilBertForTokenClassification,
'zero-shot': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowercase : Any = False
lowercase : int = False
def a_ ( self ):
UpperCamelCase : Any = TFDistilBertModelTester(self )
UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
UpperCamelCase : Optional[int] = TFDistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_tf
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : List[str] = TFDistilBertModel.from_pretrained("""distilbert-base-uncased""" )
UpperCamelCase : Union[str, Any] = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : List[Any] = [1, 6, 768]
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = tf.constant(
[
[
[0.19261885, -0.13732955, 0.4119799],
[0.22150156, -0.07422661, 0.39037204],
[0.22756018, -0.0896414, 0.3701467],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 )
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
import math
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : str = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(snake_case_ )
def A_ ( snake_case_ : float = 1 / 1_2_3_4_5 ):
'''simple docstring'''
UpperCamelCase : str = 0
UpperCamelCase : Dict = 0
UpperCamelCase : Dict = 3
while True:
UpperCamelCase : Union[str, Any] = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(snake_case_ ):
UpperCamelCase : List[str] = int(snake_case_ )
total_partitions += 1
if check_partition_perfect(snake_case_ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(snake_case_ )
integer += 1
if __name__ == "__main__":
print(F'''{solution() = }''')
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
UpperCamelCase : Dict = [tensor.shape for tensor in tensor_list]
return all(shape == shapes[0] for shape in shapes[1:] )
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : str = StableDiffusionLatentUpscalePipeline
lowercase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
'height',
'width',
'cross_attention_kwargs',
'negative_prompt_embeds',
'prompt_embeds',
}
lowercase : str = PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
lowercase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowercase : Optional[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowercase : List[str] = frozenset([] )
lowercase : str = True
@property
def a_ ( self ):
UpperCamelCase : Tuple = 1
UpperCamelCase : List[str] = 4
UpperCamelCase : str = (16, 16)
UpperCamelCase : List[Any] = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE_ )
return image
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = UNetaDConditionModel(
act_fn="""gelu""" , attention_head_dim=8 , norm_num_groups=SCREAMING_SNAKE_CASE_ , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=(
"""KDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
"""KCrossAttnDownBlock2D""",
) , in_channels=8 , mid_block_type=SCREAMING_SNAKE_CASE_ , only_cross_attention=SCREAMING_SNAKE_CASE_ , out_channels=5 , resnet_time_scale_shift="""scale_shift""" , time_embedding_type="""fourier""" , timestep_post_act="""gelu""" , up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""") , )
UpperCamelCase : Optional[Any] = AutoencoderKL(
block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
"""DownEncoderBlock2D""",
] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
UpperCamelCase : Optional[int] = EulerDiscreteScheduler(prediction_type="""sample""" )
UpperCamelCase : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""quick_gelu""" , projection_dim=512 , )
UpperCamelCase : int = CLIPTextModel(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
UpperCamelCase : Tuple = {
"""unet""": model.eval(),
"""vae""": vae.eval(),
"""scheduler""": scheduler,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : Any = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Optional[int] = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": self.dummy_image.cpu(),
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def a_ ( self ):
UpperCamelCase : Union[str, Any] = """cpu"""
UpperCamelCase : Optional[Any] = self.get_dummy_components()
UpperCamelCase : int = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = pipe(**SCREAMING_SNAKE_CASE_ ).images
UpperCamelCase : Tuple = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 256, 256, 3) )
UpperCamelCase : Optional[int] = np.array(
[0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055] )
UpperCamelCase : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(SCREAMING_SNAKE_CASE_ , 1e-3 )
def a_ ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=7e-3 )
def a_ ( self ):
super().test_cpu_offload_forward_pass(expected_max_diff=3e-3 )
def a_ ( self ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def a_ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=7e-3 )
def a_ ( self ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3 )
def a_ ( self ):
super().test_save_load_local(expected_max_difference=3e-3 )
def a_ ( self ):
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def a_ ( self ):
UpperCamelCase : Dict = [
"""DDIMScheduler""",
"""DDPMScheduler""",
"""PNDMScheduler""",
"""HeunDiscreteScheduler""",
"""EulerAncestralDiscreteScheduler""",
"""KDPM2DiscreteScheduler""",
"""KDPM2AncestralDiscreteScheduler""",
"""DPMSolverSDEScheduler""",
]
UpperCamelCase : List[Any] = self.get_dummy_components()
UpperCamelCase : Tuple = self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
# make sure that PNDM does not need warm-up
pipe.scheduler.register_to_config(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
pipe.to(SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = 2
UpperCamelCase : Dict = []
for scheduler_enum in KarrasDiffusionSchedulers:
if scheduler_enum.name in skip_schedulers:
# no sigma schedulers are not supported
# no schedulers
continue
UpperCamelCase : Optional[int] = getattr(SCREAMING_SNAKE_CASE_ , scheduler_enum.name )
UpperCamelCase : Optional[int] = scheduler_cls.from_config(pipe.scheduler.config )
UpperCamelCase : Dict = pipe(**SCREAMING_SNAKE_CASE_ )[0]
outputs.append(SCREAMING_SNAKE_CASE_ )
assert check_same_shape(SCREAMING_SNAKE_CASE_ )
@require_torch_gpu
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self ):
UpperCamelCase : Tuple = torch.manual_seed(33 )
UpperCamelCase : List[Any] = StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" , torch_dtype=torch.floataa )
pipe.to("""cuda""" )
UpperCamelCase : List[str] = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
UpperCamelCase : Optional[Any] = """a photo of an astronaut high resolution, unreal engine, ultra realistic"""
UpperCamelCase : str = pipe(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , output_type="""latent""" ).images
UpperCamelCase : Tuple = upscaler(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , num_inference_steps=20 , guidance_scale=0 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , ).images[0]
UpperCamelCase : Tuple = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy""" )
assert np.abs((expected_image - image).mean() ) < 5e-2
def a_ ( self ):
UpperCamelCase : List[Any] = torch.manual_seed(33 )
UpperCamelCase : List[Any] = StableDiffusionLatentUpscalePipeline.from_pretrained(
"""stabilityai/sd-x2-latent-upscaler""" , torch_dtype=torch.floataa )
upscaler.to("""cuda""" )
UpperCamelCase : List[str] = """the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""
UpperCamelCase : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png""" )
UpperCamelCase : List[str] = upscaler(
prompt=SCREAMING_SNAKE_CASE_ , image=SCREAMING_SNAKE_CASE_ , num_inference_steps=20 , guidance_scale=0 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" , ).images[0]
UpperCamelCase : Dict = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy""" )
assert np.abs((expected_image - image).max() ) < 5e-2
| 27
|
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27
| 1
|
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
__A : Dict = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 27
|
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Dict = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2StructTextConfig''',
'''Pix2StructVisionConfig''',
],
'''processing_pix2struct''': ['''Pix2StructProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = ['''Pix2StructImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = [
'''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Pix2StructPreTrainedModel''',
'''Pix2StructForConditionalGeneration''',
'''Pix2StructVisionModel''',
'''Pix2StructTextModel''',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : List[str] = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BlipConfig''',
'''BlipTextConfig''',
'''BlipVisionConfig''',
],
'''processing_blip''': ['''BlipProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = ['''BlipImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[Any] = [
'''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BlipModel''',
'''BlipPreTrainedModel''',
'''BlipForConditionalGeneration''',
'''BlipForQuestionAnswering''',
'''BlipVisionModel''',
'''BlipTextModel''',
'''BlipForImageTextRetrieval''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = [
'''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBlipModel''',
'''TFBlipPreTrainedModel''',
'''TFBlipForConditionalGeneration''',
'''TFBlipForQuestionAnswering''',
'''TFBlipVisionModel''',
'''TFBlipTextModel''',
'''TFBlipForImageTextRetrieval''',
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
__A : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Optional[Any] = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VanForImageClassification''',
'''VanModel''',
'''VanPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
__A : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 27
|
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[str] = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = [
'''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LukeForEntityClassification''',
'''LukeForEntityPairClassification''',
'''LukeForEntitySpanClassification''',
'''LukeForMultipleChoice''',
'''LukeForQuestionAnswering''',
'''LukeForSequenceClassification''',
'''LukeForTokenClassification''',
'''LukeForMaskedLM''',
'''LukeModel''',
'''LukePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
from .tokenization_luke import LukeTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_luke import (
LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
LukeForEntityClassification,
LukeForEntityPairClassification,
LukeForEntitySpanClassification,
LukeForMaskedLM,
LukeForMultipleChoice,
LukeForQuestionAnswering,
LukeForSequenceClassification,
LukeForTokenClassification,
LukeModel,
LukePreTrainedModel,
)
else:
import sys
__A : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
| 1
|
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27
|
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase ( torch.nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased" ):
super(SCREAMING_SNAKE_CASE_ , self ).__init__()
UpperCamelCase : int = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.nn.CosineSimilarity(3 , 1e-08 )
UpperCamelCase : Any = torch.nn.Softmax(dim=1 )
def a_ ( self , **SCREAMING_SNAKE_CASE_ ):
return self.bert(**SCREAMING_SNAKE_CASE_ ).last_hidden_state
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
return token_embeddings.sum(2 , keepdim=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ):
return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = W_supports["""sizes"""].tolist()
UpperCamelCase : List[str] = W_supports["""start_token_id"""].item()
UpperCamelCase : List[Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
UpperCamelCase : List[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.BERT(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Tuple = W_supports["""input_ids"""] == start_token_id
UpperCamelCase : Optional[Any] = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(SCREAMING_SNAKE_CASE_ ):
if i == 0:
UpperCamelCase : int = 0
else:
UpperCamelCase : Optional[int] = support_sizes[i - 1]
UpperCamelCase : Tuple = S[s : s + size][start_token_masks[s : s + size]]
UpperCamelCase : int = S[s : s + size][end_token_masks[s : s + size]]
UpperCamelCase : Dict = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
UpperCamelCase : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
UpperCamelCase : List[str] = torch.vstack((p_starts, p_start) )
UpperCamelCase : Optional[Any] = torch.vstack((p_ends, p_end) )
else:
UpperCamelCase : Optional[int] = p_start
UpperCamelCase : Tuple = p_end
return p_starts, p_ends
| 27
| 1
|
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Optional[Any] ,snake_case_ : int ,snake_case_ : str ):
'''simple docstring'''
if isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : Any = np.full((len(snake_case_ ), sequence_length, 2) ,snake_case_ )
else:
UpperCamelCase : Dict = np.full((len(snake_case_ ), sequence_length) ,snake_case_ )
for i, tensor in enumerate(snake_case_ ):
if padding_side == "right":
if isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : str = tensor[:sequence_length]
else:
UpperCamelCase : Any = tensor[:sequence_length]
else:
if isinstance(snake_case_ ,snake_case_ ):
UpperCamelCase : int = tensor[:sequence_length]
else:
UpperCamelCase : str = tensor[:sequence_length]
return out_tensor.tolist()
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Optional[int] = ord(snake_case_ )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
UpperCamelCase : Optional[Any] = unicodedata.category(snake_case_ )
if cat.startswith("""P""" ):
return True
return False
@dataclass
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : PreTrainedTokenizerBase
lowercase : Union[bool, str, PaddingStrategy] = True
lowercase : Optional[int] = None
lowercase : Optional[int] = None
lowercase : int = -1_0_0
lowercase : str = "pt"
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
import torch
UpperCamelCase : str = """label""" if """label""" in features[0].keys() else """labels"""
UpperCamelCase : Optional[int] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
UpperCamelCase : Union[str, Any] = self.tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" if labels is None else None , )
if labels is None:
return batch
UpperCamelCase : Tuple = torch.tensor(batch["""entity_ids"""] ).shape[1]
UpperCamelCase : Any = self.tokenizer.padding_side
if padding_side == "right":
UpperCamelCase : str = [
list(SCREAMING_SNAKE_CASE_ ) + [self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE_ )) for label in labels
]
else:
UpperCamelCase : List[Any] = [
[self.label_pad_token_id] * (sequence_length - len(SCREAMING_SNAKE_CASE_ )) + list(SCREAMING_SNAKE_CASE_ ) for label in labels
]
UpperCamelCase : List[Any] = [feature["""ner_tags"""] for feature in features]
UpperCamelCase : List[Any] = padding_tensor(SCREAMING_SNAKE_CASE_ , -1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = [feature["""original_entity_spans"""] for feature in features]
UpperCamelCase : Dict = padding_tensor(SCREAMING_SNAKE_CASE_ , (-1, -1) , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = {k: torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 27
|
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):
return f'Node({self.data})'
class lowerCamelCase :
def __init__( self ):
UpperCamelCase : Dict = None
def __iter__( self ):
UpperCamelCase : int = self.head
while node:
yield node.data
UpperCamelCase : Union[str, Any] = node.next
def __len__( self ):
return sum(1 for _ in self )
def __repr__( self ):
return "->".join([str(SCREAMING_SNAKE_CASE_ ) for item in self] )
def __getitem__( self , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
UpperCamelCase : List[Any] = self.head
for _ in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = current.next
UpperCamelCase : Optional[Any] = data
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.insert_nth(0 , SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
UpperCamelCase : Optional[Any] = Node(SCREAMING_SNAKE_CASE_ )
if self.head is None:
UpperCamelCase : Dict = new_node
elif index == 0:
UpperCamelCase : Any = self.head # link new_node to head
UpperCamelCase : Any = new_node
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : str = temp.next
UpperCamelCase : Any = temp.next
UpperCamelCase : Optional[Any] = new_node
def a_ ( self ): # print every node data
print(self )
def a_ ( self ):
return self.delete_nth(0 )
def a_ ( self ): # delete from tail
return self.delete_nth(len(self ) - 1 )
def a_ ( self , SCREAMING_SNAKE_CASE_ = 0 ):
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
UpperCamelCase : Union[str, Any] = self.head # default first node
if index == 0:
UpperCamelCase : Optional[Any] = self.head.next
else:
UpperCamelCase : Dict = self.head
for _ in range(index - 1 ):
UpperCamelCase : int = temp.next
UpperCamelCase : Optional[Any] = temp.next
UpperCamelCase : Dict = temp.next.next
return delete_node.data
def a_ ( self ):
return self.head is None
def a_ ( self ):
UpperCamelCase : Optional[Any] = None
UpperCamelCase : Union[str, Any] = self.head
while current:
# Store the current node's next node.
UpperCamelCase : Optional[int] = current.next
# Make the current node's next point backwards
UpperCamelCase : Optional[Any] = prev
# Make the previous node be the current node
UpperCamelCase : int = current
# Make the current node the next node (to progress iteration)
UpperCamelCase : Optional[int] = next_node
# Return prev in order to put the head at the end
UpperCamelCase : Optional[int] = prev
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = LinkedList()
assert linked_list.is_empty() is True
assert str(snake_case_ ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(1_0 ):
assert len(snake_case_ ) == i
linked_list.insert_nth(snake_case_ ,i + 1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_1 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(1_1 )
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(0 ,1_2 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 1_0
assert linked_list.delete_tail() == 1_1
assert len(snake_case_ ) == 9
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(1 ,1_0 ) )
assert all(linked_list[i] == i + 1 for i in range(0 ,9 ) ) is True
for i in range(0 ,9 ):
UpperCamelCase : Optional[Any] = -i
assert all(linked_list[i] == -i for i in range(0 ,9 ) ) is True
linked_list.reverse()
assert str(snake_case_ ) == "->".join(str(snake_case_ ) for i in range(-8 ,1 ) )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = [
-9,
1_0_0,
Node(7_7_3_4_5_1_1_2 ),
"""dlrow olleH""",
7,
5_5_5_5,
0,
-192.55555,
"""Hello, world!""",
77.9,
Node(1_0 ),
None,
None,
12.20,
]
UpperCamelCase : List[Any] = LinkedList()
for i in test_input:
linked_list.insert_tail(snake_case_ )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(snake_case_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
UpperCamelCase : Dict = linked_list.delete_head()
assert result == -9
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
UpperCamelCase : int = linked_list.delete_tail()
assert result == 12.2
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
UpperCamelCase : Optional[Any] = linked_list.delete_nth(1_0 )
assert result is None
assert (
str(snake_case_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(snake_case_ )
assert (
str(snake_case_ )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(snake_case_ )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def A_ ( ):
'''simple docstring'''
from doctest import testmod
testmod()
UpperCamelCase : List[Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(snake_case_ )
print("""\nReading/changing Node data using indexing:""" )
print(f'Element at Position 1: {linked_list[1]}' )
UpperCamelCase : List[Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(snake_case_ )
print(f'length of linked_list is : {len(snake_case_ )}' )
if __name__ == "__main__":
main()
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=36 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=37 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=1000 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : Optional[Any] = batch_size
UpperCamelCase : List[str] = num_channels
UpperCamelCase : Optional[Any] = image_size
UpperCamelCase : Tuple = patch_size
UpperCamelCase : Union[str, Any] = is_training
UpperCamelCase : Dict = use_input_mask
UpperCamelCase : Optional[Any] = use_token_type_ids
UpperCamelCase : Union[str, Any] = use_labels
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = hidden_size
UpperCamelCase : str = num_hidden_layers
UpperCamelCase : Optional[int] = num_attention_heads
UpperCamelCase : List[Any] = intermediate_size
UpperCamelCase : Any = hidden_act
UpperCamelCase : Dict = hidden_dropout_prob
UpperCamelCase : Tuple = attention_probs_dropout_prob
UpperCamelCase : Tuple = max_position_embeddings
UpperCamelCase : int = type_vocab_size
UpperCamelCase : Tuple = type_sequence_label_size
UpperCamelCase : Any = initializer_range
UpperCamelCase : List[str] = coordinate_size
UpperCamelCase : int = shape_size
UpperCamelCase : Tuple = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : Optional[Any] = scope
UpperCamelCase : Tuple = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
UpperCamelCase : Dict = text_seq_length
UpperCamelCase : Optional[int] = (image_size // patch_size) ** 2 + 1
UpperCamelCase : int = self.text_seq_length + self.image_seq_length
def a_ ( self ):
UpperCamelCase : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
UpperCamelCase : Optional[Any] = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCamelCase : Optional[Any] = bbox[i, j, 3]
UpperCamelCase : Optional[Any] = bbox[i, j, 1]
UpperCamelCase : Dict = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCamelCase : Union[str, Any] = bbox[i, j, 2]
UpperCamelCase : List[Any] = bbox[i, j, 0]
UpperCamelCase : Dict = tmp_coordinate
UpperCamelCase : Optional[int] = tf.constant(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase : List[Any] = None
if self.use_input_mask:
UpperCamelCase : Optional[int] = random_attention_mask([self.batch_size, self.text_seq_length] )
UpperCamelCase : int = None
if self.use_token_type_ids:
UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
UpperCamelCase : List[Any] = None
UpperCamelCase : List[Any] = None
if self.use_labels:
UpperCamelCase : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
UpperCamelCase : Optional[Any] = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE_ )
# text + image
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
UpperCamelCase : Dict = model({"""pixel_values""": pixel_values} , training=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : List[Any] = TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = self.num_labels
UpperCamelCase : Optional[Any] = TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = 2
UpperCamelCase : int = TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = model(
SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self ):
UpperCamelCase : Dict = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Tuple = config_and_inputs
UpperCamelCase : List[Any] = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : List[Any] = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase : Union[str, Any] = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
lowercase : int = False
lowercase : List[str] = False
lowercase : int = False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return True
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Any = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = {
k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_ , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(SCREAMING_SNAKE_CASE_ , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
UpperCamelCase : Union[str, Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def a_ ( self ):
UpperCamelCase : Any = TFLayoutLMvaModelTester(self )
UpperCamelCase : Tuple = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase , UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase : Optional[int] = model_class(SCREAMING_SNAKE_CASE_ )
if getattr(SCREAMING_SNAKE_CASE_ , """hf_compute_loss""" , SCREAMING_SNAKE_CASE_ ):
# The number of elements in the loss should be the same as the number of elements in the label
UpperCamelCase : List[Any] = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=SCREAMING_SNAKE_CASE_ )[0]
]
UpperCamelCase : str = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
UpperCamelCase : int = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = prepared_for_class.pop("""input_ids""" )
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
UpperCamelCase : Optional[Any] = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
UpperCamelCase : Optional[Any] = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
UpperCamelCase : Optional[Any] = -100
UpperCamelCase : Any = tf.convert_to_tensor(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
UpperCamelCase : str = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
UpperCamelCase : Any = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
# Get keys that were added with the _prepare_for_class function
UpperCamelCase : Tuple = prepared_for_class.keys() - inputs_dict.keys()
UpperCamelCase : Optional[Any] = inspect.signature(model.call ).parameters
UpperCamelCase : int = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
UpperCamelCase : List[Any] = {0: """input_ids"""}
for label_key in label_keys:
UpperCamelCase : List[str] = signature_names.index(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = label_key
UpperCamelCase : List[str] = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
UpperCamelCase : Tuple = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
UpperCamelCase : Dict = prepared_for_class[value]
UpperCamelCase : Tuple = tuple(SCREAMING_SNAKE_CASE_ )
# Send to model
UpperCamelCase : str = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def a_ ( self ):
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase : str = type
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : int = TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class lowerCamelCase ( unittest.TestCase ):
@cached_property
def a_ ( self ):
return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE_ ) if is_vision_available() else None
@slow
def a_ ( self ):
UpperCamelCase : Any = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
UpperCamelCase : Optional[int] = self.default_image_processor
UpperCamelCase : str = prepare_img()
UpperCamelCase : List[str] = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="""tf""" ).pixel_values
UpperCamelCase : List[Any] = tf.constant([[1, 2]] )
UpperCamelCase : Dict = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
UpperCamelCase : Union[str, Any] = model(input_ids=SCREAMING_SNAKE_CASE_ , bbox=SCREAMING_SNAKE_CASE_ , pixel_values=SCREAMING_SNAKE_CASE_ , training=SCREAMING_SNAKE_CASE_ )
# verify the logits
UpperCamelCase : Union[str, Any] = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__A : Union[str, Any] = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : Tuple = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : Tuple = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Dict="" ,snake_case_ : Dict=None ,snake_case_ : Any=None ):
'''simple docstring'''
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Optional[Any] = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : int = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Any = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Any = [lines[index + 1]]
index += 1
else:
UpperCamelCase : List[str] = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
def _inner(snake_case_ : Tuple ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : List[Any] ,snake_case_ : Optional[int]=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Dict ):
return x
if key is None:
UpperCamelCase : int = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[Any] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : str = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : List[str] = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Tuple = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : int ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : List[Any] ):
UpperCamelCase : Any = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : Union[str, Any] = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[str] = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : str = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : str = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Dict = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : int = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Any = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[Any] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : Optional[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : List[Any] = keys[:-1]
UpperCamelCase : int = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Tuple ,snake_case_ : str=True ):
'''simple docstring'''
with open(snake_case_ ,"""r""" ) as f:
UpperCamelCase : int = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : Dict = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Optional[Any] = main_blocks[block_idx]
UpperCamelCase : Optional[int] = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : Union[str, Any] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : List[str] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Dict = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Union[str, Any] = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : Optional[int] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Union[str, Any] = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Union[str, Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Optional[Any] = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : List[Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[Any] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : str = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Any = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Union[str, Any] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : Any = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : str = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
raise ValueError("""numbers must be an iterable of integers""" )
UpperCamelCase : int = numbers[0]
for i in range(1 ,len(snake_case_ ) ):
# update the maximum and minimum subarray products
UpperCamelCase : List[str] = numbers[i]
if number < 0:
UpperCamelCase , UpperCamelCase : Optional[int] = min_till_now, max_till_now
UpperCamelCase : Dict = max(snake_case_ ,max_till_now * number )
UpperCamelCase : Union[str, Any] = min(snake_case_ ,min_till_now * number )
# update the maximum product found till now
UpperCamelCase : Union[str, Any] = max(snake_case_ ,snake_case_ )
return max_prod
| 27
| 1
|
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class lowerCamelCase ( _UpperCAmelCase ):
def a_ ( self ):
UpperCamelCase : Optional[int] = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def a_ ( self ):
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def a_ ( self ):
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""bool""" ) , type=Value("""int64""" ) ) )
def a_ ( self ):
UpperCamelCase : Tuple = pa.array(TypedSequence([1, 2, 3] , type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def a_ ( self ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
UpperCamelCase : int = pa.array(TypedSequence(["""foo""", """bar"""] , type=Value("""int64""" ) ) )
def a_ ( self ):
UpperCamelCase : Dict = pa.array(TypedSequence([1, 2, 3] , try_type=Value("""int32""" ) ) )
self.assertEqual(arr.type , pa.intaa() )
def a_ ( self ):
UpperCamelCase : List[str] = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=Value("""int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
def a_ ( self ):
UpperCamelCase : List[Any] = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def a_ ( self ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
UpperCamelCase : Tuple = pa.array(TypedSequence(["""foo""", """bar"""] , type=ArrayaD((1, 3) , """int64""" ) ) )
def a_ ( self ):
UpperCamelCase : List[str] = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , """int64""" ) )
def a_ ( self ):
UpperCamelCase : int = pa.array(TypedSequence(["""foo""", """bar"""] , try_type=ArrayaD((1, 3) , """int64""" ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def a_ ( self ):
import PIL.Image
UpperCamelCase : List[str] = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
"""datasets.arrow_writer.cast_to_python_objects""" , side_effect=SCREAMING_SNAKE_CASE_ ) as mock_cast_to_python_objects:
UpperCamelCase : Optional[int] = pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image] , type=Image() ) )
UpperCamelCase , UpperCamelCase : Tuple = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn("""optimize_list_casting""" , SCREAMING_SNAKE_CASE_ )
self.assertFalse(kwargs["""optimize_list_casting"""] )
def A_ ( snake_case_ : Any ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : str = pa.BufferReader(snake_case_ ) if isinstance(snake_case_ ,pa.Buffer ) else pa.memory_map(snake_case_ )
UpperCamelCase : List[Any] = pa.ipc.open_stream(snake_case_ )
UpperCamelCase : pa.Table = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : int ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = pa.BufferOutputStream()
UpperCamelCase : str = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
UpperCamelCase , UpperCamelCase : Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : Union[str, Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Any = pa.BufferOutputStream()
UpperCamelCase : str = Features({"""labels""": ClassLabel(names=["""neg""", """pos"""] )} )
with ArrowWriter(stream=snake_case_ ,features=snake_case_ ) as writer:
writer.write({"""labels""": 0} )
writer.write({"""labels""": 1} )
UpperCamelCase , UpperCamelCase : Any = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
UpperCamelCase : Tuple = pa.BufferReader(output.getvalue() )
UpperCamelCase : Union[str, Any] = pa.ipc.open_stream(snake_case_ )
UpperCamelCase : pa.Table = f.read_all()
UpperCamelCase : List[Any] = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(snake_case_ )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
UpperCamelCase : List[Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ ,writer_batch_size=snake_case_ ,hash_salt="""split_name""" ,check_duplicates=snake_case_ ,) as writer:
with pytest.raises(snake_case_ ):
writer.write({"""col_1""": """foo""", """col_2""": 1} ,key=[1, 2] )
UpperCamelCase , UpperCamelCase : int = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 2, 1_0] )
def A_ ( snake_case_ : Optional[int] ):
'''simple docstring'''
UpperCamelCase : Optional[int] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ ,writer_batch_size=snake_case_ ,hash_salt="""split_name""" ,check_duplicates=snake_case_ ,) as writer:
with pytest.raises(snake_case_ ):
writer.write({"""col_1""": """foo""", """col_2""": 1} ,key=1_0 )
writer.write({"""col_1""": """bar""", """col_2""": 2} ,key=1_0 )
UpperCamelCase , UpperCamelCase : Any = writer.finalize()
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 2, 1_0] )
def A_ ( snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : List[Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case_ ,writer_batch_size=snake_case_ ,hash_salt="""split_name""" ,check_duplicates=snake_case_ ,) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} ,key=1 )
writer.write({"""col_1""": """bar""", """col_2""": 2} ,key=2 )
UpperCamelCase , UpperCamelCase : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : Optional[int] ,snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : Any = pa.BufferOutputStream()
UpperCamelCase : Optional[Any] = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
writer.write_batch({"""col_1""": [], """col_2""": []} )
UpperCamelCase , UpperCamelCase : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : str = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : Any ,snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Any = pa.BufferOutputStream()
UpperCamelCase : Dict = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} ) )
UpperCamelCase , UpperCamelCase : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : Union[str, Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize("""writer_batch_size""" ,[None, 1, 1_0] )
@pytest.mark.parametrize(
"""fields""" ,[None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}] )
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Optional[Any] ):
'''simple docstring'''
UpperCamelCase : Tuple = pa.BufferOutputStream()
UpperCamelCase : Tuple = pa.schema(snake_case_ ) if fields else None
with ArrowWriter(stream=snake_case_ ,schema=snake_case_ ,writer_batch_size=snake_case_ ) as writer:
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]} ) )
writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]} ) )
UpperCamelCase , UpperCamelCase : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : List[Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(output.getvalue() ,expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def A_ ( ):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : List[Any] = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
UpperCamelCase : Any = os.path.join(snake_case_ ,"""test.arrow""" )
with ArrowWriter(path=snake_case_ ,schema=pa.schema(snake_case_ ) ) as writer:
writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]} )
UpperCamelCase , UpperCamelCase : List[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(snake_case_ ,metadata=writer._schema.metadata )
_check_output(snake_case_ ,1 )
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
if pa.types.is_list(snake_case_ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def A_ ( snake_case_ : Optional[int] ,snake_case_ : Dict ):
'''simple docstring'''
if isinstance(lst[0] ,snake_case_ ):
change_first_primitive_element_in_list(lst[0] ,snake_case_ )
else:
UpperCamelCase : List[Any] = value
@pytest.mark.parametrize("""optimized_int_type, expected_dtype""" ,[(None, pa.intaa()), (Value("""int32""" ), pa.intaa())] )
@pytest.mark.parametrize("""sequence""" ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def A_ ( snake_case_ : Tuple ,snake_case_ : List[Any] ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = pa.array(TypedSequence(snake_case_ ,optimized_int_type=snake_case_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
"""col, expected_dtype""" ,[
("""attention_mask""", pa.inta()),
("""special_tokens_mask""", pa.inta()),
("""token_type_ids""", pa.inta()),
("""input_ids""", pa.intaa()),
("""other""", pa.intaa()),
] ,)
@pytest.mark.parametrize("""sequence""" ,[[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Union[str, Any] ,snake_case_ : str ):
'''simple docstring'''
# in range
UpperCamelCase : str = pa.array(OptimizedTypedSequence(snake_case_ ,col=snake_case_ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
UpperCamelCase : str = copy.deepcopy(snake_case_ )
UpperCamelCase : Tuple = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(snake_case_ ,snake_case_ )
UpperCamelCase : int = pa.array(OptimizedTypedSequence(snake_case_ ,col=snake_case_ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize("""raise_exception""" ,[False, True] )
def A_ ( snake_case_ : Dict ,snake_case_ : Tuple ):
'''simple docstring'''
UpperCamelCase : Tuple = str(tmp_path / """dataset-train.arrow""" )
try:
with ArrowWriter(path=snake_case_ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Dict = """mock://dataset-train.arrow"""
with ArrowWriter(path=snake_case_ ,storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs ,type(snake_case_ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
UpperCamelCase , UpperCamelCase : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = pa.BufferOutputStream()
with ParquetWriter(stream=snake_case_ ) as writer:
writer.write({"""col_1""": """foo""", """col_2""": 1} )
writer.write({"""col_1""": """bar""", """col_2""": 2} )
UpperCamelCase , UpperCamelCase : List[str] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
UpperCamelCase : List[str] = pa.BufferReader(output.getvalue() )
UpperCamelCase : pa.Table = pq.read_table(snake_case_ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize("""embed_local_files""" ,[False, True] )
def A_ ( snake_case_ : str ,snake_case_ : List[Any] ):
'''simple docstring'''
import PIL.Image
UpperCamelCase : Optional[Any] = str(tmp_path / """test_image_rgb.jpg""" )
PIL.Image.fromarray(np.zeros((5, 5) ,dtype=np.uinta ) ).save(snake_case_ ,format="""png""" )
UpperCamelCase : List[str] = pa.BufferOutputStream()
with ParquetWriter(
stream=snake_case_ ,features=Features({"""image""": Image()} ) ,embed_local_files=snake_case_ ) as writer:
writer.write({"""image""": image_path} )
writer.finalize()
UpperCamelCase : Union[str, Any] = pa.BufferReader(output.getvalue() )
UpperCamelCase : pa.Table = pq.read_table(snake_case_ )
UpperCamelCase : Any = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out["""image"""][0]["""path"""] ,snake_case_ )
with open(snake_case_ ,"""rb""" ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def A_ ( ):
'''simple docstring'''
UpperCamelCase : int = pa.schema([pa.field("""col_1""" ,pa.string() ,nullable=snake_case_ )] )
UpperCamelCase : Dict = pa.BufferOutputStream()
with ArrowWriter(stream=snake_case_ ) as writer:
writer._build_writer(inferred_schema=snake_case_ )
assert writer._schema == pa.schema([pa.field("""col_1""" ,pa.string() )] )
| 27
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = AudioLDMPipeline
lowercase : Union[str, Any] = TEXT_TO_AUDIO_PARAMS
lowercase : List[str] = TEXT_TO_AUDIO_BATCH_PARAMS
lowercase : Tuple = frozenset(
[
'num_inference_steps',
'num_waveforms_per_prompt',
'generator',
'latents',
'output_type',
'return_dict',
'callback',
'callback_steps',
] )
def a_ ( self ):
torch.manual_seed(0 )
UpperCamelCase : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=(32, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[Any] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , )
torch.manual_seed(0 )
UpperCamelCase : Optional[int] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
UpperCamelCase : int = ClapTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , projection_dim=32 , )
UpperCamelCase : Optional[int] = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 )
UpperCamelCase : Tuple = SpeechTaHifiGanConfig(
model_in_dim=8 , sampling_rate=1_6000 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ):
if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
UpperCamelCase : List[Any] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Any = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def a_ ( self ):
UpperCamelCase : str = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Any = self.get_dummy_components()
UpperCamelCase : int = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Tuple = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
UpperCamelCase : Union[str, Any] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[str] = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Optional[int] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : str = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
UpperCamelCase : Tuple = prompt_embeds
# forward
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : List[str] = self.get_dummy_components()
UpperCamelCase : List[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = 3 * ["""this is a negative prompt"""]
UpperCamelCase : List[Any] = negative_prompt
UpperCamelCase : str = 3 * [inputs["""prompt"""]]
# forward
UpperCamelCase : str = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
UpperCamelCase : Tuple = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = 3 * [inputs.pop("""prompt""" )]
UpperCamelCase : List[Any] = []
for p in [prompt, negative_prompt]:
UpperCamelCase : int = audioldm_pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
UpperCamelCase : Union[str, Any] = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audioldm_pipe.text_encoder(
SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Tuple = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
UpperCamelCase : Optional[int] = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 )
embeds.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase : Tuple = embeds
# forward
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Optional[int] = self.get_dummy_components()
UpperCamelCase : List[str] = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = """egg cracking"""
UpperCamelCase : List[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 256
UpperCamelCase : Union[str, Any] = audio[:10]
UpperCamelCase : Dict = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Union[str, Any] = self.get_dummy_components()
UpperCamelCase : Tuple = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
UpperCamelCase : List[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
UpperCamelCase : Dict = 2
UpperCamelCase : List[str] = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
UpperCamelCase : List[str] = 2
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
UpperCamelCase : Any = 2
UpperCamelCase : str = audioldm_pipe(
[prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def a_ ( self ):
UpperCamelCase : Optional[int] = """cpu""" # ensure determinism for the device-dependent torch.Generator
UpperCamelCase : Tuple = self.get_dummy_components()
UpperCamelCase : Tuple = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = audioldm_pipe.vocoder.config.sampling_rate
UpperCamelCase : List[str] = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016
UpperCamelCase : Optional[Any] = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = output.audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032
def a_ ( self ):
UpperCamelCase : str = self.get_dummy_components()
UpperCamelCase : Optional[Any] = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = ["""hey"""]
UpperCamelCase : Dict = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : str = output.audios.shape
assert audio_shape == (1, 256)
UpperCamelCase : Optional[Any] = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
UpperCamelCase : str = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 )
UpperCamelCase : List[str] = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def a_ ( self ):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def a_ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ )
@slow
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ):
UpperCamelCase : str = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 128, 16) )
UpperCamelCase : int = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def a_ ( self ):
UpperCamelCase : Optional[int] = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : List[Any] = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = 25
UpperCamelCase : Optional[Any] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[7_7230:7_7240]
UpperCamelCase : Optional[Any] = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
UpperCamelCase : Any = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def a_ ( self ):
UpperCamelCase : Any = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
UpperCamelCase : Any = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
UpperCamelCase : str = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ )
audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = self.get_inputs(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0]
assert audio.ndim == 1
assert len(SCREAMING_SNAKE_CASE_ ) == 8_1920
UpperCamelCase : Union[str, Any] = audio[2_7780:2_7790]
UpperCamelCase : Tuple = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
UpperCamelCase : Tuple = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 27
| 1
|
"""simple docstring"""
# using dfs for finding eulerian path traversal
def A_ ( snake_case_ : List[Any] ,snake_case_ : Any ,snake_case_ : Optional[Any] ,snake_case_ : Dict=None ):
'''simple docstring'''
UpperCamelCase : str = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
UpperCamelCase , UpperCamelCase : Any = True, True
UpperCamelCase : Optional[Any] = dfs(snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
return path
def A_ ( snake_case_ : List[Any] ,snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : int = 0
UpperCamelCase : Any = -1
for i in range(snake_case_ ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
UpperCamelCase : int = i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def A_ ( snake_case_ : int ,snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : int = [[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
UpperCamelCase , UpperCamelCase : int = check_circuit_or_path(snake_case_ ,snake_case_ )
if check == 3:
print("""graph is not Eulerian""" )
print("""no path""" )
return
UpperCamelCase : str = 1
if check == 2:
UpperCamelCase : Optional[Any] = odd_node
print("""graph has a Euler path""" )
if check == 1:
print("""graph has a Euler cycle""" )
UpperCamelCase : Tuple = dfs(snake_case_ ,snake_case_ ,snake_case_ )
print(snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[int] = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
UpperCamelCase : int = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
UpperCamelCase : Union[str, Any] = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
UpperCamelCase : List[Any] = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
UpperCamelCase : int = {
1: [],
2: []
# all degree is zero
}
UpperCamelCase : Any = 1_0
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
check_euler(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def A_ ( snake_case_ : Dataset ,snake_case_ : Dict[str, str] ):
'''simple docstring'''
UpperCamelCase : List[str] = args.log_outputs
UpperCamelCase : Tuple = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase : List[Any] = load_metric("""wer""" )
UpperCamelCase : Any = load_metric("""cer""" )
# compute metrics
UpperCamelCase : str = wer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
UpperCamelCase : Dict = cer.compute(references=result["""target"""] ,predictions=result["""prediction"""] )
# print & log results
UpperCamelCase : Optional[int] = f'WER: {wer_result}\nCER: {cer_result}'
print(snake_case_ )
with open(f'{dataset_id}_eval_results.txt' ,"""w""" ) as f:
f.write(snake_case_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase : Optional[Any] = f'log_{dataset_id}_predictions.txt'
UpperCamelCase : str = f'log_{dataset_id}_targets.txt'
with open(snake_case_ ,"""w""" ) as p, open(snake_case_ ,"""w""" ) as t:
# mapping function to write output
def write_to_file(snake_case_ : Union[str, Any] ,snake_case_ : Tuple ):
p.write(f'{i}' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'{i}' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(snake_case_ ,with_indices=snake_case_ )
def A_ ( snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Dict = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase : str = re.sub(snake_case_ ,"""""" ,text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase : List[str] = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase : Tuple = """ """.join(text.split(snake_case_ ) )
return text
def A_ ( snake_case_ : str ):
'''simple docstring'''
# load dataset
UpperCamelCase : Union[str, Any] = load_dataset(args.dataset ,args.config ,split=args.split ,use_auth_token=snake_case_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase : List[Any] = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase : Dict = feature_extractor.sampling_rate
# resample audio
UpperCamelCase : Optional[Any] = dataset.cast_column("""audio""" ,Audio(sampling_rate=snake_case_ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase : int = 0 if torch.cuda.is_available() else -1
UpperCamelCase : Union[str, Any] = pipeline("""automatic-speech-recognition""" ,model=args.model_id ,device=args.device )
# map function to decode audio
def map_to_pred(snake_case_ : Union[str, Any] ):
UpperCamelCase : List[Any] = asr(
batch["""audio"""]["""array"""] ,chunk_length_s=args.chunk_length_s ,stride_length_s=args.stride_length_s )
UpperCamelCase : Union[str, Any] = prediction["""text"""]
UpperCamelCase : Optional[Any] = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase : Any = dataset.map(snake_case_ ,remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(snake_case_ ,snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
__A : Optional[Any] = parser.parse_args()
main(args)
| 27
| 1
|
"""simple docstring"""
def A_ ( snake_case_ : list[list[int]] ,snake_case_ : int ,snake_case_ : int ,snake_case_ : set ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : int = len(snake_case_ ), len(grid[0] )
if (
min(snake_case_ ,snake_case_ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
UpperCamelCase : Optional[int] = 0
count += depth_first_search(snake_case_ ,row + 1 ,snake_case_ ,snake_case_ )
count += depth_first_search(snake_case_ ,row - 1 ,snake_case_ ,snake_case_ )
count += depth_first_search(snake_case_ ,snake_case_ ,col + 1 ,snake_case_ )
count += depth_first_search(snake_case_ ,snake_case_ ,col - 1 ,snake_case_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
|
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Union[str, Any] = 'EncodecFeatureExtractor'
lowercase : List[Any] = ('T5Tokenizer', 'T5TokenizerFast')
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = self.feature_extractor
UpperCamelCase : Any = False
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE_ , language=SCREAMING_SNAKE_CASE_ , no_timestamps=SCREAMING_SNAKE_CASE_ )
def __call__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = kwargs.pop("""sampling_rate""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = kwargs.pop("""text""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Any = args[0]
UpperCamelCase : str = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
UpperCamelCase : Optional[int] = self.tokenizer(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is not None:
UpperCamelCase : str = self.feature_extractor(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , sampling_rate=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
UpperCamelCase : int = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
UpperCamelCase : Optional[Any] = audio_inputs["""padding_mask"""]
return inputs
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = kwargs.pop("""audio""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = kwargs.pop("""padding_mask""" , SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : Optional[int] = args[0]
UpperCamelCase : Any = args[1:]
if audio_values is not None:
return self._decode_audio(SCREAMING_SNAKE_CASE_ , padding_mask=SCREAMING_SNAKE_CASE_ )
else:
return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = to_numpy(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase , UpperCamelCase : int = audio_values.shape
if padding_mask is None:
return list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = to_numpy(SCREAMING_SNAKE_CASE_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
UpperCamelCase : List[str] = seq_len - padding_mask.shape[-1]
UpperCamelCase : Optional[int] = 1 - self.feature_extractor.padding_value
UpperCamelCase : Any = np.pad(SCREAMING_SNAKE_CASE_ , ((0, 0), (0, difference)) , """constant""" , constant_values=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = audio_values.tolist()
for i in range(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
UpperCamelCase : Optional[Any] = sliced_audio.reshape(SCREAMING_SNAKE_CASE_ , -1 )
return audio_values
| 27
| 1
|
"""simple docstring"""
from __future__ import annotations
def A_ ( snake_case_ : list[int | str] ):
'''simple docstring'''
create_state_space_tree(snake_case_ ,[] ,0 ,[0 for i in range(len(snake_case_ ) )] )
def A_ ( snake_case_ : list[int | str] ,snake_case_ : list[int | str] ,snake_case_ : int ,snake_case_ : list[int] ,):
'''simple docstring'''
if index == len(snake_case_ ):
print(snake_case_ )
return
for i in range(len(snake_case_ ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
UpperCamelCase : Optional[int] = True
create_state_space_tree(snake_case_ ,snake_case_ ,index + 1 ,snake_case_ )
current_sequence.pop()
UpperCamelCase : List[str] = False
__A : list[int | str] = [3, 1, 2, 4]
generate_all_permutations(sequence)
__A : list[int | str] = ["A", "B", "C"]
generate_all_permutations(sequence_a)
| 27
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def A_ ( snake_case_ : str = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
UpperCamelCase : Any = BeautifulSoup(requests.get(snake_case_ ).text ,"""html.parser""" )
UpperCamelCase : Optional[int] = soup.findAll("""h1""" )
UpperCamelCase : List[Any] = soup.findAll("""div""" ,{"""class""": """maincounter-number"""} )
keys += soup.findAll("""span""" ,{"""class""": """panel-title"""} )
values += soup.findAll("""div""" ,{"""class""": """number-table-main"""} )
return {key.text.strip(): value.text.strip() for key, value in zip(snake_case_ ,snake_case_ )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(F'''{key}\n{value}\n''')
| 27
| 1
|
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27
|
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=1 , ):
UpperCamelCase : Tuple = parent
UpperCamelCase : Optional[int] = batch_size
UpperCamelCase : Optional[Any] = seq_length
UpperCamelCase : int = is_training
UpperCamelCase : Union[str, Any] = use_input_mask
UpperCamelCase : Union[str, Any] = use_token_type_ids
UpperCamelCase : Dict = use_labels
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Tuple = num_hidden_layers
UpperCamelCase : Any = num_attention_heads
UpperCamelCase : int = intermediate_size
UpperCamelCase : str = hidden_act
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Optional[Any] = type_vocab_size
UpperCamelCase : int = type_sequence_label_size
UpperCamelCase : Dict = initializer_range
UpperCamelCase : Dict = num_labels
UpperCamelCase : Tuple = num_choices
UpperCamelCase : Optional[int] = scope
UpperCamelCase : List[Any] = q_groups
UpperCamelCase : Tuple = k_groups
UpperCamelCase : Any = v_groups
UpperCamelCase : List[str] = post_attention_groups
UpperCamelCase : Tuple = intermediate_groups
UpperCamelCase : int = output_groups
def a_ ( self ):
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Tuple = None
if self.use_input_mask:
UpperCamelCase : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
UpperCamelCase : Dict = None
if self.use_labels:
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Tuple = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = SqueezeBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = SqueezeBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = SqueezeBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = self.num_labels
UpperCamelCase : str = SqueezeBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = self.num_choices
UpperCamelCase : Tuple = SqueezeBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Union[str, Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
((UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase) , (UpperCamelCase)) : Optional[int] = config_and_inputs
UpperCamelCase : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
lowercase : Dict = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : Dict = False
lowercase : str = True
lowercase : str = False
def a_ ( self ):
UpperCamelCase : Any = SqueezeBertModelTester(self )
UpperCamelCase : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Optional[Any] = SqueezeBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
UpperCamelCase : Dict = torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]] )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = torch.Size((1, 3) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
| 1
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__A : List[Any] = logging.get_logger(__name__)
__A : Any = torch.device('''cpu''')
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCamelCase : List[str] = Image.open(requests.get(snake_case_ ,stream=snake_case_ ).raw )
return im
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02] )
def A_ ( snake_case_ : str ,snake_case_ : Tuple ,snake_case_ : Any ):
'''simple docstring'''
UpperCamelCase : Any = dct.pop(snake_case_ )
UpperCamelCase : List[Any] = val
def A_ ( snake_case_ : Dict ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = []
for k in state_dict.keys():
UpperCamelCase : int = k
if ".pwconv" in k:
UpperCamelCase : Optional[int] = k_new.replace(""".pwconv""" ,""".point_wise_conv""" )
if ".dwconv" in k:
UpperCamelCase : Optional[Any] = k_new.replace(""".dwconv""" ,""".depth_wise_conv""" )
if ".Proj." in k:
UpperCamelCase : List[str] = k_new.replace(""".Proj.""" ,""".proj.""" )
if "patch_embed" in k_new:
UpperCamelCase : List[str] = k_new.replace("""patch_embed""" ,"""swiftformer.patch_embed.patch_embedding""" )
if "network" in k_new:
UpperCamelCase : List[str] = k_new.split(""".""" )
if ls[2].isdigit():
UpperCamelCase : List[str] = """swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:] )
else:
UpperCamelCase : Dict = k_new.replace("""network""" ,"""swiftformer.encoder.network""" )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def A_ ( snake_case_ : Optional[Any] ,snake_case_ : Any ,snake_case_ : str ):
'''simple docstring'''
UpperCamelCase : Any = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
UpperCamelCase : Union[str, Any] = 1_0_0_0
UpperCamelCase : Union[str, Any] = """huggingface/label-files"""
UpperCamelCase : Any = """imagenet-1k-id2label.json"""
UpperCamelCase : Optional[Any] = json.load(open(hf_hub_download(snake_case_ ,snake_case_ ,repo_type="""dataset""" ) ,"""r""" ) )
UpperCamelCase : Union[str, Any] = {int(snake_case_ ): v for k, v in idalabel.items()}
UpperCamelCase : Dict = idalabel
UpperCamelCase : Any = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
UpperCamelCase : str = [3, 3, 6, 4]
UpperCamelCase : str = [4_8, 5_6, 1_1_2, 2_2_0]
elif swiftformer_name == "swiftformer_s":
UpperCamelCase : Any = [3, 3, 9, 6]
UpperCamelCase : Optional[int] = [4_8, 6_4, 1_6_8, 2_2_4]
elif swiftformer_name == "swiftformer_l1":
UpperCamelCase : str = [4, 3, 1_0, 5]
UpperCamelCase : List[str] = [4_8, 9_6, 1_9_2, 3_8_4]
elif swiftformer_name == "swiftformer_l3":
UpperCamelCase : Any = [4, 4, 1_2, 6]
UpperCamelCase : Tuple = [6_4, 1_2_8, 3_2_0, 5_1_2]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith("""https""" ):
UpperCamelCase : int = torch.hub.load_state_dict_from_url(snake_case_ ,map_location="""cpu""" ,check_hash=snake_case_ )
else:
UpperCamelCase : Tuple = torch.load(snake_case_ ,map_location="""cpu""" )
UpperCamelCase : Optional[int] = checkpoint
UpperCamelCase : Dict = create_rename_keys(snake_case_ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(snake_case_ ,snake_case_ ,snake_case_ )
# load HuggingFace model
UpperCamelCase : Union[str, Any] = SwiftFormerForImageClassification(snake_case_ ).eval()
hf_model.load_state_dict(snake_case_ )
# prepare test inputs
UpperCamelCase : Dict = prepare_img()
UpperCamelCase : List[Any] = ViTImageProcessor.from_pretrained("""preprocessor_config""" )
UpperCamelCase : Tuple = processor(images=snake_case_ ,return_tensors="""pt""" )
# compare outputs from both models
UpperCamelCase : Tuple = get_expected_output(snake_case_ )
UpperCamelCase : List[str] = hf_model(inputs["""pixel_values"""] ).logits
assert hf_logits.shape == torch.Size([1, 1_0_0_0] )
assert torch.allclose(hf_logits[0, 0:5] ,snake_case_ ,atol=1e-3 )
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}' )
hf_model.save_pretrained(snake_case_ )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swiftformer_name''',
default='''swiftformer_xs''',
choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
type=str,
help='''Name of the SwiftFormer model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''./converted_outputs/''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')
__A : List[Any] = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 27
|
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase ( nn.Module ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 32 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "geglu" , SCREAMING_SNAKE_CASE_ = None , ):
super().__init__()
UpperCamelCase : int = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE_ , attention_head_dim=SCREAMING_SNAKE_CASE_ , in_channels=SCREAMING_SNAKE_CASE_ , num_layers=SCREAMING_SNAKE_CASE_ , dropout=SCREAMING_SNAKE_CASE_ , norm_num_groups=SCREAMING_SNAKE_CASE_ , cross_attention_dim=SCREAMING_SNAKE_CASE_ , attention_bias=SCREAMING_SNAKE_CASE_ , sample_size=SCREAMING_SNAKE_CASE_ , num_vector_embeds=SCREAMING_SNAKE_CASE_ , activation_fn=SCREAMING_SNAKE_CASE_ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE_ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
UpperCamelCase : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
UpperCamelCase : List[Any] = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
UpperCamelCase : int = [1, 0]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Dict = hidden_states
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
UpperCamelCase : Optional[int] = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
UpperCamelCase : str = self.transformer_index_for_condition[i]
UpperCamelCase : Any = self.transformers[transformer_index](
SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , timestep=SCREAMING_SNAKE_CASE_ , cross_attention_kwargs=SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
UpperCamelCase : Any = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
UpperCamelCase : List[str] = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : List[str] = {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
'''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
'''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = 'big_bird'
def __init__( self , SCREAMING_SNAKE_CASE_=5_0358 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu_new" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-12 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=66 , SCREAMING_SNAKE_CASE_="block_sparse" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , sep_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : List[Any] = vocab_size
UpperCamelCase : Optional[int] = max_position_embeddings
UpperCamelCase : List[Any] = hidden_size
UpperCamelCase : Any = num_hidden_layers
UpperCamelCase : List[Any] = num_attention_heads
UpperCamelCase : Any = intermediate_size
UpperCamelCase : Dict = hidden_act
UpperCamelCase : Any = hidden_dropout_prob
UpperCamelCase : int = attention_probs_dropout_prob
UpperCamelCase : List[str] = initializer_range
UpperCamelCase : str = type_vocab_size
UpperCamelCase : str = layer_norm_eps
UpperCamelCase : Dict = use_cache
UpperCamelCase : List[Any] = rescale_embeddings
UpperCamelCase : List[Any] = attention_type
UpperCamelCase : List[Any] = use_bias
UpperCamelCase : List[Any] = block_size
UpperCamelCase : Any = num_random_blocks
UpperCamelCase : List[str] = classifier_dropout
class lowerCamelCase ( _UpperCAmelCase ):
@property
def a_ ( self ):
if self.task == "multiple-choice":
UpperCamelCase : Dict = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
UpperCamelCase : Dict = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 27
|
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[int] = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Optional[int] = 'mvp'
lowercase : Optional[Any] = ['past_key_values']
lowercase : Union[str, Any] = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self , SCREAMING_SNAKE_CASE_=5_0267 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=4096 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=800 , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = vocab_size
UpperCamelCase : Dict = max_position_embeddings
UpperCamelCase : Optional[int] = d_model
UpperCamelCase : Optional[Any] = encoder_ffn_dim
UpperCamelCase : Any = encoder_layers
UpperCamelCase : List[Any] = encoder_attention_heads
UpperCamelCase : Optional[Any] = decoder_ffn_dim
UpperCamelCase : Optional[int] = decoder_layers
UpperCamelCase : Dict = decoder_attention_heads
UpperCamelCase : List[str] = dropout
UpperCamelCase : List[str] = attention_dropout
UpperCamelCase : List[Any] = activation_dropout
UpperCamelCase : Dict = activation_function
UpperCamelCase : List[str] = init_std
UpperCamelCase : int = encoder_layerdrop
UpperCamelCase : Dict = decoder_layerdrop
UpperCamelCase : Any = classifier_dropout
UpperCamelCase : Tuple = use_cache
UpperCamelCase : Dict = encoder_layers
UpperCamelCase : Tuple = scale_embedding # scale factor will be sqrt(d_model) if True
UpperCamelCase : Optional[Any] = use_prompt
UpperCamelCase : Any = prompt_length
UpperCamelCase : List[Any] = prompt_mid_dim
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , is_encoder_decoder=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , forced_eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated""" , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.bos_token_id
warnings.warn(
f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
"""The config can simply be saved and uploaded again to be fixed.""" )
| 27
| 1
|
"""simple docstring"""
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def A_ ( snake_case_ : Tuple ):
'''simple docstring'''
return {key.lstrip("""-""" ): value for key, value in zip(unknown_args[::2] ,unknown_args[1::2] )}
def A_ ( ):
'''simple docstring'''
UpperCamelCase : Optional[int] = ArgumentParser(
"""HuggingFace Datasets CLI tool""" ,usage="""datasets-cli <command> [<args>]""" ,allow_abbrev=snake_case_ )
UpperCamelCase : Optional[int] = parser.add_subparsers(help="""datasets-cli command helpers""" )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(snake_case_ )
EnvironmentCommand.register_subcommand(snake_case_ )
TestCommand.register_subcommand(snake_case_ )
RunBeamCommand.register_subcommand(snake_case_ )
DummyDataCommand.register_subcommand(snake_case_ )
# Parse args
UpperCamelCase , UpperCamelCase : List[Any] = parser.parse_known_args()
if not hasattr(snake_case_ ,"""func""" ):
parser.print_help()
exit(1 )
UpperCamelCase : Any = parse_unknown_args(snake_case_ )
# Run
UpperCamelCase : Any = args.func(snake_case_ ,**snake_case_ )
service.run()
if __name__ == "__main__":
main()
| 27
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__A : Optional[Any] = 16
__A : str = 32
def A_ ( snake_case_ : Accelerator ,snake_case_ : int = 1_6 ):
'''simple docstring'''
UpperCamelCase : Tuple = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase : Optional[int] = load_dataset("""glue""" ,"""mrpc""" )
def tokenize_function(snake_case_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase : Union[str, Any] = tokenizer(examples["""sentence1"""] ,examples["""sentence2"""] ,truncation=snake_case_ ,max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
UpperCamelCase : Optional[Any] = datasets.map(
snake_case_ ,batched=snake_case_ ,remove_columns=["""idx""", """sentence1""", """sentence2"""] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCamelCase : str = tokenized_datasets.rename_column("""label""" ,"""labels""" )
def collate_fn(snake_case_ : Any ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase : Union[str, Any] = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
UpperCamelCase : Optional[Any] = 1_6
elif accelerator.mixed_precision != "no":
UpperCamelCase : Any = 8
else:
UpperCamelCase : Optional[Any] = None
return tokenizer.pad(
snake_case_ ,padding="""longest""" ,max_length=snake_case_ ,pad_to_multiple_of=snake_case_ ,return_tensors="""pt""" ,)
# Instantiate dataloaders.
UpperCamelCase : str = DataLoader(
tokenized_datasets["""train"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
UpperCamelCase : Dict = DataLoader(
tokenized_datasets["""validation"""] ,shuffle=snake_case_ ,collate_fn=snake_case_ ,batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__A : int = mocked_dataloaders # noqa: F811
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ):
'''simple docstring'''
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,snake_case_ ) == "1":
UpperCamelCase : Union[str, Any] = 2
# New Code #
UpperCamelCase : Dict = int(args.gradient_accumulation_steps )
UpperCamelCase : List[Any] = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase : str = Accelerator(
cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase : Union[str, Any] = config["""lr"""]
UpperCamelCase : int = int(config["""num_epochs"""] )
UpperCamelCase : int = int(config["""seed"""] )
UpperCamelCase : List[Any] = int(config["""batch_size"""] )
UpperCamelCase : Optional[int] = evaluate.load("""glue""" ,"""mrpc""" )
set_seed(snake_case_ )
UpperCamelCase , UpperCamelCase : Dict = get_dataloaders(snake_case_ ,snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" ,return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
UpperCamelCase : Tuple = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase : List[Any] = AdamW(params=model.parameters() ,lr=snake_case_ )
# Instantiate scheduler
UpperCamelCase : str = get_linear_schedule_with_warmup(
optimizer=snake_case_ ,num_warmup_steps=1_0_0 ,num_training_steps=(len(snake_case_ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase : Any = accelerator.prepare(
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ ,model=snake_case_ ,local_sgd_steps=snake_case_ ,enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
UpperCamelCase : Optional[Any] = model(**snake_case_ )
UpperCamelCase : Optional[int] = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase : Any = model(**snake_case_ )
UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase : int = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ ,references=snake_case_ ,)
UpperCamelCase : str = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' ,snake_case_ )
def A_ ( ):
'''simple docstring'''
UpperCamelCase : str = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" ,type=snake_case_ ,default=snake_case_ ,choices=["""no""", """fp16""", """bf16""", """fp8"""] ,help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" ,)
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" ,type=snake_case_ ,default=1 ,help="""The number of minibatches to be ran before gradients are accumulated.""" ,)
parser.add_argument(
"""--local_sgd_steps""" ,type=snake_case_ ,default=8 ,help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" ,action="""store_true""" ,help="""If passed, will train on the CPU.""" )
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : List[Any] = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
training_function(snake_case_ ,snake_case_ )
if __name__ == "__main__":
main()
| 27
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
__A : Dict = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
__A : List[str] = TaTokenizerFast
__A : str = {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : int = [
'''MT5EncoderModel''',
'''MT5ForConditionalGeneration''',
'''MT5ForQuestionAnswering''',
'''MT5Model''',
'''MT5PreTrainedModel''',
'''MT5Stack''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Dict = ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Any = ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
__A : Any = _LazyModule(
__name__,
globals()['''__file__'''],
_import_structure,
extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
module_spec=__spec__,
)
| 27
|
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A : Any = logging.get_logger(__name__)
__A : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A : Optional[Any] = {
'''vocab_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
},
'''merges_file''': {
'''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
},
}
__A : Any = {'''allegro/herbert-base-cased''': 514}
__A : Optional[Any] = {}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : Dict = VOCAB_FILES_NAMES
lowercase : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase : List[str] = PRETRAINED_INIT_CONFIGURATION
lowercase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase : Union[str, Any] = HerbertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_="<s>" , SCREAMING_SNAKE_CASE_="<unk>" , SCREAMING_SNAKE_CASE_="<pad>" , SCREAMING_SNAKE_CASE_="<mask>" , SCREAMING_SNAKE_CASE_="</s>" , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer_file=SCREAMING_SNAKE_CASE_ , cls_token=SCREAMING_SNAKE_CASE_ , unk_token=SCREAMING_SNAKE_CASE_ , pad_token=SCREAMING_SNAKE_CASE_ , mask_token=SCREAMING_SNAKE_CASE_ , sep_token=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Dict = [self.cls_token_id]
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE_ , token_ids_a=SCREAMING_SNAKE_CASE_ , already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Tuple = [self.sep_token_id]
UpperCamelCase : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : Optional[int] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Any = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : List[Any] = 'umt5'
lowercase : Union[str, Any] = ['past_key_values']
def __init__( self , SCREAMING_SNAKE_CASE_=25_0112 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=64 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=6 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=128 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=1e-6 , SCREAMING_SNAKE_CASE_=1.0 , SCREAMING_SNAKE_CASE_="gated-gelu" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_="T5Tokenizer" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=0 , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(
is_encoder_decoder=SCREAMING_SNAKE_CASE_ , tokenizer_class=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , decoder_start_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Optional[int] = vocab_size
UpperCamelCase : Union[str, Any] = d_model
UpperCamelCase : List[Any] = d_kv
UpperCamelCase : List[str] = d_ff
UpperCamelCase : Optional[int] = num_layers
UpperCamelCase : Union[str, Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
UpperCamelCase : Tuple = num_heads
UpperCamelCase : Tuple = relative_attention_num_buckets
UpperCamelCase : Union[str, Any] = relative_attention_max_distance
UpperCamelCase : Tuple = dropout_rate
UpperCamelCase : List[str] = layer_norm_epsilon
UpperCamelCase : List[Any] = initializer_factor
UpperCamelCase : int = feed_forward_proj
UpperCamelCase : Any = use_cache
UpperCamelCase : Any = self.feed_forward_proj.split("""-""" )
UpperCamelCase : Optional[int] = act_info[-1]
UpperCamelCase : Union[str, Any] = act_info[0] == """gated"""
if len(SCREAMING_SNAKE_CASE_ ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_ ) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
if feed_forward_proj == "gated-gelu":
UpperCamelCase : List[str] = """gelu_new"""
@property
def a_ ( self ):
return self.d_model
@property
def a_ ( self ):
return self.num_heads
@property
def a_ ( self ):
return self.num_layers
class lowerCamelCase ( _UpperCAmelCase ):
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def a_ ( self ):
UpperCamelCase : Any = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
UpperCamelCase : Tuple = """past_encoder_sequence + sequence"""
UpperCamelCase : Optional[Any] = {0: """batch"""}
UpperCamelCase : str = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
UpperCamelCase : Dict = {0: """batch""", 1: """decoder_sequence"""}
UpperCamelCase : int = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_ , direction="""inputs""" )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def a_ ( self ):
return 13
@property
def a_ ( self ):
return 5e-4
| 27
|
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=1024 , SCREAMING_SNAKE_CASE_=3.6 ):
UpperCamelCase : Dict = tokenizer
UpperCamelCase : Optional[Any] = tokenizer.bos_token_id
UpperCamelCase : Any = dataset
UpperCamelCase : List[str] = seq_length
UpperCamelCase : Optional[Any] = seq_length * chars_per_token * num_of_sequences
def __iter__( self ):
UpperCamelCase : Dict = iter(self.dataset )
UpperCamelCase : Union[str, Any] = True
while more_examples:
UpperCamelCase , UpperCamelCase : Tuple = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(SCREAMING_SNAKE_CASE_ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
UpperCamelCase : Dict = False
break
UpperCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
UpperCamelCase : str = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , self.seq_length ):
UpperCamelCase : List[str] = all_token_ids[i : i + self.seq_length]
if len(SCREAMING_SNAKE_CASE_ ) == self.seq_length:
yield torch.tensor(SCREAMING_SNAKE_CASE_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
UpperCamelCase : Dict = {"""streaming""": True}
UpperCamelCase : Optional[int] = load_dataset(args.dataset_name ,split="""train""" ,**snake_case_ )
UpperCamelCase : Optional[int] = ConstantLengthDataset(snake_case_ ,snake_case_ ,seq_length=args.seq_length )
UpperCamelCase : List[Any] = DataLoader(snake_case_ ,batch_size=args.batch_size )
return eval_dataloader
def A_ ( snake_case_ : Optional[Any] ):
'''simple docstring'''
model.eval()
UpperCamelCase : Dict = []
for step, batch in enumerate(snake_case_ ):
with torch.no_grad():
UpperCamelCase : List[Any] = model(snake_case_ ,labels=snake_case_ )
UpperCamelCase : Any = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(snake_case_ ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
UpperCamelCase : Dict = torch.mean(torch.cat(snake_case_ ) )
try:
UpperCamelCase : Dict = torch.exp(snake_case_ )
except OverflowError:
UpperCamelCase : Optional[int] = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
__A : List[Any] = Accelerator()
# Parse configuration
__A : str = HfArgumentParser(EvaluationArguments)
__A : List[Any] = parser.parse_args()
set_seed(args.seed)
# Logging
__A : Any = logging.getLogger(__name__)
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
# Load model and tokenizer
__A : List[Any] = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__A : List[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
__A : int = create_dataloader(args)
# Prepare everything with our `accelerator`.
__A , __A : Optional[Any] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__A , __A : Tuple = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 27
| 1
|
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Any = CpmAntTokenizer
lowercase : Optional[Any] = False
def a_ ( self ):
super().setUp()
UpperCamelCase : Union[str, Any] = [
"""<d>""",
"""</d>""",
"""<s>""",
"""</s>""",
"""</_>""",
"""<unk>""",
"""<pad>""",
"""</n>""",
"""我""",
"""是""",
"""C""",
"""P""",
"""M""",
"""A""",
"""n""",
"""t""",
]
UpperCamelCase : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
@tooslow
def a_ ( self ):
UpperCamelCase : Optional[int] = CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b""" )
UpperCamelCase : Union[str, Any] = """今天天气真好！"""
UpperCamelCase : int = ["""今天""", """天气""", """真""", """好""", """！"""]
UpperCamelCase : Optional[Any] = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = """今天天气真好！"""
UpperCamelCase : List[Any] = [tokenizer.bos_token] + tokens
UpperCamelCase : Dict = [6, 9802, 1_4962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
| 27
|
"""simple docstring"""
import argparse
import os
import re
__A : Any = '''src/transformers'''
# Pattern that looks at the indentation in a line.
__A : Tuple = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__A : List[Any] = re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__A : Dict = re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__A : List[str] = re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__A : List[Any] = re.compile(R'''\[([^\]]+)\]''')
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : Any = _re_indent.search(snake_case_ )
return "" if search is None else search.groups()[0]
def A_ ( snake_case_ : str ,snake_case_ : str="" ,snake_case_ : Any=None ,snake_case_ : Union[str, Any]=None ):
'''simple docstring'''
UpperCamelCase : List[Any] = 0
UpperCamelCase : Optional[int] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(snake_case_ ):
index += 1
UpperCamelCase : Tuple = ["""\n""".join(lines[:index] )]
else:
UpperCamelCase : Tuple = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
UpperCamelCase : Dict = [lines[index]]
index += 1
while index < len(snake_case_ ) and (end_prompt is None or not lines[index].startswith(snake_case_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(snake_case_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(snake_case_ ) )
if index < len(snake_case_ ) - 1:
UpperCamelCase : Optional[Any] = [lines[index + 1]]
index += 1
else:
UpperCamelCase : str = []
else:
blocks.append("""\n""".join(snake_case_ ) )
UpperCamelCase : int = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(snake_case_ ) > 0:
blocks.append("""\n""".join(snake_case_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(snake_case_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
def _inner(snake_case_ : List[str] ):
return key(snake_case_ ).lower().replace("""_""" ,"""""" )
return _inner
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : Tuple=None ):
'''simple docstring'''
# If no key is provided, we use a noop.
def noop(snake_case_ : Optional[int] ):
return x
if key is None:
UpperCamelCase : List[str] = noop
# Constants are all uppercase, they go first.
UpperCamelCase : List[str] = [obj for obj in objects if key(snake_case_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
UpperCamelCase : Tuple = [obj for obj in objects if key(snake_case_ )[0].isupper() and not key(snake_case_ ).isupper()]
# Functions begin with a lowercase, they go last.
UpperCamelCase : int = [obj for obj in objects if not key(snake_case_ )[0].isupper()]
UpperCamelCase : Union[str, Any] = ignore_underscore(snake_case_ )
return sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ ) + sorted(snake_case_ ,key=snake_case_ )
def A_ ( snake_case_ : List[Any] ):
'''simple docstring'''
# This inner function sort imports between [ ].
def _replace(snake_case_ : Any ):
UpperCamelCase : Union[str, Any] = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
UpperCamelCase : int = [part.strip().replace("""\"""" ,"""""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : str = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(snake_case_ )] ) + "]"
UpperCamelCase : Optional[int] = import_statement.split("""\n""" )
if len(snake_case_ ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
UpperCamelCase : int = 2 if lines[1].strip() == """[""" else 1
UpperCamelCase : Tuple = [(i, _re_strip_line.search(snake_case_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
UpperCamelCase : List[Any] = sort_objects(snake_case_ ,key=lambda snake_case_ : x[1] )
UpperCamelCase : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(snake_case_ ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
UpperCamelCase : List[str] = _re_bracket_content.sub(_replace ,lines[1] )
else:
UpperCamelCase : List[Any] = [part.strip().replace("""\"""" ,"""""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
UpperCamelCase : Optional[int] = keys[:-1]
UpperCamelCase : Union[str, Any] = get_indent(lines[1] ) + """, """.join([f'"{k}"' for k in sort_objects(snake_case_ )] )
return "\n".join(snake_case_ )
else:
# Finally we have to deal with imports fitting on one line
UpperCamelCase : Any = _re_bracket_content.sub(_replace ,snake_case_ )
return import_statement
def A_ ( snake_case_ : Union[str, Any] ,snake_case_ : int=True ):
'''simple docstring'''
with open(snake_case_ ,encoding="""utf-8""" ) as f:
UpperCamelCase : List[str] = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
UpperCamelCase : int = split_code_in_indented_blocks(
snake_case_ ,start_prompt="""_import_structure = {""" ,end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 ,len(snake_case_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
UpperCamelCase : Dict = main_blocks[block_idx]
UpperCamelCase : Dict = block.split("""\n""" )
# Get to the start of the imports.
UpperCamelCase : List[str] = 0
while line_idx < len(snake_case_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
UpperCamelCase : Optional[Any] = len(snake_case_ )
else:
line_idx += 1
if line_idx >= len(snake_case_ ):
continue
# Ignore beginning and last line: they don't contain anything.
UpperCamelCase : Optional[Any] = """\n""".join(block_lines[line_idx:-1] )
UpperCamelCase : Any = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
UpperCamelCase : List[Any] = split_code_in_indented_blocks(snake_case_ ,indent_level=snake_case_ )
# We have two categories of import key: list or _import_structure[key].append/extend
UpperCamelCase : Optional[Any] = _re_direct_key if """_import_structure = {""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
UpperCamelCase : Optional[Any] = [(pattern.search(snake_case_ ).groups()[0] if pattern.search(snake_case_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
UpperCamelCase : Any = [(i, key) for i, key in enumerate(snake_case_ ) if key is not None]
UpperCamelCase : Union[str, Any] = [x[0] for x in sorted(snake_case_ ,key=lambda snake_case_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
UpperCamelCase : str = 0
UpperCamelCase : List[str] = []
for i in range(len(snake_case_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
UpperCamelCase : Optional[int] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(snake_case_ )
count += 1
# And we put our main block back together with its first and last line.
UpperCamelCase : Tuple = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(snake_case_ ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(snake_case_ ,"""w""" ,encoding="""utf-8""" ) as f:
f.write("""\n""".join(snake_case_ ) )
def A_ ( snake_case_ : int=True ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = []
for root, _, files in os.walk(snake_case_ ):
if "__init__.py" in files:
UpperCamelCase : Optional[int] = sort_imports(os.path.join(snake_case_ ,"""__init__.py""" ) ,check_only=snake_case_ )
if result:
UpperCamelCase : List[Any] = [os.path.join(snake_case_ ,"""__init__.py""" )]
if len(snake_case_ ) > 0:
raise ValueError(f'Would overwrite {len(snake_case_ )} files, run `make style`.' )
if __name__ == "__main__":
__A : Optional[int] = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
__A : Union[str, Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 27
| 1
|
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__A : str = logging.get_logger(__name__)
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ):
if not conversation_id:
UpperCamelCase : Optional[int] = uuid.uuida()
if past_user_inputs is None:
UpperCamelCase : Any = []
if generated_responses is None:
UpperCamelCase : List[str] = []
UpperCamelCase : uuid.UUID = conversation_id
UpperCamelCase : List[str] = past_user_inputs
UpperCamelCase : List[str] = generated_responses
UpperCamelCase : Optional[str] = text
def __eq__( self , SCREAMING_SNAKE_CASE_ ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False ):
if self.new_user_input:
if overwrite:
logger.warning(
f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
f'with: "{text}".' )
UpperCamelCase : Union[str, Any] = text
else:
logger.warning(
f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input' )
else:
UpperCamelCase : str = text
def a_ ( self ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
UpperCamelCase : Optional[int] = None
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
self.generated_responses.append(SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
UpperCamelCase : List[Any] = f'Conversation id: {self.uuid} \n'
for is_user, text in self.iter_texts():
UpperCamelCase : Dict = """user""" if is_user else """bot"""
output += f'{name} >> {text} \n'
return output
@add_end_docstrings(
_UpperCAmelCase , R'\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n ' , )
class lowerCamelCase ( _UpperCAmelCase ):
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
super().__init__(*SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if self.tokenizer.pad_token_id is None:
UpperCamelCase : str = self.tokenizer.eos_token
def a_ ( self , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = {}
UpperCamelCase : Optional[Any] = {}
UpperCamelCase : Tuple = {}
if min_length_for_response is not None:
UpperCamelCase : Union[str, Any] = min_length_for_response
if minimum_tokens is not None:
UpperCamelCase : Tuple = minimum_tokens
if "max_length" in generate_kwargs:
UpperCamelCase : Optional[int] = generate_kwargs["""max_length"""]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
UpperCamelCase : Optional[Any] = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(SCREAMING_SNAKE_CASE_ )
return preprocess_params, forward_params, postprocess_params
def __call__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = super().__call__(SCREAMING_SNAKE_CASE_ , num_workers=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and len(SCREAMING_SNAKE_CASE_ ) == 1:
return outputs[0]
return outputs
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=32 ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""ConversationalPipeline, expects Conversation as inputs""" )
if conversation.new_user_input is None:
raise ValueError(
f'Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '
"""Add user inputs with the conversation's `add_user_input` method""" )
if hasattr(self.tokenizer , """_build_conversation_input_ids""" ):
UpperCamelCase : int = self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE_ )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
UpperCamelCase : Dict = self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE_ )
if self.framework == "pt":
UpperCamelCase : List[str] = torch.LongTensor([input_ids] )
elif self.framework == "tf":
UpperCamelCase : Any = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=10 , **SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = generate_kwargs.get("""max_length""" , self.model.config.max_length )
UpperCamelCase : Optional[Any] = model_inputs["""input_ids"""].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})' )
UpperCamelCase : Any = max_length - minimum_tokens
UpperCamelCase : Union[str, Any] = model_inputs["""input_ids"""][:, -trim:]
if "attention_mask" in model_inputs:
UpperCamelCase : str = model_inputs["""attention_mask"""][:, -trim:]
UpperCamelCase : Union[str, Any] = model_inputs.pop("""conversation""" )
UpperCamelCase : int = max_length
UpperCamelCase : int = self.model.generate(**SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if self.model.config.is_encoder_decoder:
UpperCamelCase : List[str] = 1
else:
UpperCamelCase : Optional[int] = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=True ):
UpperCamelCase : Dict = model_outputs["""output_ids"""]
UpperCamelCase : Union[str, Any] = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Dict = model_outputs["""conversation"""]
conversation.mark_processed()
conversation.append_response(SCREAMING_SNAKE_CASE_ )
return conversation
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Dict = self.tokenizer.eos_token_id
UpperCamelCase : Union[str, Any] = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) > self.tokenizer.model_max_length:
UpperCamelCase : Optional[int] = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 27
|
"""simple docstring"""
def A_ ( snake_case_ : int ):
'''simple docstring'''
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=8 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=36 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=None , ):
UpperCamelCase : List[str] = parent
UpperCamelCase : Tuple = batch_size
UpperCamelCase : Tuple = seq_length
UpperCamelCase : str = is_training
UpperCamelCase : List[Any] = use_input_mask
UpperCamelCase : Optional[Any] = use_token_type_ids
UpperCamelCase : Any = use_labels
UpperCamelCase : Tuple = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : str = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : str = intermediate_size
UpperCamelCase : Optional[Any] = hidden_act
UpperCamelCase : Union[str, Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : List[Any] = max_position_embeddings
UpperCamelCase : Tuple = type_vocab_size
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : Optional[Any] = initializer_range
UpperCamelCase : Optional[int] = num_labels
UpperCamelCase : Optional[Any] = num_choices
UpperCamelCase : Any = scope
def a_ ( self ):
UpperCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Optional[int] = None
if self.use_input_mask:
UpperCamelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : List[Any] = None
if self.use_token_type_ids:
UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase : List[Any] = None
UpperCamelCase : List[str] = None
UpperCamelCase : Optional[Any] = None
if self.use_labels:
UpperCamelCase : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : int = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a_ ( self ):
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=SCREAMING_SNAKE_CASE_ , initializer_range=self.initializer_range , )
def a_ ( self ):
UpperCamelCase : List[str] = self.get_config()
UpperCamelCase : int = 300
return config
def a_ ( self ):
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Tuple = self.prepare_config_and_inputs()
UpperCamelCase : str = True
UpperCamelCase : Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = MraModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = True
UpperCamelCase : int = MraModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Tuple = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , encoder_attention_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , encoder_hidden_states=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Union[str, Any] = MraForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = MraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = self.num_labels
UpperCamelCase : Optional[int] = MraForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Any = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = self.num_labels
UpperCamelCase : int = MraForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[Any] = self.num_choices
UpperCamelCase : Union[str, Any] = MraForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[int] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : str = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : Any = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Any = config_and_inputs
UpperCamelCase : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowercase : Tuple = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase : List[str] = False
lowercase : List[Any] = False
lowercase : List[Any] = False
lowercase : Optional[int] = False
lowercase : List[Any] = ()
def a_ ( self ):
UpperCamelCase : List[str] = MraModelTester(self )
UpperCamelCase : str = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , hidden_size=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase : Optional[int] = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def a_ ( self ):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : Dict = MraModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason="""MRA does not output attentions""" )
def a_ ( self ):
return
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : List[str] = MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
UpperCamelCase : int = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : str = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = torch.tensor(
[[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
@slow
def a_ ( self ):
UpperCamelCase : Union[str, Any] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
UpperCamelCase : int = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = 5_0265
UpperCamelCase : Optional[int] = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = torch.tensor(
[[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
@slow
def a_ ( self ):
UpperCamelCase : Optional[Any] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
UpperCamelCase : List[str] = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCamelCase : Optional[Any] = 5_0265
UpperCamelCase : Any = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = torch.tensor(
[[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 27
|
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__A : Optional[Any] = logging.get_logger(__name__)
def A_ ( snake_case_ : np.ndarray ,snake_case_ : Union[int, Iterable[int]] ,snake_case_ : bool ,snake_case_ : int ):
'''simple docstring'''
def constraint_to_multiple_of(snake_case_ : Optional[Any] ,snake_case_ : Optional[int] ,snake_case_ : List[str]=0 ,snake_case_ : Optional[Any]=None ):
UpperCamelCase : List[str] = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
UpperCamelCase : Optional[Any] = math.floor(val / multiple ) * multiple
if x < min_val:
UpperCamelCase : Dict = math.ceil(val / multiple ) * multiple
return x
UpperCamelCase : Any = (output_size, output_size) if isinstance(snake_case_ ,snake_case_ ) else output_size
UpperCamelCase , UpperCamelCase : int = get_image_size(snake_case_ )
UpperCamelCase , UpperCamelCase : Union[str, Any] = output_size
# determine new height and width
UpperCamelCase : List[str] = output_height / input_height
UpperCamelCase : List[str] = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
UpperCamelCase : int = scale_width
else:
# fit height
UpperCamelCase : Optional[Any] = scale_height
UpperCamelCase : int = constraint_to_multiple_of(scale_height * input_height ,multiple=snake_case_ )
UpperCamelCase : Union[str, Any] = constraint_to_multiple_of(scale_width * input_width ,multiple=snake_case_ )
return (new_height, new_width)
class lowerCamelCase ( _UpperCAmelCase ):
lowercase : str = ['pixel_values']
def __init__( self , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = 1 / 255 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = size if size is not None else {"""height""": 384, """width""": 384}
UpperCamelCase : List[Any] = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = do_resize
UpperCamelCase : Union[str, Any] = size
UpperCamelCase : Union[str, Any] = keep_aspect_ratio
UpperCamelCase : Any = ensure_multiple_of
UpperCamelCase : List[Any] = resample
UpperCamelCase : str = do_rescale
UpperCamelCase : Optional[Any] = rescale_factor
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCamelCase : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Tuple = get_size_dict(SCREAMING_SNAKE_CASE_ )
if "height" not in size or "width" not in size:
raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
UpperCamelCase : Dict = get_resize_output_image_size(
SCREAMING_SNAKE_CASE_ , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=SCREAMING_SNAKE_CASE_ , multiple=SCREAMING_SNAKE_CASE_ , )
return resize(SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return rescale(SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , **SCREAMING_SNAKE_CASE_ , ):
return normalize(SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ , data_format=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCamelCase : List[Any] = size if size is not None else self.size
UpperCamelCase : Dict = get_size_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
UpperCamelCase : Optional[int] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
UpperCamelCase : Tuple = resample if resample is not None else self.resample
UpperCamelCase : str = do_rescale if do_rescale is not None else self.do_rescale
UpperCamelCase : List[str] = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCamelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
UpperCamelCase : Any = image_mean if image_mean is not None else self.image_mean
UpperCamelCase : List[Any] = image_std if image_std is not None else self.image_std
UpperCamelCase : str = make_list_of_images(SCREAMING_SNAKE_CASE_ )
if not valid_images(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
UpperCamelCase : Tuple = [to_numpy_array(SCREAMING_SNAKE_CASE_ ) for image in images]
if do_resize:
UpperCamelCase : Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE_ , size=SCREAMING_SNAKE_CASE_ , resample=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_rescale:
UpperCamelCase : int = [self.rescale(image=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ ) for image in images]
if do_normalize:
UpperCamelCase : List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE_ , mean=SCREAMING_SNAKE_CASE_ , std=SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Any = [to_channel_dimension_format(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for image in images]
UpperCamelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE_ , tensor_type=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ):
UpperCamelCase : str = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(SCREAMING_SNAKE_CASE_ ) != len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[Any] = target_sizes.numpy()
UpperCamelCase : Dict = []
for idx in range(len(SCREAMING_SNAKE_CASE_ ) ):
UpperCamelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : List[Any] = logits.argmax(dim=1 )
UpperCamelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 27
| 1
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27
|
"""simple docstring"""
from collections.abc import Callable
def A_ ( snake_case_ : Callable[[float], float] ,snake_case_ : float ,snake_case_ : float ):
'''simple docstring'''
UpperCamelCase : float = a
UpperCamelCase : float = b
if function(snake_case_ ) == 0: # one of the a or b is a root for the function
return a
elif function(snake_case_ ) == 0:
return b
elif (
function(snake_case_ ) * function(snake_case_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError("""could not find root in given interval.""" )
else:
UpperCamelCase : float = start + (end - start) / 2.0
while abs(start - mid ) > 1_0**-7: # until precisely equals to 10^-7
if function(snake_case_ ) == 0:
return mid
elif function(snake_case_ ) * function(snake_case_ ) < 0:
UpperCamelCase : Dict = mid
else:
UpperCamelCase : List[str] = mid
UpperCamelCase : Tuple = start + (end - start) / 2.0
return mid
def A_ ( snake_case_ : float ):
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 1000))
import doctest
doctest.testmod()
| 27
| 1
|
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class lowerCamelCase :
lowercase : List[str]
lowercase : Optional[str] = None
# Automatically constructed
lowercase : ClassVar[str] = "dict"
lowercase : ClassVar[Any] = None
lowercase : str = field(default='Translation' , init=_UpperCAmelCase , repr=_UpperCAmelCase )
def __call__( self ):
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def a_ ( self ):
from .features import Value
return {k: Value("""string""" ) for k in sorted(self.languages )}
@dataclass
class lowerCamelCase :
lowercase : Optional[List] = None
lowercase : Optional[int] = None
lowercase : Optional[str] = None
# Automatically constructed
lowercase : ClassVar[str] = "dict"
lowercase : ClassVar[Any] = None
lowercase : str = field(default='TranslationVariableLanguages' , init=_UpperCAmelCase , repr=_UpperCAmelCase )
def a_ ( self ):
UpperCamelCase : str = sorted(set(self.languages ) ) if self.languages else None
UpperCamelCase : Union[str, Any] = len(self.languages ) if self.languages else None
def __call__( self ):
return pa.struct({"""language""": pa.list_(pa.string() ), """translation""": pa.list_(pa.string() )} )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = set(self.languages )
if self.languages and set(SCREAMING_SNAKE_CASE_ ) - lang_set:
raise ValueError(
f'Some languages in example ({", ".join(sorted(set(SCREAMING_SNAKE_CASE_ ) - lang_set ) )}) are not in valid set ({", ".join(SCREAMING_SNAKE_CASE_ )}).' )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
UpperCamelCase : List[Any] = []
for lang, text in translation_dict.items():
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
UpperCamelCase , UpperCamelCase : Any = zip(*sorted(SCREAMING_SNAKE_CASE_ ) )
return {"language": languages, "translation": translations}
def a_ ( self ):
from .features import Sequence, Value
return {
"language": Sequence(Value("""string""" ) ),
"translation": Sequence(Value("""string""" ) ),
}
| 27
|
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase ( unittest.TestCase ):
def a_ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def a_ ( self ):
UpperCamelCase : Tuple = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
UpperCamelCase : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
UpperCamelCase : Dict = """xvjiarui/stable-diffusion-2-inpainting"""
UpperCamelCase , UpperCamelCase : List[str] = FlaxStableDiffusionInpaintPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = """Face of a yellow cat, high resolution, sitting on a park bench"""
UpperCamelCase : List[str] = jax.random.PRNGKey(0 )
UpperCamelCase : Tuple = 50
UpperCamelCase : Dict = jax.device_count()
UpperCamelCase : Optional[int] = num_samples * [prompt]
UpperCamelCase : int = num_samples * [init_image]
UpperCamelCase : List[Any] = num_samples * [mask_image]
UpperCamelCase , UpperCamelCase , UpperCamelCase : Optional[Any] = pipeline.prepare_inputs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# shard inputs and rng
UpperCamelCase : Optional[int] = replicate(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[int] = jax.random.split(SCREAMING_SNAKE_CASE_ , jax.device_count() )
UpperCamelCase : str = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = shard(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = pipeline(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , jit=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = output.images.reshape(SCREAMING_SNAKE_CASE_ , 512 , 512 , 3 )
UpperCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
UpperCamelCase : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
UpperCamelCase : Dict = jnp.array(
[0.3611307, 0.37649736, 0.3757408, 0.38213953, 0.39295167, 0.3841631, 0.41554978, 0.4137475, 0.4217084] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 27
| 1
|
"""simple docstring"""
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
__A : Any = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(42)
__A : int = {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}
__A : List[str] = '''zero2'''
__A : Dict = '''zero3'''
__A : Any = [ZEROa, ZEROa]
def A_ ( snake_case_ : Tuple ,snake_case_ : Dict ,snake_case_ : Optional[Any] ):
'''simple docstring'''
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
UpperCamelCase : List[str] = parameterized.to_safe_name("""_""".join(str(snake_case_ ) for x in param.args ) )
return f'{func.__name__}_{param_based_name}'
# Cartesian-product of zero stages with models to test
__A : Optional[Any] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class lowerCamelCase ( _UpperCAmelCase ):
@parameterized.expand(SCREAMING_SNAKE_CASE_ , name_func=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.run_and_check(
stage=SCREAMING_SNAKE_CASE_ , model=SCREAMING_SNAKE_CASE_ , distributed=SCREAMING_SNAKE_CASE_ , fpaa=SCREAMING_SNAKE_CASE_ , )
@require_torch_multi_gpu
@parameterized.expand(SCREAMING_SNAKE_CASE_ , name_func=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.run_and_check(
stage=SCREAMING_SNAKE_CASE_ , model=SCREAMING_SNAKE_CASE_ , distributed=SCREAMING_SNAKE_CASE_ , fpaa=SCREAMING_SNAKE_CASE_ , )
@parameterized.expand(SCREAMING_SNAKE_CASE_ , name_func=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.run_and_check(
stage=SCREAMING_SNAKE_CASE_ , model=SCREAMING_SNAKE_CASE_ , distributed=SCREAMING_SNAKE_CASE_ , fpaa=SCREAMING_SNAKE_CASE_ , )
@require_torch_multi_gpu
@parameterized.expand(SCREAMING_SNAKE_CASE_ , name_func=SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.run_and_check(
stage=SCREAMING_SNAKE_CASE_ , model=SCREAMING_SNAKE_CASE_ , distributed=SCREAMING_SNAKE_CASE_ , fpaa=SCREAMING_SNAKE_CASE_ , )
def a_ ( self , SCREAMING_SNAKE_CASE_ ):
# XXX: run_asr is premature and doesn't save any results
# so all we check for now is that the process didn't fail
pass
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 10 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : int = models[model]
UpperCamelCase : Optional[int] = self.run_trainer(
stage=SCREAMING_SNAKE_CASE_ , model_name=SCREAMING_SNAKE_CASE_ , eval_steps=SCREAMING_SNAKE_CASE_ , num_train_epochs=1 , distributed=SCREAMING_SNAKE_CASE_ , fpaa=SCREAMING_SNAKE_CASE_ , )
self.do_checks(SCREAMING_SNAKE_CASE_ )
return output_dir
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 10 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = True , ):
UpperCamelCase : Optional[int] = self.get_auto_remove_tmp_dir("""./xxx""" , after=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = f'\n --model_name_or_path {model_name}\n --dataset_name hf-internal-testing/librispeech_asr_dummy\n --dataset_config_name clean\n --train_split_name validation\n --validation_split_name validation\n --output_dir {output_dir}\n --num_train_epochs {str(SCREAMING_SNAKE_CASE_ )}\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 2\n --evaluation_strategy steps\n --learning_rate 5e-4\n --warmup_steps 8\n --orthography timit\n --preprocessing_num_workers 1\n --group_by_length\n --freeze_feature_extractor\n --report_to none\n --save_steps 0\n --eval_steps {eval_steps}\n --report_to none\n '.split()
if fpaa:
args.extend(["""--fp16"""] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
UpperCamelCase : int = f'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
UpperCamelCase : Union[str, Any] = [f'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
UpperCamelCase : Dict = self.get_launcher(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(SCREAMING_SNAKE_CASE_ , env=self.get_env() )
return output_dir
def a_ ( self , SCREAMING_SNAKE_CASE_=False ):
# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
# - it won't be able to handle that
# 2. for now testing with just 2 gpus max (since some quality tests may give different
# results with mode gpus because we use very little data)
UpperCamelCase : Optional[Any] = min(2 , get_gpu_count() ) if distributed else 1
return f'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()
| 27
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def A_ ( snake_case_ : int ): # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def A_ ( ):
'''simple docstring'''
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
UpperCamelCase : Optional[Any] = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ ,snake_case_ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" ,[2, -1] )
def A_ ( snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : List[Any] = [1, 2]
UpperCamelCase : List[Any] = {"""a""": 1, """b""": 2}
UpperCamelCase : List[str] = {"""a""": [1, 2], """b""": [3, 4]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 1}, """b""": 2}
UpperCamelCase : Any = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
UpperCamelCase : Optional[int] = [2, 3]
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3}
UpperCamelCase : Any = {"""a""": [2, 3], """b""": [4, 5]}
UpperCamelCase : Tuple = {"""a""": {"""1""": 2}, """b""": 3}
UpperCamelCase : List[str] = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ ,snake_case_ ,num_proc=snake_case_ ) == expected_map_nested_sa
| 27
| 1
|
"""simple docstring"""
import random
def A_ ( snake_case_ : int ):
'''simple docstring'''
UpperCamelCase : List[Any] = num - 1
UpperCamelCase : str = 0
while s % 2 == 0:
UpperCamelCase : List[str] = s // 2
t += 1
for _ in range(5 ):
UpperCamelCase : Optional[Any] = random.randrange(2 ,num - 1 )
UpperCamelCase : List[str] = pow(snake_case_ ,snake_case_ ,snake_case_ )
if v != 1:
UpperCamelCase : Tuple = 0
while v != (num - 1):
if i == t - 1:
return False
else:
UpperCamelCase : Tuple = i + 1
UpperCamelCase : str = (v**2) % num
return True
def A_ ( snake_case_ : int ):
'''simple docstring'''
if num < 2:
return False
UpperCamelCase : Dict = [
2,
3,
5,
7,
1_1,
1_3,
1_7,
1_9,
2_3,
2_9,
3_1,
3_7,
4_1,
4_3,
4_7,
5_3,
5_9,
6_1,
6_7,
7_1,
7_3,
7_9,
8_3,
8_9,
9_7,
1_0_1,
1_0_3,
1_0_7,
1_0_9,
1_1_3,
1_2_7,
1_3_1,
1_3_7,
1_3_9,
1_4_9,
1_5_1,
1_5_7,
1_6_3,
1_6_7,
1_7_3,
1_7_9,
1_8_1,
1_9_1,
1_9_3,
1_9_7,
1_9_9,
2_1_1,
2_2_3,
2_2_7,
2_2_9,
2_3_3,
2_3_9,
2_4_1,
2_5_1,
2_5_7,
2_6_3,
2_6_9,
2_7_1,
2_7_7,
2_8_1,
2_8_3,
2_9_3,
3_0_7,
3_1_1,
3_1_3,
3_1_7,
3_3_1,
3_3_7,
3_4_7,
3_4_9,
3_5_3,
3_5_9,
3_6_7,
3_7_3,
3_7_9,
3_8_3,
3_8_9,
3_9_7,
4_0_1,
4_0_9,
4_1_9,
4_2_1,
4_3_1,
4_3_3,
4_3_9,
4_4_3,
4_4_9,
4_5_7,
4_6_1,
4_6_3,
4_6_7,
4_7_9,
4_8_7,
4_9_1,
4_9_9,
5_0_3,
5_0_9,
5_2_1,
5_2_3,
5_4_1,
5_4_7,
5_5_7,
5_6_3,
5_6_9,
5_7_1,
5_7_7,
5_8_7,
5_9_3,
5_9_9,
6_0_1,
6_0_7,
6_1_3,
6_1_7,
6_1_9,
6_3_1,
6_4_1,
6_4_3,
6_4_7,
6_5_3,
6_5_9,
6_6_1,
6_7_3,
6_7_7,
6_8_3,
6_9_1,
7_0_1,
7_0_9,
7_1_9,
7_2_7,
7_3_3,
7_3_9,
7_4_3,
7_5_1,
7_5_7,
7_6_1,
7_6_9,
7_7_3,
7_8_7,
7_9_7,
8_0_9,
8_1_1,
8_2_1,
8_2_3,
8_2_7,
8_2_9,
8_3_9,
8_5_3,
8_5_7,
8_5_9,
8_6_3,
8_7_7,
8_8_1,
8_8_3,
8_8_7,
9_0_7,
9_1_1,
9_1_9,
9_2_9,
9_3_7,
9_4_1,
9_4_7,
9_5_3,
9_6_7,
9_7_1,
9_7_7,
9_8_3,
9_9_1,
9_9_7,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(snake_case_ )
def A_ ( snake_case_ : int = 1_0_2_4 ):
'''simple docstring'''
while True:
UpperCamelCase : Optional[int] = random.randrange(2 ** (keysize - 1) ,2 ** (keysize) )
if is_prime_low_num(snake_case_ ):
return num
if __name__ == "__main__":
__A : Dict = generate_large_prime()
print(('''Prime number:''', num))
print(('''is_prime_low_num:''', is_prime_low_num(num)))
| 27
|
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 , SCREAMING_SNAKE_CASE_=7 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=99 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=4 , SCREAMING_SNAKE_CASE_="last" , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=0 , ):
UpperCamelCase : Union[str, Any] = parent
UpperCamelCase : str = batch_size
UpperCamelCase : int = seq_length
UpperCamelCase : Optional[Any] = is_training
UpperCamelCase : Any = use_input_lengths
UpperCamelCase : Tuple = use_token_type_ids
UpperCamelCase : List[Any] = use_labels
UpperCamelCase : Union[str, Any] = gelu_activation
UpperCamelCase : Dict = sinusoidal_embeddings
UpperCamelCase : Optional[int] = causal
UpperCamelCase : List[Any] = asm
UpperCamelCase : int = n_langs
UpperCamelCase : Optional[Any] = vocab_size
UpperCamelCase : str = n_special
UpperCamelCase : Dict = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Optional[Any] = num_attention_heads
UpperCamelCase : Optional[Any] = hidden_dropout_prob
UpperCamelCase : str = attention_probs_dropout_prob
UpperCamelCase : int = max_position_embeddings
UpperCamelCase : Any = type_sequence_label_size
UpperCamelCase : str = initializer_range
UpperCamelCase : str = num_labels
UpperCamelCase : Union[str, Any] = num_choices
UpperCamelCase : List[str] = summary_type
UpperCamelCase : int = use_proj
UpperCamelCase : List[str] = scope
UpperCamelCase : Dict = bos_token_id
def a_ ( self ):
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Union[str, Any] = None
if self.use_input_lengths:
UpperCamelCase : str = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
UpperCamelCase : int = None
UpperCamelCase : Dict = None
UpperCamelCase : str = None
if self.use_labels:
UpperCamelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Dict = ids_tensor([self.batch_size] , 2 ).float()
UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : List[str] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a_ ( self ):
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[int] = XLMModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , lengths=SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = model(SCREAMING_SNAKE_CASE_ , langs=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Optional[Any] = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = model(SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[str] = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : List[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[Any] = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , p_mask=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase : Any = model(
SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ , cls_index=SCREAMING_SNAKE_CASE_ , is_impossible=SCREAMING_SNAKE_CASE_ , )
((UpperCamelCase) , ) : Union[str, Any] = result_with_labels.to_tuple()
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
((UpperCamelCase) , ) : Tuple = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : Union[str, Any] = XLMForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[Any] = model(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = model(SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : int = self.num_labels
UpperCamelCase : int = XLMForTokenClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Optional[int] = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase : List[Any] = self.num_choices
UpperCamelCase : Tuple = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCamelCase : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Dict = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase : Optional[Any] = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , token_type_ids=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a_ ( self ):
UpperCamelCase : int = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : List[Any] = config_and_inputs
UpperCamelCase : Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
return config, inputs_dict
@require_torch
class lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
lowercase : Dict = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
lowercase : List[Any] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
lowercase : Optional[Any] = (
{
'feature-extraction': XLMModel,
'fill-mask': XLMWithLMHeadModel,
'question-answering': XLMForQuestionAnsweringSimple,
'text-classification': XLMForSequenceClassification,
'text-generation': XLMWithLMHeadModel,
'token-classification': XLMForTokenClassification,
'zero-shot': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
UpperCamelCase : Union[str, Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_labels=SCREAMING_SNAKE_CASE_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
UpperCamelCase : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ )
return inputs_dict
def a_ ( self ):
UpperCamelCase : List[Any] = XLMModelTester(self )
UpperCamelCase : Optional[int] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , emb_dim=37 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE_ )
def a_ ( self ):
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : int = min_length + idx + 1
UpperCamelCase : Tuple = min_length + idx + 1
UpperCamelCase : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=1 ):
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE_ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE_ ):
# adds PAD dummy token
UpperCamelCase : List[str] = min_length + idx + 1
UpperCamelCase : Optional[Any] = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE_ ) , )
pass
@slow
def a_ ( self ):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase : str = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@require_torch
class lowerCamelCase ( unittest.TestCase ):
@slow
def a_ ( self ):
UpperCamelCase : Dict = XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
model.to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.tensor([[14, 447]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE_ ) # the president
UpperCamelCase : List[Any] = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
UpperCamelCase : Optional[int] = model.generate(SCREAMING_SNAKE_CASE_ , do_sample=SCREAMING_SNAKE_CASE_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE_ )
| 27
| 1
|
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
__A : List[str] = {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}
@is_pipeline_test
class lowerCamelCase ( unittest.TestCase ):
lowercase : Dict = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowercase : str = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
lowercase : Optional[Any] = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
lowercase : Optional[int] = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def a_ ( self ):
UpperCamelCase : Tuple = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" )
UpperCamelCase : int = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
UpperCamelCase : Tuple = text_classifier("""This is great !""" , top_k=2 )
self.assertEqual(
nested_simplify(__a ) , [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}] )
UpperCamelCase : Any = text_classifier(["""This is great !""", """This is bad"""] , top_k=2 )
self.assertEqual(
nested_simplify(__a ) , [
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
] , )
UpperCamelCase : List[Any] = text_classifier("""This is great !""" , top_k=1 )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
# Legacy behavior
UpperCamelCase : Optional[Any] = text_classifier("""This is great !""" , return_all_scores=__a )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
UpperCamelCase : Optional[Any] = text_classifier("""This is great !""" , return_all_scores=__a )
self.assertEqual(
nested_simplify(__a ) , [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]] )
UpperCamelCase : str = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=__a )
self.assertEqual(
nested_simplify(__a ) , [
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
] , )
UpperCamelCase : Tuple = text_classifier(["""This is great !""", """Something else"""] , return_all_scores=__a )
self.assertEqual(
nested_simplify(__a ) , [
{"""label""": """LABEL_0""", """score""": 0.504},
{"""label""": """LABEL_0""", """score""": 0.504},
] , )
@require_torch
def a_ ( self ):
import torch
UpperCamelCase : Optional[Any] = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""pt""" , device=torch.device("""cpu""" ) , )
UpperCamelCase : Dict = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
@require_tf
def a_ ( self ):
UpperCamelCase : Dict = pipeline(
task="""text-classification""" , model="""hf-internal-testing/tiny-random-distilbert""" , framework="""tf""" )
UpperCamelCase : Dict = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """LABEL_0""", """score""": 0.504}] )
@slow
@require_torch
def a_ ( self ):
UpperCamelCase : int = pipeline("""text-classification""" )
UpperCamelCase : Optional[Any] = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """POSITIVE""", """score""": 1.0}] )
UpperCamelCase : List[str] = text_classifier("""This is bad !""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
UpperCamelCase : Optional[Any] = text_classifier("""Birds are a type of animal""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """POSITIVE""", """score""": 0.988}] )
@slow
@require_tf
def a_ ( self ):
UpperCamelCase : Optional[Any] = pipeline("""text-classification""" , framework="""tf""" )
UpperCamelCase : Dict = text_classifier("""This is great !""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """POSITIVE""", """score""": 1.0}] )
UpperCamelCase : List[str] = text_classifier("""This is bad !""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """NEGATIVE""", """score""": 1.0}] )
UpperCamelCase : List[Any] = text_classifier("""Birds are a type of animal""" )
self.assertEqual(nested_simplify(__a ) , [{"""label""": """POSITIVE""", """score""": 0.988}] )
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = TextClassificationPipeline(model=__a , tokenizer=__a )
return text_classifier, ["HuggingFace is in", "This is another test"]
def a_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
UpperCamelCase : Union[str, Any] = 'HuggingFace is in'
UpperCamelCase : Tuple = text_classifier(__a )
self.assertEqual(nested_simplify(__a ) , [{"""label""": ANY(__a ), """score""": ANY(__a )}] )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
UpperCamelCase : int = ['HuggingFace is in ', 'Paris is in France']
UpperCamelCase : Dict = text_classifier(__a )
self.assertEqual(
nested_simplify(__a ) , [{"""label""": ANY(__a ), """score""": ANY(__a )}, {"""label""": ANY(__a ), """score""": ANY(__a )}] , )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values() )
# Forcing to get all results with `top_k=None`
# This is NOT the legacy format
UpperCamelCase : Tuple = text_classifier(__a , top_k=__a )
UpperCamelCase : Optional[int] = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(__a ) , [[{"""label""": ANY(__a ), """score""": ANY(__a )}] * N, [{"""label""": ANY(__a ), """score""": ANY(__a )}] * N] , )
UpperCamelCase : str = {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
UpperCamelCase : Optional[int] = text_classifier(__a )
self.assertEqual(
nested_simplify(__a ) , {"""label""": ANY(__a ), """score""": ANY(__a )} , )
self.assertTrue(outputs["""label"""] in model.config.idalabel.values() )
# This might be used a text pair, but tokenizer + pipe interaction
# makes it hard to understand that it's not using the pair properly
# https://github.com/huggingface/transformers/issues/17305
# We disabled this usage instead as it was outputting wrong outputs.
UpperCamelCase : Optional[Any] = [['HuggingFace is in ', 'Paris is in France']]
with self.assertRaises(__a ):
text_classifier(__a )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
UpperCamelCase : Tuple = text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]] )
self.assertEqual(
nested_simplify(__a ) , [{"""label""": ANY(__a ), """score""": ANY(__a )}] , )
self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values() )
| 350
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : int = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = [
'''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTBigCodeForSequenceClassification''',
'''GPTBigCodeForTokenClassification''',
'''GPTBigCodeForCausalLM''',
'''GPTBigCodeModel''',
'''GPTBigCodePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_bigcode import (
GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTBigCodeForCausalLM,
GPTBigCodeForSequenceClassification,
GPTBigCodeForTokenClassification,
GPTBigCodeModel,
GPTBigCodePreTrainedModel,
)
else:
import sys
__A : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 27
| 0
|
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