code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
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import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
a_ : List[Any] = logging.get_logger(__name__)
a_ : List[str] = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions',
'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
'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[Any] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
for attribute in key.split("." ):
lowerCamelCase = getattr(UpperCAmelCase__ , UpperCAmelCase__ )
if weight_type is not None:
lowerCamelCase = getattr(UpperCAmelCase__ , UpperCAmelCase__ ).shape
else:
lowerCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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":
lowerCamelCase = value
elif weight_type == "weight_g":
lowerCamelCase = value
elif weight_type == "weight_v":
lowerCamelCase = value
elif weight_type == "bias":
lowerCamelCase = value
elif weight_type == "running_mean":
lowerCamelCase = value
elif weight_type == "running_var":
lowerCamelCase = value
elif weight_type == "num_batches_tracked":
lowerCamelCase = value
elif weight_type == "inv_freq":
lowerCamelCase = value
else:
lowerCamelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = []
lowerCamelCase = fairseq_model.state_dict()
lowerCamelCase = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
lowerCamelCase = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , hf_model.config.feat_extract_norm == "group" , )
lowerCamelCase = True
else:
for key, mapped_key in MAPPING.items():
lowerCamelCase = "wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
lowerCamelCase = True
if "*" in mapped_key:
lowerCamelCase = name.split(UpperCAmelCase__ )[0].split("." )[-2]
lowerCamelCase = mapped_key.replace("*" , UpperCAmelCase__ )
if "pos_bias_u" in name:
lowerCamelCase = None
elif "pos_bias_v" in name:
lowerCamelCase = None
elif "weight_g" in name:
lowerCamelCase = "weight_g"
elif "weight_v" in name:
lowerCamelCase = "weight_v"
elif "bias" in name:
lowerCamelCase = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
lowerCamelCase = "weight"
elif "running_mean" in name:
lowerCamelCase = "running_mean"
elif "inv_freq" in name:
lowerCamelCase = "inv_freq"
elif "running_var" in name:
lowerCamelCase = "running_var"
elif "num_batches_tracked" in name:
lowerCamelCase = "num_batches_tracked"
else:
lowerCamelCase = None
set_recursively(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = full_name.split("conv_layers." )[-1]
lowerCamelCase = name.split("." )
lowerCamelCase = int(items[0] )
lowerCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
lowerCamelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
lowerCamelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
lowerCamelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(UpperCAmelCase__ )
@torch.no_grad()
def __lowercase( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=True ):
"""simple docstring"""
if config_path is not None:
lowerCamelCase = WavaVecaConformerConfig.from_pretrained(UpperCAmelCase__ , hidden_act="swish" )
else:
lowerCamelCase = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
lowerCamelCase = "rotary"
if is_finetuned:
if dict_path:
lowerCamelCase = Dictionary.load(UpperCAmelCase__ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
lowerCamelCase = target_dict.pad_index
lowerCamelCase = target_dict.bos_index
lowerCamelCase = target_dict.eos_index
lowerCamelCase = len(target_dict.symbols )
lowerCamelCase = os.path.join(UpperCAmelCase__ , "vocab.json" )
if not os.path.isdir(UpperCAmelCase__ ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase__ ) )
return
os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__ )
lowerCamelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
lowerCamelCase = 0
lowerCamelCase = 1
with open(UpperCAmelCase__ , "w" , encoding="utf-8" ) as vocab_handle:
json.dump(UpperCAmelCase__ , UpperCAmelCase__ )
lowerCamelCase = WavaVecaCTCTokenizer(
UpperCAmelCase__ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="|" , do_lower_case=UpperCAmelCase__ , )
lowerCamelCase = True if config.feat_extract_norm == "layer" else False
lowerCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , )
lowerCamelCase = WavaVecaProcessor(feature_extractor=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ )
processor.save_pretrained(UpperCAmelCase__ )
lowerCamelCase = WavaVecaConformerForCTC(UpperCAmelCase__ )
else:
lowerCamelCase = WavaVecaConformerForPreTraining(UpperCAmelCase__ )
if is_finetuned:
lowerCamelCase , lowerCamelCase , lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
lowerCamelCase = argparse.Namespace(task="audio_pretraining" )
lowerCamelCase = fairseq.tasks.setup_task(UpperCAmelCase__ )
lowerCamelCase , lowerCamelCase , lowerCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=UpperCAmelCase__ )
lowerCamelCase = model[0].eval()
recursively_load_weights(UpperCAmelCase__ , UpperCAmelCase__ , not is_finetuned )
hf_wavavec.save_pretrained(UpperCAmelCase__ )
if __name__ == "__main__":
a_ : int = 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('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
a_ : Optional[Any] = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
) | 623 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Union[str, Any] = [
'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
'UniSpeechForCTC',
'UniSpeechForPreTraining',
'UniSpeechForSequenceClassification',
'UniSpeechModel',
'UniSpeechPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
a_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 623 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : list[list[str]] =[[] for _ in range(SCREAMING_SNAKE_CASE )]
__lowerCamelCase : Dict =key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1 or len(SCREAMING_SNAKE_CASE ) <= key:
return input_string
for position, character in enumerate(SCREAMING_SNAKE_CASE ):
__lowerCamelCase : Dict =position % (lowest * 2) # puts it in bounds
__lowerCamelCase : int =min(SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(SCREAMING_SNAKE_CASE )
__lowerCamelCase : List[str] =[''.join(SCREAMING_SNAKE_CASE ) for row in temp_grid]
__lowerCamelCase : Optional[int] =''.join(SCREAMING_SNAKE_CASE )
return output_string
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : List[Any] =[]
__lowerCamelCase : List[str] =key - 1
if key <= 0:
raise ValueError('''Height of grid can\'t be 0 or negative''' )
if key == 1:
return input_string
__lowerCamelCase : list[list[str]] =[[] for _ in range(SCREAMING_SNAKE_CASE )] # generates template
for position in range(len(SCREAMING_SNAKE_CASE ) ):
__lowerCamelCase : Tuple =position % (lowest * 2) # puts it in bounds
__lowerCamelCase : str =min(SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('''*''' )
__lowerCamelCase : List[Any] =0
for row in temp_grid: # fills in the characters
__lowerCamelCase : List[str] =input_string[counter : counter + len(SCREAMING_SNAKE_CASE )]
grid.append(list(SCREAMING_SNAKE_CASE ) )
counter += len(SCREAMING_SNAKE_CASE )
__lowerCamelCase : Tuple ='' # reads as zigzag
for position in range(len(SCREAMING_SNAKE_CASE ) ):
__lowerCamelCase : int =position % (lowest * 2) # puts it in bounds
__lowerCamelCase : str =min(SCREAMING_SNAKE_CASE , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
__lowerCamelCase : Tuple ={}
for key_guess in range(1 , len(SCREAMING_SNAKE_CASE ) ): # tries every key
__lowerCamelCase : str =decrypt(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
__lowerCamelCase : Any =[]
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm1.weight', F'encoder.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm1.bias', F'encoder.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.weight', F'encoder.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.attn.proj.bias', F'encoder.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.norm2.weight', F'encoder.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.norm2.bias', F'encoder.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.weight', F'encoder.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc1.bias', F'encoder.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append(
(F'encoder.deit.blocks.{i}.mlp.fc2.weight', F'encoder.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'encoder.deit.blocks.{i}.mlp.fc2.bias', F'encoder.encoder.layer.{i}.output.dense.bias') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
] )
return rename_keys
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__lowerCamelCase : str =state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight' )
__lowerCamelCase : Dict =in_proj_weight[
: encoder_config.hidden_size, :
]
__lowerCamelCase : Any =in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__lowerCamelCase : Dict =in_proj_weight[
-encoder_config.hidden_size :, :
]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict ):
'''simple docstring'''
__lowerCamelCase : Optional[int] =dct.pop(SCREAMING_SNAKE_CASE )
__lowerCamelCase : Optional[Any] =val
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
if "handwritten" in checkpoint_url:
__lowerCamelCase : List[Any] ='''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg''' # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCamelCase : List[Any] ='''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
__lowerCamelCase : int =Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw ).convert('''RGB''' )
return im
@torch.no_grad()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] =ViTConfig(image_size=384 , qkv_bias=SCREAMING_SNAKE_CASE )
__lowerCamelCase : Optional[int] =TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__lowerCamelCase : Any =768
elif "large" in checkpoint_url:
# use ViT-large encoder
__lowerCamelCase : Optional[int] =1024
__lowerCamelCase : List[Any] =4096
__lowerCamelCase : Dict =24
__lowerCamelCase : Optional[Any] =16
__lowerCamelCase : Union[str, Any] =1024
else:
raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL''' )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCamelCase : Optional[int] =False
__lowerCamelCase : int ='''relu'''
__lowerCamelCase : Any =1024
__lowerCamelCase : str =True
__lowerCamelCase : List[Any] =False
__lowerCamelCase : Optional[int] =False
# load HuggingFace model
__lowerCamelCase : Dict =ViTModel(SCREAMING_SNAKE_CASE , add_pooling_layer=SCREAMING_SNAKE_CASE )
__lowerCamelCase : Any =TrOCRForCausalLM(SCREAMING_SNAKE_CASE )
__lowerCamelCase : List[Any] =VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE , decoder=SCREAMING_SNAKE_CASE )
model.eval()
# load state_dict of original model, rename some keys
__lowerCamelCase : Optional[Any] =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE , map_location='''cpu''' , check_hash=SCREAMING_SNAKE_CASE )['''model''']
__lowerCamelCase : Optional[int] =create_rename_keys(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
read_in_q_k_v(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__lowerCamelCase : List[Any] =state_dict.pop(SCREAMING_SNAKE_CASE )
if key.startswith('''decoder''' ) and "output_projection" not in key:
__lowerCamelCase : List[Any] =val
else:
__lowerCamelCase : Optional[Any] =val
# load state dict
model.load_state_dict(SCREAMING_SNAKE_CASE )
# Check outputs on an image
__lowerCamelCase : int =ViTImageProcessor(size=encoder_config.image_size )
__lowerCamelCase : Dict =RobertaTokenizer.from_pretrained('''roberta-large''' )
__lowerCamelCase : str =TrOCRProcessor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCamelCase : str =processor(images=prepare_img(SCREAMING_SNAKE_CASE ) , return_tensors='''pt''' ).pixel_values
# verify logits
__lowerCamelCase : List[Any] =torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__lowerCamelCase : Optional[int] =model(pixel_values=SCREAMING_SNAKE_CASE , decoder_input_ids=SCREAMING_SNAKE_CASE )
__lowerCamelCase : Optional[Any] =outputs.logits
__lowerCamelCase : Optional[Any] =torch.Size([1, 1, 50265] )
if "trocr-base-handwritten" in checkpoint_url:
__lowerCamelCase : List[str] =torch.tensor(
[-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311] )
elif "trocr-large-handwritten" in checkpoint_url:
__lowerCamelCase : Any =torch.tensor(
[-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170] )
elif "trocr-base-printed" in checkpoint_url:
__lowerCamelCase : Optional[Any] =torch.tensor(
[-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210] )
elif "trocr-large-printed" in checkpoint_url:
__lowerCamelCase : Optional[Any] =torch.tensor(
[-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10] , SCREAMING_SNAKE_CASE , atol=1E-3 ), "First elements of logits not as expected"
Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
print(F'Saving processor to {pytorch_dump_folder_path}' )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
type=str,
help='URL to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
_UpperCamelCase = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 363 | 0 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self : Dict ):
UpperCAmelCase = [
'''safety_checker/pytorch_model.bin''',
'''safety_checker/model.safetensors''',
'''vae/diffusion_pytorch_model.bin''',
'''vae/diffusion_pytorch_model.safetensors''',
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
self.assertTrue(is_safetensors_compatible(a__ ) )
def __snake_case ( self : Union[str, Any] ):
UpperCAmelCase = [
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
self.assertTrue(is_safetensors_compatible(a__ ) )
def __snake_case ( self : Union[str, Any] ):
UpperCAmelCase = [
'''safety_checker/pytorch_model.bin''',
'''safety_checker/model.safetensors''',
'''vae/diffusion_pytorch_model.bin''',
'''vae/diffusion_pytorch_model.safetensors''',
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
'''unet/diffusion_pytorch_model.bin''',
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(a__ ) )
def __snake_case ( self : Union[str, Any] ):
UpperCAmelCase = [
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
]
self.assertTrue(is_safetensors_compatible(a__ ) )
def __snake_case ( self : Dict ):
UpperCAmelCase = [
'''safety_checker/pytorch_model.bin''',
'''safety_checker/model.safetensors''',
'''vae/diffusion_pytorch_model.bin''',
'''vae/diffusion_pytorch_model.safetensors''',
'''text_encoder/pytorch_model.bin''',
# Removed: 'text_encoder/model.safetensors',
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
self.assertFalse(is_safetensors_compatible(a__ ) )
def __snake_case ( self : Tuple ):
UpperCAmelCase = [
'''safety_checker/pytorch_model.fp16.bin''',
'''safety_checker/model.fp16.safetensors''',
'''vae/diffusion_pytorch_model.fp16.bin''',
'''vae/diffusion_pytorch_model.fp16.safetensors''',
'''text_encoder/pytorch_model.fp16.bin''',
'''text_encoder/model.fp16.safetensors''',
'''unet/diffusion_pytorch_model.fp16.bin''',
'''unet/diffusion_pytorch_model.fp16.safetensors''',
]
UpperCAmelCase = '''fp16'''
self.assertTrue(is_safetensors_compatible(a__ , variant=a__ ) )
def __snake_case ( self : Tuple ):
UpperCAmelCase = [
'''unet/diffusion_pytorch_model.fp16.bin''',
'''unet/diffusion_pytorch_model.fp16.safetensors''',
]
UpperCAmelCase = '''fp16'''
self.assertTrue(is_safetensors_compatible(a__ , variant=a__ ) )
def __snake_case ( self : Any ):
# pass variant but use the non-variant filenames
UpperCAmelCase = [
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
UpperCAmelCase = '''fp16'''
self.assertTrue(is_safetensors_compatible(a__ , variant=a__ ) )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = [
'''safety_checker/pytorch_model.fp16.bin''',
'''safety_checker/model.fp16.safetensors''',
'''vae/diffusion_pytorch_model.fp16.bin''',
'''vae/diffusion_pytorch_model.fp16.safetensors''',
'''text_encoder/pytorch_model.fp16.bin''',
'''text_encoder/model.fp16.safetensors''',
'''unet/diffusion_pytorch_model.fp16.bin''',
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
UpperCAmelCase = '''fp16'''
self.assertFalse(is_safetensors_compatible(a__ , variant=a__ ) )
def __snake_case ( self : List[str] ):
UpperCAmelCase = [
'''text_encoder/pytorch_model.fp16.bin''',
'''text_encoder/model.fp16.safetensors''',
]
UpperCAmelCase = '''fp16'''
self.assertTrue(is_safetensors_compatible(a__ , variant=a__ ) )
def __snake_case ( self : str ):
# pass variant but use the non-variant filenames
UpperCAmelCase = [
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
]
UpperCAmelCase = '''fp16'''
self.assertTrue(is_safetensors_compatible(a__ , variant=a__ ) )
def __snake_case ( self : Optional[int] ):
UpperCAmelCase = [
'''safety_checker/pytorch_model.fp16.bin''',
'''safety_checker/model.fp16.safetensors''',
'''vae/diffusion_pytorch_model.fp16.bin''',
'''vae/diffusion_pytorch_model.fp16.safetensors''',
'''text_encoder/pytorch_model.fp16.bin''',
# 'text_encoder/model.fp16.safetensors',
'''unet/diffusion_pytorch_model.fp16.bin''',
'''unet/diffusion_pytorch_model.fp16.safetensors''',
]
UpperCAmelCase = '''fp16'''
self.assertFalse(is_safetensors_compatible(a__ , variant=a__ ) )
| 51 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
lowercase_ : int
lowercase_ : TreeNode | None = None
lowercase_ : TreeNode | None = None
__SCREAMING_SNAKE_CASE = namedtuple('CoinsDistribResult', 'moves excess')
def UpperCAmelCase ( a__ ):
'''simple docstring'''
if root is None:
return 0
# Validation
def count_nodes(a__ ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(a__ ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(a__ ) != count_coins(a__ ):
raise ValueError('The nodes number should be same as the number of coins' )
# Main calculation
def get_distrib(a__ ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowerCAmelCase , lowerCAmelCase :Optional[int] = get_distrib(node.left )
lowerCAmelCase , lowerCAmelCase :str = get_distrib(node.right )
lowerCAmelCase :str = 1 - left_distrib_excess
lowerCAmelCase :Optional[Any] = 1 - right_distrib_excess
lowerCAmelCase :Any = (
left_distrib_moves
+ right_distrib_moves
+ abs(a__ )
+ abs(a__ )
)
lowerCAmelCase :Tuple = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(a__ , a__ )
return get_distrib(a__ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod() | 553 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCamelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> List[str]:
A__ = params
A__ = np.array(lowercase__ )
A__ = np.array([len(lowercase__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , SCREAMING_SNAKE_CASE__ ) -> str:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> str:
return len(self.lengths )
def snake_case__ ( self ) -> Any:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def snake_case__ ( self ) -> Any:
A__ = self.params.max_model_input_size
A__ = self.lengths > max_len
logger.info(f"""Splitting {sum(lowercase__ )} too long sequences.""" )
def divide_chunks(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return [l[i : i + n] for i in range(0 , len(lowercase__ ) , lowercase__ )]
A__ = []
A__ = []
if self.params.mlm:
A__ = self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
else:
A__ = self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
A__ = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
A__ = np.insert(lowercase__ , 0 , lowercase__ )
if sub_s[-1] != sep_id:
A__ = np.insert(lowercase__ , len(lowercase__ ) , lowercase__ )
assert len(lowercase__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowercase__ )
new_tok_ids.extend(lowercase__ )
new_lengths.extend([len(lowercase__ ) for l in sub_seqs] )
A__ = np.array(lowercase__ )
A__ = np.array(lowercase__ )
def snake_case__ ( self ) -> int:
A__ = len(self )
A__ = self.lengths > 11
A__ = self.token_ids[indices]
A__ = self.lengths[indices]
A__ = len(self )
logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""" )
def snake_case__ ( self ) -> Tuple:
if "unk_token" not in self.params.special_tok_ids:
return
else:
A__ = self.params.special_tok_ids["unk_token"]
A__ = len(self )
A__ = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
A__ = (unk_occs / self.lengths) < 0.5
A__ = self.token_ids[indices]
A__ = self.lengths[indices]
A__ = len(self )
logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""" )
def snake_case__ ( self ) -> Dict:
if not self.params.is_master:
return
logger.info(f"""{len(self )} sequences""" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ ) -> List[str]:
A__ = [t[0] for t in batch]
A__ = [t[1] for t in batch]
assert len(lowercase__ ) == len(lowercase__ )
# Max for paddings
A__ = max(lowercase__ )
# Pad token ids
if self.params.mlm:
A__ = self.params.special_tok_ids["pad_token"]
else:
A__ = self.params.special_tok_ids["unk_token"]
A__ = [list(t.astype(lowercase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowercase__ )) for t in token_ids]
assert len(tk_ ) == len(lowercase__ )
assert all(len(lowercase__ ) == max_seq_len_ for t in tk_ )
A__ = torch.tensor(tk_ ) # (bs, max_seq_len_)
A__ = torch.tensor(lowercase__ ) # (bs)
return tk_t, lg_t
| 702 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
A__ : str = (DEISMultistepScheduler,)
A__ : List[str] = (("num_inference_steps", 2_5),)
def snake_case__ ( self , **SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
A__ = {
"num_train_timesteps": 1000,
"beta_start": 0.0_0_0_1,
"beta_end": 0.0_2,
"beta_schedule": "linear",
"solver_order": 2,
}
config.update(**SCREAMING_SNAKE_CASE__ )
return config
def snake_case__ ( self , SCREAMING_SNAKE_CASE__=0 , **SCREAMING_SNAKE_CASE__ ) -> Tuple:
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("num_inference_steps" , SCREAMING_SNAKE_CASE__ )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__ )
A__ = scheduler_class(**SCREAMING_SNAKE_CASE__ )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
# copy over dummy past residuals
A__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE__ )
A__ = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
# copy over dummy past residuals
A__ = dummy_past_residuals[: new_scheduler.config.solver_order]
A__ , A__ = sample, sample
for t in range(SCREAMING_SNAKE_CASE__ , time_step + scheduler.config.solver_order + 1 ):
A__ = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
A__ = new_scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def snake_case__ ( self ) -> List[Any]:
pass
def snake_case__ ( self , SCREAMING_SNAKE_CASE__=0 , **SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("num_inference_steps" , SCREAMING_SNAKE_CASE__ )
A__ = self.dummy_sample
A__ = 0.1 * sample
A__ = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config()
A__ = scheduler_class(**SCREAMING_SNAKE_CASE__ )
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
# copy over dummy past residuals (must be after setting timesteps)
A__ = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(SCREAMING_SNAKE_CASE__ )
A__ = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
# copy over dummy past residuals
new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
# copy over dummy past residual (must be after setting timesteps)
A__ = dummy_past_residuals[: new_scheduler.config.solver_order]
A__ = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
A__ = new_scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def snake_case__ ( self , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ ) -> str:
if scheduler is None:
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__ )
A__ = scheduler_class(**SCREAMING_SNAKE_CASE__ )
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__ )
A__ = scheduler_class(**SCREAMING_SNAKE_CASE__ )
A__ = 10
A__ = self.dummy_model()
A__ = self.dummy_sample_deter
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
for i, t in enumerate(scheduler.timesteps ):
A__ = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A__ = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
return sample
def snake_case__ ( self ) -> Tuple:
A__ = dict(self.forward_default_kwargs )
A__ = kwargs.pop("num_inference_steps" , SCREAMING_SNAKE_CASE__ )
for scheduler_class in self.scheduler_classes:
A__ = self.get_scheduler_config()
A__ = scheduler_class(**SCREAMING_SNAKE_CASE__ )
A__ = self.dummy_sample
A__ = 0.1 * sample
if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__ , "set_timesteps" ):
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__ , "set_timesteps" ):
A__ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
A__ = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
A__ = dummy_past_residuals[: scheduler.config.solver_order]
A__ = scheduler.timesteps[5]
A__ = scheduler.timesteps[6]
A__ = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
A__ = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def snake_case__ ( self ) -> List[str]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
A__ = DEISMultistepScheduler(**self.get_scheduler_config() )
A__ = self.full_loop(scheduler=SCREAMING_SNAKE_CASE__ )
A__ = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
A__ = DPMSolverSinglestepScheduler.from_config(scheduler.config )
A__ = DPMSolverMultistepScheduler.from_config(scheduler.config )
A__ = UniPCMultistepScheduler.from_config(scheduler.config )
A__ = DEISMultistepScheduler.from_config(scheduler.config )
A__ = self.full_loop(scheduler=SCREAMING_SNAKE_CASE__ )
A__ = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
def snake_case__ ( self ) -> List[str]:
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self ) -> Optional[int]:
self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__ )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=SCREAMING_SNAKE_CASE__ , prediction_type=SCREAMING_SNAKE_CASE__ , sample_max_value=SCREAMING_SNAKE_CASE__ , algorithm_type="deis" , solver_order=SCREAMING_SNAKE_CASE__ , solver_type=SCREAMING_SNAKE_CASE__ , )
def snake_case__ ( self ) -> List[str]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self ) -> Dict:
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=SCREAMING_SNAKE_CASE__ , solver_type=SCREAMING_SNAKE_CASE__ , prediction_type=SCREAMING_SNAKE_CASE__ , algorithm_type=SCREAMING_SNAKE_CASE__ , )
A__ = self.full_loop(
solver_order=SCREAMING_SNAKE_CASE__ , solver_type=SCREAMING_SNAKE_CASE__ , prediction_type=SCREAMING_SNAKE_CASE__ , algorithm_type=SCREAMING_SNAKE_CASE__ , )
assert not torch.isnan(SCREAMING_SNAKE_CASE__ ).any(), "Samples have nan numbers"
def snake_case__ ( self ) -> Optional[int]:
self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__ )
self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__ )
def snake_case__ ( self ) -> Optional[Any]:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__ , time_step=0 )
def snake_case__ ( self ) -> int:
A__ = self.full_loop()
A__ = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1e-3
def snake_case__ ( self ) -> Union[str, Any]:
A__ = self.full_loop(prediction_type="v_prediction" )
A__ = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__ ) )
assert abs(result_mean.item() - 0.0_9_1 ) < 1e-3
def snake_case__ ( self ) -> List[str]:
A__ = self.scheduler_classes[0]
A__ = self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__ , dynamic_thresholding_ratio=0 )
A__ = scheduler_class(**SCREAMING_SNAKE_CASE__ )
A__ = 10
A__ = self.dummy_model()
A__ = self.dummy_sample_deter.half()
scheduler.set_timesteps(SCREAMING_SNAKE_CASE__ )
for i, t in enumerate(scheduler.timesteps ):
A__ = model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A__ = scheduler.step(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).prev_sample
assert sample.dtype == torch.floataa
| 562 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Tuple = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : str = [
"GIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GitForCausalLM",
"GitModel",
"GitPreTrainedModel",
"GitVisionModel",
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
A_ = 250_004
A_ = 250_020
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = MBartTokenizer
SCREAMING_SNAKE_CASE_ = MBartTokenizerFast
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = True
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase_ = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase_ = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase_ = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase_ = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
lowerCamelCase_ = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase_ = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 'facebook/mbart-large-en-ro'
SCREAMING_SNAKE_CASE_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
SCREAMING_SNAKE_CASE_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
SCREAMING_SNAKE_CASE_ = [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]
@classmethod
def UpperCamelCase( cls ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCamelCase_ = 1
return cls
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] , 250001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] , 250004 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] , 250020 )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
lowerCamelCase_ = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCamelCase_ = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase_ = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = 10
lowerCamelCase_ = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [250026, 250001] )
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = tempfile.mkdtemp()
lowerCamelCase_ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = MBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , return_tensors='pt' )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCamelCase_ = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase_ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
lowerCamelCase_ = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
lowerCamelCase_ = targets['input_ids']
lowerCamelCase_ = shift_tokens_right(SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 3034, 2, 250004]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 250001,
} , )
| 42 | 0 |
"""simple docstring"""
from math import ceil
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = list(range(0 , lowerCamelCase ) )
UpperCAmelCase__ = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
UpperCAmelCase__ = []
for i in device_map_blocks:
if device_map_blocks.count(lowerCamelCase ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(lowerCamelCase )
# Missing blocks
UpperCAmelCase__ = [i for i in blocks if i not in device_map_blocks]
UpperCAmelCase__ = [i for i in device_map_blocks if i not in blocks]
if len(lowerCamelCase ) != 0:
raise ValueError(
'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
' These attention blocks were specified more than once: ' + str(lowerCamelCase ) )
if len(lowerCamelCase ) != 0:
raise ValueError(
'There are attention blocks for this model that are not specified in the device_map. Add these attention '
'blocks to a device on the device_map: ' + str(lowerCamelCase ) )
if len(lowerCamelCase ) != 0:
raise ValueError(
'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
+ str(lowerCamelCase ) )
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = list(range(lowerCamelCase ) )
UpperCAmelCase__ = int(ceil(n_layers / len(lowerCamelCase ) ) )
UpperCAmelCase__ = [layers[i : i + n_blocks] for i in range(0 , lowerCamelCase , lowerCamelCase )]
return dict(zip(lowerCamelCase , lowerCamelCase ) )
| 632 | """simple docstring"""
# Copyright 2021 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 argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
lowerCAmelCase__ : Dict = 'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'
def a_ ( ):
UpperCAmelCase__ = _ask_options(
'In which compute environment are you running?' , ['This machine', 'AWS (Amazon SageMaker)'] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
UpperCAmelCase__ = get_sagemaker_input()
else:
UpperCAmelCase__ = get_cluster_input()
return config
def a_ ( lowerCamelCase=None ):
if subparsers is not None:
UpperCAmelCase__ = subparsers.add_parser('config' , description=lowerCamelCase )
else:
UpperCAmelCase__ = argparse.ArgumentParser('Accelerate config command' , description=lowerCamelCase )
parser.add_argument(
'--config_file' , default=lowerCamelCase , help=(
'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
'with \'huggingface\'.'
) , )
if subparsers is not None:
parser.set_defaults(func=lowerCamelCase )
return parser
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = get_user_input()
if args.config_file is not None:
UpperCAmelCase__ = args.config_file
else:
if not os.path.isdir(lowerCamelCase ):
os.makedirs(lowerCamelCase )
UpperCAmelCase__ = default_yaml_config_file
if config_file.endswith('.json' ):
config.to_json_file(lowerCamelCase )
else:
config.to_yaml_file(lowerCamelCase )
print(f'''accelerate configuration saved at {config_file}''' )
def a_ ( ):
UpperCAmelCase__ = config_command_parser()
UpperCAmelCase__ = parser.parse_args()
config_command(lowerCamelCase )
if __name__ == "__main__":
main()
| 632 | 1 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _UpperCAmelCase ( unittest.TestCase ):
def a ( self : List[Any] ):
__UpperCAmelCase = get_activation('''swish''' )
self.assertIsInstance(_lowercase , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def a ( self : List[str] ):
__UpperCAmelCase = get_activation('''silu''' )
self.assertIsInstance(_lowercase , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def a ( self : Optional[Any] ):
__UpperCAmelCase = get_activation('''mish''' )
self.assertIsInstance(_lowercase , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def a ( self : Any ):
__UpperCAmelCase = get_activation('''gelu''' )
self.assertIsInstance(_lowercase , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 49 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCAmelCase ( _lowerCAmelCase , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
@property
def a ( self : List[str] ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def a ( self : Dict ):
__UpperCAmelCase = ort.SessionOptions()
__UpperCAmelCase = False
return options
def a ( self : Any ):
__UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__UpperCAmelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=_lowercase , feature_extractor=_lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowercase )
__UpperCAmelCase = '''A red cat sitting on a park bench'''
__UpperCAmelCase = np.random.RandomState(0 )
__UpperCAmelCase = pipe(
prompt=_lowercase , image=_lowercase , mask_image=_lowercase , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowercase , output_type='''np''' , )
__UpperCAmelCase = output.images
__UpperCAmelCase = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__UpperCAmelCase = np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def a ( self : Optional[int] ):
__UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__UpperCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__UpperCAmelCase = LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
__UpperCAmelCase = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=_lowercase , safety_checker=_lowercase , feature_extractor=_lowercase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowercase )
__UpperCAmelCase = '''A red cat sitting on a park bench'''
__UpperCAmelCase = np.random.RandomState(0 )
__UpperCAmelCase = pipe(
prompt=_lowercase , image=_lowercase , mask_image=_lowercase , guidance_scale=7.5 , num_inference_steps=20 , generator=_lowercase , output_type='''np''' , )
__UpperCAmelCase = output.images
__UpperCAmelCase = images[0, 2_55:2_58, 2_55:2_58, -1]
assert images.shape == (1, 5_12, 5_12, 3)
__UpperCAmelCase = np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
| 49 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 559 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import 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 (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class A :
def __init__( self : List[Any] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any]=13 , __UpperCAmelCase : str=7 , __UpperCAmelCase : Dict=True , __UpperCAmelCase : str=True , __UpperCAmelCase : List[str]=True , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Any=99 , __UpperCAmelCase : Union[str, Any]=16 , __UpperCAmelCase : Union[str, Any]=36 , __UpperCAmelCase : Optional[int]=6 , __UpperCAmelCase : Union[str, Any]=6 , __UpperCAmelCase : List[str]=6 , __UpperCAmelCase : Union[str, Any]=37 , __UpperCAmelCase : Dict="gelu" , __UpperCAmelCase : Any=0.1 , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : Dict=512 , __UpperCAmelCase : List[Any]=16 , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : Optional[Any]=0.02 , __UpperCAmelCase : Any=3 , __UpperCAmelCase : Optional[int]=4 , __UpperCAmelCase : Optional[Any]=None , ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = seq_length
UpperCamelCase_ = is_training
UpperCamelCase_ = use_input_mask
UpperCamelCase_ = use_token_type_ids
UpperCamelCase_ = use_labels
UpperCamelCase_ = vocab_size
UpperCamelCase_ = embedding_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_hidden_groups
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_act
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = type_vocab_size
UpperCamelCase_ = type_sequence_label_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = num_labels
UpperCamelCase_ = num_choices
UpperCamelCase_ = scope
def lowercase__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ = None
if self.use_input_mask:
UpperCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase_ = None
if self.use_token_type_ids:
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase_ = None
UpperCamelCase_ = None
UpperCamelCase_ = None
if self.use_labels:
UpperCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase__ ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
return AlbertConfig(
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 , num_hidden_groups=self.num_hidden_groups , )
def lowercase__ ( self : List[str] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = AlbertModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCamelCase_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
UpperCamelCase_ = model(__UpperCAmelCase , token_type_ids=__UpperCAmelCase )
UpperCamelCase_ = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowercase__ ( self : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
UpperCamelCase_ = AlbertForPreTraining(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCamelCase_ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , sentence_order_label=__UpperCAmelCase , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def lowercase__ ( self : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = AlbertForMaskedLM(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCamelCase_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase__ ( self : Tuple , __UpperCAmelCase : Any , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = AlbertForQuestionAnswering(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCamelCase_ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , start_positions=__UpperCAmelCase , end_positions=__UpperCAmelCase , )
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 lowercase__ ( self : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[Any] , __UpperCAmelCase : int ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.num_labels
UpperCamelCase_ = AlbertForSequenceClassification(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCamelCase_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase__ ( self : List[str] , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : List[str] , __UpperCAmelCase : Any , __UpperCAmelCase : Any , __UpperCAmelCase : Any ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase_ = self.num_labels
UpperCamelCase_ = AlbertForTokenClassification(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCamelCase_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase__ ( self : Any , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict , __UpperCAmelCase : int , __UpperCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.num_choices
UpperCamelCase_ = AlbertForMultipleChoice(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
UpperCamelCase_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ = model(
__UpperCAmelCase , attention_mask=__UpperCAmelCase , token_type_ids=__UpperCAmelCase , labels=__UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowercase__ ( self : int ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = self.prepare_config_and_inputs()
(
(
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) ,
) = config_and_inputs
UpperCamelCase_ = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE : Union[str, Any] = (
{
'''feature-extraction''': AlbertModel,
'''fill-mask''': AlbertForMaskedLM,
'''question-answering''': AlbertForQuestionAnswering,
'''text-classification''': AlbertForSequenceClassification,
'''token-classification''': AlbertForTokenClassification,
'''zero-shot''': AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : int = True
def lowercase__ ( self : Any , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : int=False ) -> List[Any]:
"""simple docstring"""
UpperCamelCase_ = super()._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase , return_labels=__UpperCAmelCase )
if return_labels:
if model_class in get_values(__UpperCAmelCase ):
UpperCamelCase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__UpperCAmelCase )
UpperCamelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__UpperCAmelCase )
return inputs_dict
def lowercase__ ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = AlbertModelTester(self )
UpperCamelCase_ = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=37 )
def lowercase__ ( self : str ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self : str ) -> int:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowercase__ ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*__UpperCAmelCase )
def lowercase__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowercase__ ( self : Any ) -> int:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )
def lowercase__ ( self : Dict ) -> int:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )
def lowercase__ ( self : Any ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )
def lowercase__ ( self : int ) -> Dict:
"""simple docstring"""
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase_ = type
self.model_tester.create_and_check_model(*__UpperCAmelCase )
@slow
def lowercase__ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase_ = AlbertModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@require_torch
class A ( unittest.TestCase ):
@slow
def lowercase__ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase_ = AlbertModel.from_pretrained('albert-base-v2' )
UpperCamelCase_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
UpperCamelCase_ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCamelCase_ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )[0]
UpperCamelCase_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , __UpperCAmelCase )
UpperCamelCase_ = torch.tensor(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , __UpperCAmelCase , atol=1E-4 ) )
| 559 | 1 |
import sys
from collections import defaultdict
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self ) -> List[Any]:
lowerCamelCase : Any = []
def _lowercase ( self , UpperCamelCase__ ) -> List[str]:
return self.node_position[vertex]
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> int:
lowerCamelCase : Tuple = pos
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
lowerCamelCase : Tuple = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
lowerCamelCase : Optional[Any] = 2 * start + 1
else:
lowerCamelCase : Union[str, Any] = 2 * start + 2
if heap[smallest_child] < heap[start]:
lowerCamelCase , lowerCamelCase : Tuple = heap[smallest_child], positions[smallest_child]
lowerCamelCase , lowerCamelCase : Any = (
heap[start],
positions[start],
)
lowerCamelCase , lowerCamelCase : Any = temp, tempa
lowerCamelCase : Tuple = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , UpperCamelCase__ )
self.top_to_bottom(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
lowerCamelCase : int = position[index]
while index != 0:
lowerCamelCase : List[str] = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
lowerCamelCase : str = heap[parent]
lowerCamelCase : List[str] = position[parent]
self.set_position(position[parent] , UpperCamelCase__ )
else:
lowerCamelCase : List[Any] = val
lowerCamelCase : Dict = temp
self.set_position(UpperCamelCase__ , UpperCamelCase__ )
break
lowerCamelCase : List[Any] = parent
else:
lowerCamelCase : Any = val
lowerCamelCase : Dict = temp
self.set_position(UpperCamelCase__ , 0 )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
lowerCamelCase : Dict = len(UpperCamelCase__ ) // 2 - 1
for i in range(UpperCamelCase__ , -1 , -1 ):
self.top_to_bottom(UpperCamelCase__ , UpperCamelCase__ , len(UpperCamelCase__ ) , UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
lowerCamelCase : int = positions[0]
lowerCamelCase : str = sys.maxsize
self.top_to_bottom(UpperCamelCase__ , 0 , len(UpperCamelCase__ ) , UpperCamelCase__ )
return temp
def A ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
lowerCamelCase : List[str] = Heap()
lowerCamelCase : Optional[Any] = [0] * len(_SCREAMING_SNAKE_CASE )
lowerCamelCase : List[str] = [-1] * len(_SCREAMING_SNAKE_CASE ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
lowerCamelCase : Optional[Any] = [] # Heap of Distance of vertices from their neighboring vertex
lowerCamelCase : Any = []
for vertex in range(len(_SCREAMING_SNAKE_CASE ) ):
distance_tv.append(sys.maxsize )
positions.append(_SCREAMING_SNAKE_CASE )
heap.node_position.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Union[str, Any] = []
lowerCamelCase : Optional[int] = 1
lowerCamelCase : Tuple = sys.maxsize
for neighbor, distance in adjacency_list[0]:
lowerCamelCase : Optional[int] = 0
lowerCamelCase : str = distance
heap.heapify(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
for _ in range(1 ,len(_SCREAMING_SNAKE_CASE ) ):
lowerCamelCase : Tuple = heap.delete_minimum(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
lowerCamelCase : Optional[Any] = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(_SCREAMING_SNAKE_CASE )]
):
lowerCamelCase : Tuple = distance
heap.bottom_to_top(
_SCREAMING_SNAKE_CASE ,heap.get_position(_SCREAMING_SNAKE_CASE ) ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
lowerCamelCase : Union[str, Any] = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
SCREAMING_SNAKE_CASE__ : int = int(input('Enter number of edges: ').strip())
SCREAMING_SNAKE_CASE__ : str = defaultdict(list)
for _ in range(edges_number):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 311 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , *UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> List[Any]:
super().__init__(*UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = eval_examples
lowerCamelCase : Optional[int] = post_process_function
def _lowercase ( self , UpperCamelCase__ = None , UpperCamelCase__=None , UpperCamelCase__ = None , UpperCamelCase__ = "eval" , **UpperCamelCase__ , ) -> Dict[str, float]:
lowerCamelCase : Dict = gen_kwargs.copy()
lowerCamelCase : List[str] = (
gen_kwargs["max_length"] if gen_kwargs.get("max_length" ) is not None else self.args.generation_max_length
)
lowerCamelCase : List[str] = (
gen_kwargs["num_beams"] if gen_kwargs.get("num_beams" ) is not None else self.args.generation_num_beams
)
lowerCamelCase : Optional[Any] = gen_kwargs
lowerCamelCase : Optional[int] = self.eval_dataset if eval_dataset is None else eval_dataset
lowerCamelCase : List[str] = self.get_eval_dataloader(UpperCamelCase__ )
lowerCamelCase : Optional[int] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase : Dict = self.compute_metrics
lowerCamelCase : Any = None
lowerCamelCase : Optional[int] = time.time()
lowerCamelCase : Any = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase : Dict = eval_loop(
UpperCamelCase__ , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , metric_key_prefix=UpperCamelCase__ , )
finally:
lowerCamelCase : Union[str, Any] = compute_metrics
lowerCamelCase : Optional[Any] = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
UpperCamelCase__ , UpperCamelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
lowerCamelCase : List[str] = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : int = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
lowerCamelCase : Any = metrics.pop(UpperCamelCase__ )
metrics.update(output.metrics )
else:
lowerCamelCase : Tuple = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(UpperCamelCase__ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowerCamelCase : Optional[int] = self.callback_handler.on_evaluate(self.args , self.state , self.control , UpperCamelCase__ )
return metrics
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__ = "test" , **UpperCamelCase__ ) -> int:
lowerCamelCase : str = gen_kwargs.copy()
lowerCamelCase : str = self.get_test_dataloader(UpperCamelCase__ )
# Temporarily disable metric computation, we will do it in the loop here.
lowerCamelCase : Union[str, Any] = self.compute_metrics
lowerCamelCase : int = None
lowerCamelCase : Optional[int] = time.time()
lowerCamelCase : str = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCamelCase : Any = eval_loop(
UpperCamelCase__ , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=UpperCamelCase__ , metric_key_prefix=UpperCamelCase__ , )
finally:
lowerCamelCase : Tuple = compute_metrics
lowerCamelCase : Optional[Any] = self.args.eval_batch_size * self.args.world_size
if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
UpperCamelCase__ , UpperCamelCase__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
lowerCamelCase : str = self.post_process_function(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , "predict" )
lowerCamelCase : Dict = self.compute_metrics(UpperCamelCase__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
lowerCamelCase : int = metrics.pop(UpperCamelCase__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=UpperCamelCase__ )
| 311 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a__: Tuple = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = XLNetTokenizer
__SCREAMING_SNAKE_CASE = XLNetTokenizerFast
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = True
def UpperCamelCase ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
A__ = XLNetTokenizer(__lowerCamelCase,keep_accents=__lowerCamelCase )
tokenizer.sanitize_special_tokens()
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self ):
A__ = '''<s>'''
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ),__lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ),__lowerCamelCase )
def UpperCamelCase ( self ):
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0],'''<unk>''' )
self.assertEqual(vocab_keys[1],'''<s>''' )
self.assertEqual(vocab_keys[-1],'''<eod>''' )
self.assertEqual(len(__lowerCamelCase ),1006 )
def UpperCamelCase ( self ):
self.assertEqual(self.get_tokenizer().vocab_size,1000 )
def UpperCamelCase ( self ):
A__ = XLNetTokenizer(__lowerCamelCase,keep_accents=__lowerCamelCase )
A__ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowerCamelCase,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ),[285, 46, 10, 170, 382] )
A__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowerCamelCase,[
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
],)
A__ = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(__lowerCamelCase,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] )
A__ = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase,[
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
],)
def UpperCamelCase ( self ):
A__ = XLNetTokenizer(__lowerCamelCase,do_lower_case=__lowerCamelCase )
A__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowerCamelCase,[
SPIECE_UNDERLINE + '''''',
'''i''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''se''',
'''.''',
],)
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ),['''▁he''', '''ll''', '''o'''] )
def UpperCamelCase ( self ):
A__ = XLNetTokenizer(__lowerCamelCase,do_lower_case=__lowerCamelCase )
A__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowerCamelCase,[
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''se''',
'''.''',
],)
@slow
def UpperCamelCase ( self ):
A__ = XLNetTokenizer.from_pretrained('''xlnet-base-cased''' )
A__ = tokenizer.encode('''sequence builders''',add_special_tokens=__lowerCamelCase )
A__ = tokenizer.encode('''multi-sequence build''',add_special_tokens=__lowerCamelCase )
A__ = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase )
A__ = tokenizer.build_inputs_with_special_tokens(__lowerCamelCase,__lowerCamelCase )
assert encoded_sentence == text + [4, 3]
assert encoded_pair == text + [4] + text_a + [4, 3]
@slow
def UpperCamelCase ( self ):
# fmt: off
A__ = {'''input_ids''': [[17, 2_1442, 270, 17, 10, 1_4645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 2_2018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 1_4431, 13, 5500, 11, 1176, 580, 13, 1_6819, 4797, 23, 17, 10, 1_7135, 658, 19, 457, 7932, 13, 184, 19, 3154, 1_7135, 6468, 19, 1404, 1_2269, 19, 4229, 5356, 1_6264, 46, 19, 17, 2_0545, 1_0395, 9, 9, 9, 11, 28, 6421, 9531, 2_0729, 17, 10, 353, 1_7022, 11, 21, 6421, 9531, 1_6949, 17, 10, 1_1509, 753, 11, 33, 95, 2421, 7385, 956, 1_4431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 2_4738, 19, 1_3203, 658, 218, 787, 21, 430, 1_8482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 2_2178, 27, 1064, 22, 956, 13, 1_1101, 1429, 5854, 2_4313, 1_8953, 40, 422, 2_4366, 68, 1758, 37, 1_0483, 1_4257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 1_3894, 3380, 23, 95, 18, 1_7634, 2288, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase,model_name='''xlnet-base-cased''',revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''',)
| 719 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a__: int = '▁'
a__: List[Any] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = BertGenerationTokenizer
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
def UpperCamelCase ( self ):
super().setUp()
A__ = BertGenerationTokenizer(__lowerCamelCase,keep_accents=__lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self ):
A__ = '''<s>'''
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ),__lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ),__lowerCamelCase )
def UpperCamelCase ( self ):
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0],'''<unk>''' )
self.assertEqual(vocab_keys[1],'''<s>''' )
self.assertEqual(vocab_keys[-1],'''<pad>''' )
self.assertEqual(len(__lowerCamelCase ),1002 )
def UpperCamelCase ( self ):
self.assertEqual(self.get_tokenizer().vocab_size,1000 )
def UpperCamelCase ( self ):
A__ = BertGenerationTokenizer(__lowerCamelCase,keep_accents=__lowerCamelCase )
A__ = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowerCamelCase,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCamelCase ),[285, 46, 10, 170, 382],)
A__ = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowerCamelCase,[
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
],)
A__ = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],)
A__ = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase,[
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
],)
@cached_property
def UpperCamelCase ( self ):
return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''' )
@slow
def UpperCamelCase ( self ):
A__ = '''Hello World!'''
A__ = [1_8536, 2260, 101]
self.assertListEqual(__lowerCamelCase,self.big_tokenizer.encode(__lowerCamelCase ) )
@slow
def UpperCamelCase ( self ):
A__ = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
A__ = [
871,
419,
358,
946,
991,
2521,
452,
358,
1357,
387,
7751,
3536,
112,
985,
456,
126,
865,
938,
5400,
5734,
458,
1368,
467,
786,
2462,
5246,
1159,
633,
865,
4519,
457,
582,
852,
2557,
427,
916,
508,
405,
3_4324,
497,
391,
408,
1_1342,
1244,
385,
100,
938,
985,
456,
574,
362,
1_2597,
3200,
3129,
1172,
]
self.assertListEqual(__lowerCamelCase,self.big_tokenizer.encode(__lowerCamelCase ) )
@require_torch
@slow
def UpperCamelCase ( self ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
A__ = list(self.big_tokenizer.get_vocab().keys() )[:10]
A__ = ''' '''.join(__lowerCamelCase )
A__ = self.big_tokenizer.encode_plus(__lowerCamelCase,return_tensors='''pt''',return_token_type_ids=__lowerCamelCase )
A__ = self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence],return_tensors='''pt''',return_token_type_ids=__lowerCamelCase )
A__ = BertGenerationConfig()
A__ = BertGenerationEncoder(__lowerCamelCase )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__lowerCamelCase )
model(**__lowerCamelCase )
@slow
def UpperCamelCase ( self ):
# fmt: off
A__ = {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase,model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''',revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''',)
| 212 | 0 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE_ ( __A : np.ndarray , __A : tuple[int, int] , __A : tuple[int, int] , __A : bool , ) -> tuple[float | int, list[tuple[int, int]]]:
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = grid.shape
_SCREAMING_SNAKE_CASE = [-1, 1, 0, 0]
_SCREAMING_SNAKE_CASE = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = [(0, source)], set()
_SCREAMING_SNAKE_CASE = np.full((rows, cols) , np.inf )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = np.empty((rows, cols) , dtype=__A )
_SCREAMING_SNAKE_CASE = None
while queue:
((_SCREAMING_SNAKE_CASE), (_SCREAMING_SNAKE_CASE)) = heappop(__A )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
_SCREAMING_SNAKE_CASE = []
while (x, y) != source:
path.append((x, y) )
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = predecessors[x, y]
path.append(__A ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(__A ) ):
_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
_SCREAMING_SNAKE_CASE = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(__A , (dist + 1, (nx, ny)) )
_SCREAMING_SNAKE_CASE = dist + 1
_SCREAMING_SNAKE_CASE = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 418 |
'''simple docstring'''
from collections.abc import Callable
def SCREAMING_SNAKE_CASE_ ( __A : Callable[[float], float] , __A : float , __A : float ) -> float:
_SCREAMING_SNAKE_CASE = a
_SCREAMING_SNAKE_CASE = b
if function(__A ) == 0: # one of the a or b is a root for the function
return a
elif function(__A ) == 0:
return b
elif (
function(__A ) * function(__A ) > 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:
_SCREAMING_SNAKE_CASE = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(__A ) == 0:
return mid
elif function(__A ) * function(__A ) < 0:
_SCREAMING_SNAKE_CASE = mid
else:
_SCREAMING_SNAKE_CASE = mid
_SCREAMING_SNAKE_CASE = start + (end - start) / 2.0
return mid
def SCREAMING_SNAKE_CASE_ ( __A : float ) -> float:
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 10_00))
import doctest
doctest.testmod()
| 418 | 1 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def __lowerCamelCase ( __snake_case : Tuple, __snake_case : str ) -> Optional[int]:
"""simple docstring"""
A__ : Any =checkpoint
A__ : Optional[int] ={}
A__ : Union[str, Any] =vae_state_dict["""encoder.conv_in.weight"""]
A__ : Optional[int] =vae_state_dict["""encoder.conv_in.bias"""]
A__ : Union[str, Any] =vae_state_dict["""encoder.conv_out.weight"""]
A__ : Optional[int] =vae_state_dict["""encoder.conv_out.bias"""]
A__ : List[str] =vae_state_dict["""encoder.norm_out.weight"""]
A__ : Dict =vae_state_dict["""encoder.norm_out.bias"""]
A__ : int =vae_state_dict["""decoder.conv_in.weight"""]
A__ : List[str] =vae_state_dict["""decoder.conv_in.bias"""]
A__ : Tuple =vae_state_dict["""decoder.conv_out.weight"""]
A__ : List[Any] =vae_state_dict["""decoder.conv_out.bias"""]
A__ : Union[str, Any] =vae_state_dict["""decoder.norm_out.weight"""]
A__ : int =vae_state_dict["""decoder.norm_out.bias"""]
A__ : Dict =vae_state_dict["""quant_conv.weight"""]
A__ : Dict =vae_state_dict["""quant_conv.bias"""]
A__ : Dict =vae_state_dict["""post_quant_conv.weight"""]
A__ : List[Any] =vae_state_dict["""post_quant_conv.bias"""]
# Retrieves the keys for the encoder down blocks only
A__ : Dict =len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """encoder.down""" in layer} )
A__ : List[Any] ={
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(__snake_case )
}
# Retrieves the keys for the decoder up blocks only
A__ : int =len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """decoder.up""" in layer} )
A__ : Union[str, Any] ={
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(__snake_case )
}
for i in range(__snake_case ):
A__ : Optional[int] =[key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
A__ : Optional[Any] =vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.weight" )
A__ : Union[str, Any] =vae_state_dict.pop(
f"encoder.down.{i}.downsample.conv.bias" )
A__ : Union[str, Any] =renew_vae_resnet_paths(__snake_case )
A__ : Dict ={"""old""": f"down.{i}.block", """new""": f"down_blocks.{i}.resnets"}
assign_to_checkpoint(__snake_case, __snake_case, __snake_case, additional_replacements=[meta_path], config=__snake_case )
A__ : Union[str, Any] =[key for key in vae_state_dict if """encoder.mid.block""" in key]
A__ : str =2
for i in range(1, num_mid_res_blocks + 1 ):
A__ : List[Any] =[key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
A__ : Dict =renew_vae_resnet_paths(__snake_case )
A__ : List[Any] ={"""old""": f"mid.block_{i}", """new""": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(__snake_case, __snake_case, __snake_case, additional_replacements=[meta_path], config=__snake_case )
A__ : Optional[int] =[key for key in vae_state_dict if """encoder.mid.attn""" in key]
A__ : Union[str, Any] =renew_vae_attention_paths(__snake_case )
A__ : Dict ={"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(__snake_case, __snake_case, __snake_case, additional_replacements=[meta_path], config=__snake_case )
conv_attn_to_linear(__snake_case )
for i in range(__snake_case ):
A__ : Any =num_up_blocks - 1 - i
A__ : Union[str, Any] =[
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
]
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
A__ : List[Any] =vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.weight"
]
A__ : Union[str, Any] =vae_state_dict[
f"decoder.up.{block_id}.upsample.conv.bias"
]
A__ : Dict =renew_vae_resnet_paths(__snake_case )
A__ : Union[str, Any] ={"""old""": f"up.{block_id}.block", """new""": f"up_blocks.{i}.resnets"}
assign_to_checkpoint(__snake_case, __snake_case, __snake_case, additional_replacements=[meta_path], config=__snake_case )
A__ : Optional[Any] =[key for key in vae_state_dict if """decoder.mid.block""" in key]
A__ : int =2
for i in range(1, num_mid_res_blocks + 1 ):
A__ : Union[str, Any] =[key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
A__ : Optional[Any] =renew_vae_resnet_paths(__snake_case )
A__ : Optional[int] ={"""old""": f"mid.block_{i}", """new""": f"mid_block.resnets.{i - 1}"}
assign_to_checkpoint(__snake_case, __snake_case, __snake_case, additional_replacements=[meta_path], config=__snake_case )
A__ : Union[str, Any] =[key for key in vae_state_dict if """decoder.mid.attn""" in key]
A__ : Union[str, Any] =renew_vae_attention_paths(__snake_case )
A__ : List[Any] ={"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
assign_to_checkpoint(__snake_case, __snake_case, __snake_case, additional_replacements=[meta_path], config=__snake_case )
conv_attn_to_linear(__snake_case )
return new_checkpoint
def __lowerCamelCase ( __snake_case : str, __snake_case : str, ) -> Dict:
"""simple docstring"""
A__ : int =requests.get(
""" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml""" )
A__ : Optional[int] =io.BytesIO(r.content )
A__ : Dict =OmegaConf.load(__snake_case )
A__ : int =512
A__ : Any ="""cuda""" if torch.cuda.is_available() else """cpu"""
if checkpoint_path.endswith("""safetensors""" ):
from safetensors import safe_open
A__ : Union[str, Any] ={}
with safe_open(__snake_case, framework="""pt""", device="""cpu""" ) as f:
for key in f.keys():
A__ : Optional[Any] =f.get_tensor(__snake_case )
else:
A__ : Optional[Any] =torch.load(__snake_case, map_location=__snake_case )["""state_dict"""]
# Convert the VAE model.
A__ : Any =create_vae_diffusers_config(__snake_case, image_size=__snake_case )
A__ : Optional[int] =custom_convert_ldm_vae_checkpoint(__snake_case, __snake_case )
A__ : str =AutoencoderKL(**__snake_case )
vae.load_state_dict(__snake_case )
vae.save_pretrained(__snake_case )
if __name__ == "__main__":
__snake_case : str = argparse.ArgumentParser()
parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
__snake_case : Optional[int] = parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 715 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lowerCamelCase ( __snake_case : int ) -> Optional[int]:
"""simple docstring"""
random.seed(__snake_case )
np.random.seed(__snake_case )
torch.manual_seed(__snake_case )
torch.cuda.manual_seed_all(__snake_case )
# ^^ safe to call this function even if cuda is not available
class lowerCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase_ : Iterable[torch.nn.Parameter] , lowerCAmelCase_ : float = 0.9999 , lowerCAmelCase_ : float = 0.0 , lowerCAmelCase_ : int = 0 , lowerCAmelCase_ : bool = False , lowerCAmelCase_ : Union[float, int] = 1.0 , lowerCAmelCase_ : Union[float, int] = 2 / 3 , lowerCAmelCase_ : Optional[Any] = None , lowerCAmelCase_ : Dict[str, Any] = None , **lowerCAmelCase_ : Optional[Any] , ) -> List[str]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , torch.nn.Module ):
A__ : Optional[Any] =(
"""Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
"""Please pass the parameters of the module instead."""
)
deprecate(
"""passing a `torch.nn.Module` to `ExponentialMovingAverage`""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ , )
A__ : List[str] =parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
A__ : int =True
if kwargs.get("""max_value""" , lowerCAmelCase_ ) is not None:
A__ : Tuple ="""The `max_value` argument is deprecated. Please use `decay` instead."""
deprecate("""max_value""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ )
A__ : Union[str, Any] =kwargs["""max_value"""]
if kwargs.get("""min_value""" , lowerCAmelCase_ ) is not None:
A__ : List[str] ="""The `min_value` argument is deprecated. Please use `min_decay` instead."""
deprecate("""min_value""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ )
A__ : Optional[Any] =kwargs["""min_value"""]
A__ : Any =list(lowerCAmelCase_ )
A__ : int =[p.clone().detach() for p in parameters]
if kwargs.get("""device""" , lowerCAmelCase_ ) is not None:
A__ : List[str] ="""The `device` argument is deprecated. Please use `to` instead."""
deprecate("""device""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ )
self.to(device=kwargs["""device"""] )
A__ : Optional[int] =None
A__ : Any =decay
A__ : List[Any] =min_decay
A__ : Optional[int] =update_after_step
A__ : List[str] =use_ema_warmup
A__ : str =inv_gamma
A__ : Union[str, Any] =power
A__ : str =0
A__ : str =None # set in `step()`
A__ : List[str] =model_cls
A__ : Optional[int] =model_config
@classmethod
def lowercase__ ( cls : Dict , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Dict ) -> "EMAModel":
'''simple docstring'''
A__ , A__ : Tuple =model_cls.load_config(lowerCAmelCase_ , return_unused_kwargs=lowerCAmelCase_ )
A__ : Optional[Any] =model_cls.from_pretrained(lowerCAmelCase_ )
A__ : Optional[Any] =cls(model.parameters() , model_cls=lowerCAmelCase_ , model_config=model.config )
ema_model.load_state_dict(lowerCAmelCase_ )
return ema_model
def lowercase__ ( self : List[str] , lowerCAmelCase_ : Tuple ) -> List[Any]:
'''simple docstring'''
if self.model_cls is None:
raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__.""" )
if self.model_config is None:
raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__.""" )
A__ : Optional[int] =self.model_cls.from_config(self.model_config )
A__ : Optional[Any] =self.state_dict()
state_dict.pop("""shadow_params""" , lowerCAmelCase_ )
model.register_to_config(**lowerCAmelCase_ )
self.copy_to(model.parameters() )
model.save_pretrained(lowerCAmelCase_ )
def lowercase__ ( self : Dict , lowerCAmelCase_ : int ) -> float:
'''simple docstring'''
A__ : Optional[int] =max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
A__ : List[Any] =1 - (1 + step / self.inv_gamma) ** -self.power
else:
A__ : Union[str, Any] =(1 + step) / (10 + step)
A__ : str =min(lowerCAmelCase_ , self.decay )
# make sure decay is not smaller than min_decay
A__ : int =max(lowerCAmelCase_ , self.min_decay )
return cur_decay_value
@torch.no_grad()
def lowercase__ ( self : Optional[int] , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> Optional[Any]:
'''simple docstring'''
if isinstance(lowerCAmelCase_ , torch.nn.Module ):
A__ : Any =(
"""Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
"""Please pass the parameters of the module instead."""
)
deprecate(
"""passing a `torch.nn.Module` to `ExponentialMovingAverage.step`""" , """1.0.0""" , lowerCAmelCase_ , standard_warn=lowerCAmelCase_ , )
A__ : Optional[int] =parameters.parameters()
A__ : Dict =list(lowerCAmelCase_ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
A__ : Any =self.get_decay(self.optimization_step )
A__ : Optional[int] =decay
A__ : List[str] =1 - decay
A__ : str =contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , lowerCAmelCase_ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
A__ : List[Any] =deepspeed.zero.GatheredParameters(lowerCAmelCase_ , modifier_rank=lowerCAmelCase_ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(lowerCAmelCase_ )
def lowercase__ ( self : Tuple , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
A__ : Optional[Any] =list(lowerCAmelCase_ )
for s_param, param in zip(self.shadow_params , lowerCAmelCase_ ):
param.data.copy_(s_param.to(param.device ).data )
def lowercase__ ( self : int , lowerCAmelCase_ : Dict=None , lowerCAmelCase_ : List[Any]=None ) -> None:
'''simple docstring'''
A__ : str =[
p.to(device=lowerCAmelCase_ , dtype=lowerCAmelCase_ ) if p.is_floating_point() else p.to(device=lowerCAmelCase_ )
for p in self.shadow_params
]
def lowercase__ ( self : Optional[Any] ) -> dict:
'''simple docstring'''
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def lowercase__ ( self : Tuple , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
A__ : List[str] =[param.detach().cpu().clone() for param in parameters]
def lowercase__ ( self : List[str] , lowerCAmelCase_ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
if self.temp_stored_params is None:
raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`""" )
for c_param, param in zip(self.temp_stored_params , lowerCAmelCase_ ):
param.data.copy_(c_param.data )
# Better memory-wise.
A__ : List[str] =None
def lowercase__ ( self : List[str] , lowerCAmelCase_ : dict ) -> None:
'''simple docstring'''
A__ : List[Any] =copy.deepcopy(lowerCAmelCase_ )
A__ : List[Any] =state_dict.get("""decay""" , self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError("""Decay must be between 0 and 1""" )
A__ : List[Any] =state_dict.get("""min_decay""" , self.min_decay )
if not isinstance(self.min_decay , lowerCAmelCase_ ):
raise ValueError("""Invalid min_decay""" )
A__ : Tuple =state_dict.get("""optimization_step""" , self.optimization_step )
if not isinstance(self.optimization_step , lowerCAmelCase_ ):
raise ValueError("""Invalid optimization_step""" )
A__ : Any =state_dict.get("""update_after_step""" , self.update_after_step )
if not isinstance(self.update_after_step , lowerCAmelCase_ ):
raise ValueError("""Invalid update_after_step""" )
A__ : str =state_dict.get("""use_ema_warmup""" , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , lowerCAmelCase_ ):
raise ValueError("""Invalid use_ema_warmup""" )
A__ : str =state_dict.get("""inv_gamma""" , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError("""Invalid inv_gamma""" )
A__ : Tuple =state_dict.get("""power""" , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError("""Invalid power""" )
A__ : Tuple =state_dict.get("""shadow_params""" , lowerCAmelCase_ )
if shadow_params is not None:
A__ : List[str] =shadow_params
if not isinstance(self.shadow_params , lowerCAmelCase_ ):
raise ValueError("""shadow_params must be a list""" )
if not all(isinstance(lowerCAmelCase_ , torch.Tensor ) for p in self.shadow_params ):
raise ValueError("""shadow_params must all be Tensors""" )
| 687 | 0 |
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 _a ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =torch.nn.Linear(10 , 10 )
_UpperCAmelCase =torch.optim.SGD(model.parameters() , 0.1 )
_UpperCAmelCase =Accelerator()
_UpperCAmelCase =accelerator.prepare(_snake_case )
try:
pickle.loads(pickle.dumps(_snake_case ) )
except Exception as e:
self.fail(F"Accelerated optimizer pickling failed with {e}" )
AcceleratorState._reset_state()
| 408 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _a ( A__ , A__ , unittest.TestCase ):
"""simple docstring"""
snake_case =StableDiffusionSAGPipeline
snake_case =TEXT_TO_IMAGE_PARAMS
snake_case =TEXT_TO_IMAGE_BATCH_PARAMS
snake_case =TEXT_TO_IMAGE_IMAGE_PARAMS
snake_case =TEXT_TO_IMAGE_IMAGE_PARAMS
snake_case =False
def SCREAMING_SNAKE_CASE ( self ):
torch.manual_seed(0 )
_UpperCAmelCase =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 , )
_UpperCAmelCase =DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
_UpperCAmelCase =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 , )
torch.manual_seed(0 )
_UpperCAmelCase =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
_UpperCAmelCase =CLIPTextModel(_snake_case )
_UpperCAmelCase =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_UpperCAmelCase ={
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case=0 ):
if str(_snake_case ).startswith("mps" ):
_UpperCAmelCase =torch.manual_seed(_snake_case )
else:
_UpperCAmelCase =torch.Generator(device=_snake_case ).manual_seed(_snake_case )
_UpperCAmelCase ={
"prompt": ".",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 1.0,
"sag_scale": 1.0,
"output_type": "numpy",
}
return inputs
def SCREAMING_SNAKE_CASE ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class _a ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
_UpperCAmelCase =sag_pipe.to(_snake_case )
sag_pipe.set_progress_bar_config(disable=_snake_case )
_UpperCAmelCase ="."
_UpperCAmelCase =torch.manual_seed(0 )
_UpperCAmelCase =sag_pipe(
[prompt] , generator=_snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
_UpperCAmelCase =output.images
_UpperCAmelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase =np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
_UpperCAmelCase =sag_pipe.to(_snake_case )
sag_pipe.set_progress_bar_config(disable=_snake_case )
_UpperCAmelCase ="."
_UpperCAmelCase =torch.manual_seed(0 )
_UpperCAmelCase =sag_pipe(
[prompt] , generator=_snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
_UpperCAmelCase =output.images
_UpperCAmelCase =image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase =np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
_UpperCAmelCase =sag_pipe.to(_snake_case )
sag_pipe.set_progress_bar_config(disable=_snake_case )
_UpperCAmelCase ="."
_UpperCAmelCase =torch.manual_seed(0 )
_UpperCAmelCase =sag_pipe(
[prompt] , width=768 , height=512 , generator=_snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" , )
_UpperCAmelCase =output.images
assert image.shape == (1, 512, 768, 3)
| 408 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
def __magic_name__ ( self ):
a_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase_ , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(lowercase_ , """num_attention_heads""" ) )
class lowerCamelCase_ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=[128, 256, 384] , _SCREAMING_SNAKE_CASE=[4, 6, 8] , _SCREAMING_SNAKE_CASE=[2, 3, 4] , _SCREAMING_SNAKE_CASE=[16, 16, 16] , _SCREAMING_SNAKE_CASE=0 , _SCREAMING_SNAKE_CASE=[2, 2, 2] , _SCREAMING_SNAKE_CASE=[2, 2, 2] , _SCREAMING_SNAKE_CASE=0.0_2 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=2 , ):
a_ = parent
a_ = batch_size
a_ = image_size
a_ = num_channels
a_ = kernel_size
a_ = stride
a_ = padding
a_ = hidden_sizes
a_ = num_attention_heads
a_ = depths
a_ = key_dim
a_ = drop_path_rate
a_ = patch_size
a_ = attention_ratio
a_ = mlp_ratio
a_ = initializer_range
a_ = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
a_ = is_training
a_ = use_labels
a_ = num_labels
a_ = initializer_range
def __magic_name__ ( self ):
a_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a_ = None
if self.use_labels:
a_ = ids_tensor([self.batch_size] , self.num_labels )
a_ = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self ):
return LevitConfig(
image_size=self.image_size , num_channels=self.num_channels , kernel_size=self.kernel_size , stride=self.stride , padding=self.padding , patch_size=self.patch_size , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , depths=self.depths , key_dim=self.key_dim , drop_path_rate=self.drop_path_rate , mlp_ratio=self.mlp_ratio , attention_ratio=self.attention_ratio , initializer_range=self.initializer_range , down_ops=self.down_ops , )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = LevitModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
a_ = model(lowercase_ )
a_ = (self.image_size, self.image_size)
a_ , a_ = image_size[0], image_size[1]
for _ in range(4 ):
a_ = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
a_ = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) , )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = self.num_labels
a_ = LevitForImageClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
a_ = model(lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ , a_ , a_ = config_and_inputs
a_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
_lowerCamelCase : Dict = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
_lowerCamelCase : int = (
{
"""feature-extraction""": LevitModel,
"""image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : List[str] = False
_lowerCamelCase : Optional[Any] = False
_lowerCamelCase : List[Any] = False
_lowerCamelCase : Dict = False
def __magic_name__ ( self ):
a_ = LevitModelTester(self )
a_ = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 )
def __magic_name__ ( self ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __magic_name__ ( self ):
return
@unittest.skip(reason="""Levit does not use inputs_embeds""" )
def __magic_name__ ( self ):
pass
@unittest.skip(reason="""Levit does not support input and output embeddings""" )
def __magic_name__ ( self ):
pass
@unittest.skip(reason="""Levit does not output attentions""" )
def __magic_name__ ( self ):
pass
def __magic_name__ ( self ):
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = model_class(lowercase_ )
a_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a_ = [*signature.parameters.keys()]
a_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase_ )
def __magic_name__ ( self ):
def check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
a_ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
a_ = outputs.hidden_states
a_ = len(self.model_tester.depths ) + 1
self.assertEqual(len(lowercase_ ) , lowercase_ )
a_ = (self.model_tester.image_size, self.model_tester.image_size)
a_ , a_ = image_size[0], image_size[1]
for _ in range(4 ):
a_ = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
a_ = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [
height * width,
self.model_tester.hidden_sizes[0],
] , )
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a_ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
a_ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def __magic_name__ ( self ):
pass
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
a_ = super()._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __magic_name__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def __magic_name__ ( self ):
a_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
def __magic_name__ ( self ):
if not self.model_tester.is_training:
return
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
a_ = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowercase_ )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
a_ = model_class(lowercase_ )
model.to(lowercase_ )
model.train()
a_ = self._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
a_ = model(**lowercase_ ).loss
loss.backward()
def __magic_name__ ( self ):
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
a_ = False
a_ = True
for model_class in self.all_model_classes:
if model_class in get_values(lowercase_ ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
a_ = model_class(lowercase_ )
model.gradient_checkpointing_enable()
model.to(lowercase_ )
model.train()
a_ = self._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
a_ = model(**lowercase_ ).loss
loss.backward()
def __magic_name__ ( self ):
a_ , a_ = self.model_tester.prepare_config_and_inputs_for_common()
a_ = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowercase_ ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}""" ):
a_ = problem_type["""title"""]
a_ = problem_type["""num_labels"""]
a_ = model_class(lowercase_ )
model.to(lowercase_ )
model.train()
a_ = self._prepare_for_class(lowercase_ , lowercase_ , return_labels=lowercase_ )
if problem_type["num_labels"] > 1:
a_ = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
a_ = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowercase_ ) as warning_list:
a_ = model(**lowercase_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f"""Something is going wrong in the regression problem: intercepted {w.message}""" )
loss.backward()
@slow
def __magic_name__ ( self ):
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ = LevitModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def __SCREAMING_SNAKE_CASE ( ) -> List[str]:
"""simple docstring"""
a_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self ):
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def __magic_name__ ( self ):
a_ = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
lowercase_ )
a_ = self.default_image_processor
a_ = prepare_img()
a_ = image_processor(images=lowercase_ , return_tensors="""pt""" ).to(lowercase_ )
# forward pass
with torch.no_grad():
a_ = model(**lowercase_ )
# verify the logits
a_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowercase_ )
a_ = torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1E-4 ) ) | 716 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class lowerCamelCase_ ( unittest.TestCase ):
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=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=5 , _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=0.0_2 , _SCREAMING_SNAKE_CASE=4 , ):
a_ = parent
a_ = batch_size
a_ = seq_length
a_ = is_training
a_ = use_attention_mask
a_ = use_token_type_ids
a_ = use_labels
a_ = vocab_size
a_ = hidden_size
a_ = num_hidden_layers
a_ = num_attention_heads
a_ = intermediate_size
a_ = hidden_act
a_ = hidden_dropout_prob
a_ = attention_probs_dropout_prob
a_ = max_position_embeddings
a_ = type_vocab_size
a_ = type_sequence_label_size
a_ = initializer_range
a_ = num_choices
def __magic_name__ ( self ):
a_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a_ = None
if self.use_attention_mask:
a_ = random_attention_mask([self.batch_size, self.seq_length] )
a_ = None
if self.use_token_type_ids:
a_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
a_ = RobertaPreLayerNormConfig(
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 , )
return config, input_ids, token_type_ids, attention_mask
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ , a_ , a_ , a_ = config_and_inputs
a_ = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ , a_ , a_ , a_ = config_and_inputs
a_ = True
a_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
a_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
_lowerCamelCase : Optional[Any] = True
_lowerCamelCase : List[Any] = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __magic_name__ ( self ):
a_ = FlaxRobertaPreLayerNormModelTester(self )
@slow
def __magic_name__ ( self ):
for model_class_name in self.all_model_classes:
a_ = model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_SCREAMING_SNAKE_CASE )
a_ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@require_flax
class lowerCamelCase_ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ):
a_ = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_SCREAMING_SNAKE_CASE )
a_ = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
a_ = model(_SCREAMING_SNAKE_CASE )[0]
a_ = [1, 11, 5_0265]
self.assertEqual(list(output.shape ) , _SCREAMING_SNAKE_CASE )
# compare the actual values for a slice.
a_ = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) )
@slow
def __magic_name__ ( self ):
a_ = FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" , from_pt=_SCREAMING_SNAKE_CASE )
a_ = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
a_ = model(_SCREAMING_SNAKE_CASE )[0]
# compare the actual values for a slice.
a_ = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) ) | 403 | 0 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
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
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCAmelCase_ = logging.get_logger(__name__)
class __A ( enum.Enum ):
'''simple docstring'''
lowerCAmelCase : int = 0
lowerCAmelCase : str = 1
@add_end_docstrings(lowerCamelCase__ )
class __A ( lowerCamelCase__ ):
'''simple docstring'''
lowerCAmelCase : Optional[int] = 'generated'
def __init__( self : List[str] ,*_snake_case : List[Any] ,**_snake_case : str ) -> Optional[Any]:
"""simple docstring"""
super().__init__(*_snake_case ,**_snake_case )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Union[str, Any]=None ,_snake_case : List[Any]=None ,_snake_case : int=None ,_snake_case : int=None ,_snake_case : Any=None ,_snake_case : Dict=None ,**_snake_case : List[Any] ,) -> Optional[Any]:
"""simple docstring"""
lowercase__ : str = {}
if truncation is not None:
lowercase__ : str = truncation
lowercase__ : Tuple = generate_kwargs
lowercase__ : Union[str, Any] = {}
if return_tensors is not None and return_type is None:
lowercase__ : List[Any] = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
lowercase__ : Optional[Any] = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ : Union[str, Any] = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ : Any = self.tokenizer.encode(_snake_case ,add_special_tokens=_snake_case )
if len(_snake_case ) > 1:
warnings.warn(
'''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
''' the stop sequence will be used as the stop sequence string in the interim.''' )
lowercase__ : Tuple = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase ( self : Any ,_snake_case : List[Any] ,_snake_case : Dict ,_snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
return True
def UpperCAmelCase ( self : List[Any] ,*_snake_case : Dict ,_snake_case : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Any = self.model.config.prefix if self.model.config.prefix is not None else ''''''
if isinstance(args[0] ,_snake_case ):
if self.tokenizer.pad_token_id is None:
raise ValueError('''Please make sure that the tokenizer has a pad_token_id when using a batch input''' )
lowercase__ : Union[str, Any] = ([prefix + arg for arg in args[0]],)
lowercase__ : str = True
elif isinstance(args[0] ,_snake_case ):
lowercase__ : Dict = (prefix + args[0],)
lowercase__ : Tuple = False
else:
raise ValueError(
f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`""" )
lowercase__ : List[Any] = self.tokenizer(*_snake_case ,padding=_snake_case ,truncation=_snake_case ,return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self : str ,*_snake_case : str ,**_snake_case : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[Any] = super().__call__(*_snake_case ,**_snake_case )
if (
isinstance(args[0] ,_snake_case )
and all(isinstance(_snake_case ,_snake_case ) for el in args[0] )
and all(len(_snake_case ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : Dict ,_snake_case : List[str]=TruncationStrategy.DO_NOT_TRUNCATE ,**_snake_case : Optional[int] ) -> int:
"""simple docstring"""
lowercase__ : int = self._parse_and_tokenize(_snake_case ,truncation=_snake_case ,**_snake_case )
return inputs
def UpperCAmelCase ( self : Optional[int] ,_snake_case : List[Any] ,**_snake_case : Dict ) -> Tuple:
"""simple docstring"""
if self.framework == "pt":
lowercase__ , lowercase__ : Optional[Any] = model_inputs['''input_ids'''].shape
elif self.framework == "tf":
lowercase__ , lowercase__ : Any = tf.shape(model_inputs['''input_ids'''] ).numpy()
lowercase__ : Optional[Any] = generate_kwargs.get('''min_length''' ,self.model.config.min_length )
lowercase__ : int = generate_kwargs.get('''max_length''' ,self.model.config.max_length )
self.check_inputs(_snake_case ,generate_kwargs['''min_length'''] ,generate_kwargs['''max_length'''] )
lowercase__ : Any = self.model.generate(**_snake_case ,**_snake_case )
lowercase__ : Optional[Any] = output_ids.shape[0]
if self.framework == "pt":
lowercase__ : List[str] = output_ids.reshape(_snake_case ,out_b // in_b ,*output_ids.shape[1:] )
elif self.framework == "tf":
lowercase__ : Tuple = tf.reshape(_snake_case ,(in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def UpperCAmelCase ( self : Tuple ,_snake_case : Optional[int] ,_snake_case : str=ReturnType.TEXT ,_snake_case : str=False ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
lowercase__ : str = {f"""{self.return_name}_token_ids""": output_ids}
elif return_type == ReturnType.TEXT:
lowercase__ : Any = {
f"""{self.return_name}_text""": self.tokenizer.decode(
_snake_case ,skip_special_tokens=_snake_case ,clean_up_tokenization_spaces=_snake_case ,)
}
records.append(_snake_case )
return records
@add_end_docstrings(lowerCamelCase__ )
class __A ( lowerCamelCase__ ):
'''simple docstring'''
lowerCAmelCase : List[Any] = 'summary'
def __call__( self : str ,*_snake_case : int ,**_snake_case : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return super().__call__(*_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Optional[int] ,_snake_case : Optional[int] ,_snake_case : Tuple ,_snake_case : Tuple ) -> Any:
"""simple docstring"""
if max_length < min_length:
logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}.""" )
if input_length < max_length:
logger.warning(
f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
'''a summarization task, where outputs shorter than the input are typically wanted, you might '''
f"""consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})""" )
@add_end_docstrings(lowerCamelCase__ )
class __A ( lowerCamelCase__ ):
'''simple docstring'''
lowerCAmelCase : Optional[Any] = 'translation'
def UpperCAmelCase ( self : str ,_snake_case : Dict ,_snake_case : Dict ,_snake_case : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if input_length > 0.9 * max_length:
logger.warning(
f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
'''increasing your max_length manually, e.g. translator(\'...\', max_length=400)''' )
return True
def UpperCAmelCase ( self : Tuple ,*_snake_case : int ,_snake_case : Dict=TruncationStrategy.DO_NOT_TRUNCATE ,_snake_case : Union[str, Any]=None ,_snake_case : List[str]=None ) -> List[str]:
"""simple docstring"""
if getattr(self.tokenizer ,'''_build_translation_inputs''' ,_snake_case ):
return self.tokenizer._build_translation_inputs(
*_snake_case ,return_tensors=self.framework ,truncation=_snake_case ,src_lang=_snake_case ,tgt_lang=_snake_case )
else:
return super()._parse_and_tokenize(*_snake_case ,truncation=_snake_case )
def UpperCAmelCase ( self : Optional[Any] ,_snake_case : Tuple=None ,_snake_case : Optional[Any]=None ,**_snake_case : Optional[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ , lowercase__ , lowercase__ : Optional[Any] = super()._sanitize_parameters(**_snake_case )
if src_lang is not None:
lowercase__ : Any = src_lang
if tgt_lang is not None:
lowercase__ : Any = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
lowercase__ : Optional[Any] = kwargs.get('''task''' ,self.task )
lowercase__ : Optional[int] = task.split('''_''' )
if task and len(_snake_case ) == 4:
# translation, XX, to YY
lowercase__ : str = items[1]
lowercase__ : Optional[int] = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self : Optional[Any] ,*_snake_case : int ,**_snake_case : Union[str, Any] ) -> int:
"""simple docstring"""
return super().__call__(*_snake_case ,**_snake_case )
| 560 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
a_ : Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
super().__init__(*lowerCAmelCase, **lowerCAmelCase )
requires_backends(self, '''vision''' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def lowercase__ ( self, lowerCAmelCase=None, lowerCAmelCase=None, lowerCAmelCase=None ):
"""simple docstring"""
lowerCamelCase_ ={}
lowerCamelCase_ ={}
if prompt is not None:
lowerCamelCase_ =prompt
if generate_kwargs is not None:
lowerCamelCase_ =generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
lowerCamelCase_ ={}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
''' please use only one''' )
lowerCamelCase_ =max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self, lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
return super().__call__(lowerCAmelCase, **lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None ):
"""simple docstring"""
lowerCamelCase_ =load_image(lowerCAmelCase )
if prompt is not None:
if not isinstance(lowerCAmelCase, lowerCAmelCase ):
raise ValueError(
f'''Received an invalid text input, got - {type(lowerCAmelCase )} - but expected a single string. '''
'''Note also that one single text can be provided for conditional image to text generation.''' )
lowerCamelCase_ =self.model.config.model_type
if model_type == "git":
lowerCamelCase_ =self.image_processor(images=lowerCAmelCase, return_tensors=self.framework )
lowerCamelCase_ =self.tokenizer(text=lowerCAmelCase, add_special_tokens=lowerCAmelCase ).input_ids
lowerCamelCase_ =[self.tokenizer.cls_token_id] + input_ids
lowerCamelCase_ =torch.tensor(lowerCAmelCase ).unsqueeze(0 )
model_inputs.update({'''input_ids''': input_ids} )
elif model_type == "pix2struct":
lowerCamelCase_ =self.image_processor(images=lowerCAmelCase, header_text=lowerCAmelCase, return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
lowerCamelCase_ =self.image_processor(images=lowerCAmelCase, return_tensors=self.framework )
lowerCamelCase_ =self.tokenizer(lowerCAmelCase, return_tensors=self.framework )
model_inputs.update(lowerCAmelCase )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
lowerCamelCase_ =self.image_processor(images=lowerCAmelCase, return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
lowerCamelCase_ =None
return model_inputs
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase=None ):
"""simple docstring"""
if (
"input_ids" in model_inputs
and isinstance(model_inputs['''input_ids'''], lowerCAmelCase )
and all(x is None for x in model_inputs['''input_ids'''] )
):
lowerCamelCase_ =None
if generate_kwargs is None:
lowerCamelCase_ ={}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
lowerCamelCase_ =model_inputs.pop(self.model.main_input_name )
lowerCamelCase_ =self.model.generate(lowerCAmelCase, **lowerCAmelCase, **lowerCAmelCase )
return model_outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =[]
for output_ids in model_outputs:
lowerCamelCase_ ={
'''generated_text''': self.tokenizer.decode(
lowerCAmelCase, skip_special_tokens=lowerCAmelCase, )
}
records.append(lowerCAmelCase )
return records
| 676 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
'configuration_blenderbot_small': [
'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotSmallConfig',
'BlenderbotSmallOnnxConfig',
],
'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = ['BlenderbotSmallTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotSmallForCausalLM',
'BlenderbotSmallForConditionalGeneration',
'BlenderbotSmallModel',
'BlenderbotSmallPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'TFBlenderbotSmallForConditionalGeneration',
'TFBlenderbotSmallModel',
'TFBlenderbotSmallPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase = [
'FlaxBlenderbotSmallForConditionalGeneration',
'FlaxBlenderbotSmallModel',
'FlaxBlenderbotSmallPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotSmallConfig,
BlenderbotSmallOnnxConfig,
)
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot_small import (
BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotSmallForCausalLM,
BlenderbotSmallForConditionalGeneration,
BlenderbotSmallModel,
BlenderbotSmallPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot_small import (
TFBlenderbotSmallForConditionalGeneration,
TFBlenderbotSmallModel,
TFBlenderbotSmallPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallPreTrainedModel,
)
else:
import sys
UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 344 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version
UpperCAmelCase = logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
UpperCAmelCase = {
'base': AutoModel,
'sequence-classification': AutoModelForSequenceClassification,
'question-answering': AutoModelForQuestionAnswering,
'pretraining': AutoModelForPreTraining,
'token-classification': AutoModelForTokenClassification,
'language-modeling': AutoModelWithLMHead,
'summarization': AutoModelForSeqaSeqLM,
'translation': AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
UpperCAmelCase = {
'linear': get_linear_schedule_with_warmup,
'cosine': get_cosine_schedule_with_warmup,
'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
'polynomial': get_polynomial_decay_schedule_with_warmup,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
UpperCAmelCase = sorted(arg_to_scheduler.keys())
UpperCAmelCase = '{' + ', '.join(arg_to_scheduler_choices) + '}'
class __snake_case( pl.LightningModule ):
'''simple docstring'''
def __init__( self , A_ , A_=None , A_="base" , A_=None , A_=None , A_=None , **A_ , ) -> List[Any]:
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(A_ )
lowerCAmelCase = 0
lowerCAmelCase = Path(self.hparams.output_dir )
lowerCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
lowerCAmelCase = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path , **({"""num_labels""": num_labels} if num_labels is not None else {}) , cache_dir=A_ , **A_ , )
else:
lowerCAmelCase = config
lowerCAmelCase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(self.hparams , A_ , A_ ):
assert hasattr(self.config , A_ ), f'model config doesn\'t have a `{p}` attribute'
setattr(self.config , A_ , getattr(self.hparams , A_ ) )
if tokenizer is None:
lowerCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=A_ , )
else:
lowerCAmelCase = tokenizer
lowerCAmelCase = MODEL_MODES[mode]
if model is None:
lowerCAmelCase = self.model_type.from_pretrained(
self.hparams.model_name_or_path , from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path ) , config=self.config , cache_dir=A_ , )
else:
lowerCAmelCase = model
def __snake_case ( self , *A_ , **A_ ) -> List[Any]:
lowerCAmelCase = self.model_type.from_pretrained(*A_ , **A_ )
def __snake_case ( self ) -> Tuple:
lowerCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
lowerCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
lowerCAmelCase = {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
return scheduler
def __snake_case ( self ) -> List[str]:
lowerCAmelCase = self.model
lowerCAmelCase = ["""bias""", """LayerNorm.weight"""]
lowerCAmelCase = [
{
"""params""": [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )
], # check this named paramters
"""weight_decay""": self.hparams.weight_decay,
},
{
"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )],
"""weight_decay""": 0.0,
},
]
if self.hparams.adafactor:
lowerCAmelCase = Adafactor(
A_ , lr=self.hparams.learning_rate , scale_parameter=A_ , relative_step=A_ )
else:
lowerCAmelCase = AdamW(
A_ , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
lowerCAmelCase = optimizer
lowerCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def __snake_case ( self , A_ , A_ ) -> Optional[Any]:
return self.validation_step(A_ , A_ )
def __snake_case ( self , A_ ) -> Tuple:
return self.validation_end(A_ )
def __snake_case ( self ) -> int:
lowerCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
lowerCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def __snake_case ( self , A_ ) -> Union[str, Any]:
if stage == "test":
lowerCAmelCase = len(self.test_dataloader().dataset )
else:
lowerCAmelCase = self.get_dataloader("""train""" , self.hparams.train_batch_size , shuffle=A_ )
lowerCAmelCase = len(self.train_dataloader().dataset )
def __snake_case ( self , A_ , A_ , A_ = False ) -> int:
raise NotImplementedError("""You must implement this for your task""" )
def __snake_case ( self ) -> Any:
return self.train_loader
def __snake_case ( self ) -> Optional[Any]:
return self.get_dataloader("""dev""" , self.hparams.eval_batch_size , shuffle=A_ )
def __snake_case ( self ) -> Tuple:
return self.get_dataloader("""test""" , self.hparams.eval_batch_size , shuffle=A_ )
def __snake_case ( self , A_ ) -> List[str]:
return os.path.join(
self.hparams.data_dir , """cached_{}_{}_{}""".format(
A_ , list(filter(A_ , self.hparams.model_name_or_path.split("""/""" ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def __snake_case ( self , A_ ) -> None:
lowerCAmelCase = self.output_dir.joinpath("""best_tfmr""" )
lowerCAmelCase = self.step_count
self.model.save_pretrained(A_ )
self.tokenizer.save_pretrained(A_ )
@staticmethod
def __snake_case ( A_ , A_ ) -> Dict:
parser.add_argument(
"""--model_name_or_path""" , default=A_ , type=A_ , required=A_ , help="""Path to pretrained model or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--config_name""" , default="""""" , type=A_ , help="""Pretrained config name or path if not the same as model_name""" )
parser.add_argument(
"""--tokenizer_name""" , default=A_ , type=A_ , help="""Pretrained tokenizer name or path if not the same as model_name""" , )
parser.add_argument(
"""--cache_dir""" , default=str(Path(A_ ).parent / """test_run""" / """cache""" ) , type=A_ , help="""Where do you want to store the pre-trained models downloaded from huggingface.co""" , )
parser.add_argument(
"""--encoder_layerdrop""" , type=A_ , help="""Encoder layer dropout probability (Optional). Goes into model.config""" , )
parser.add_argument(
"""--decoder_layerdrop""" , type=A_ , help="""Decoder layer dropout probability (Optional). Goes into model.config""" , )
parser.add_argument(
"""--dropout""" , type=A_ , help="""Dropout probability (Optional). Goes into model.config""" , )
parser.add_argument(
"""--attention_dropout""" , type=A_ , help="""Attention dropout probability (Optional). Goes into model.config""" , )
parser.add_argument("""--learning_rate""" , default=5e-5 , type=A_ , help="""The initial learning rate for Adam.""" )
parser.add_argument(
"""--lr_scheduler""" , default="""linear""" , choices=A_ , metavar=A_ , type=A_ , help="""Learning rate scheduler""" , )
parser.add_argument("""--weight_decay""" , default=0.0 , type=A_ , help="""Weight decay if we apply some.""" )
parser.add_argument("""--adam_epsilon""" , default=1e-8 , type=A_ , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--warmup_steps""" , default=0 , type=A_ , help="""Linear warmup over warmup_steps.""" )
parser.add_argument("""--num_workers""" , default=4 , type=A_ , help="""kwarg passed to DataLoader""" )
parser.add_argument("""--num_train_epochs""" , dest="""max_epochs""" , default=3 , type=A_ )
parser.add_argument("""--train_batch_size""" , default=32 , type=A_ )
parser.add_argument("""--eval_batch_size""" , default=32 , type=A_ )
parser.add_argument("""--adafactor""" , action="""store_true""" )
class __snake_case( pl.Callback ):
'''simple docstring'''
def __snake_case ( self , A_ , A_ ) -> Optional[Any]:
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class __snake_case( pl.Callback ):
'''simple docstring'''
def __snake_case ( self , A_ , A_ ) -> Union[str, Any]:
# print(pl_module.model.rag)
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(A_ )
class __snake_case( pl.Callback ):
'''simple docstring'''
def __snake_case ( self , A_ , A_ ) -> Union[str, Any]:
lowerCAmelCase = trainer.lr_schedulers[0]["""scheduler"""]
lowerCAmelCase = {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(A_ )
def __snake_case ( self , A_ , A_ ) -> Union[str, Any]:
rank_zero_info("""***** Validation results *****""" )
lowerCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(A_ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("""{} = {}\n""".format(A_ , str(metrics[key] ) ) )
def __snake_case ( self , A_ , A_ ) -> Tuple:
rank_zero_info("""***** Test results *****""" )
lowerCAmelCase = trainer.callback_metrics
# Log and save results to file
lowerCAmelCase = os.path.join(pl_module.hparams.output_dir , """test_results.txt""" )
with open(A_ , """w""" ) as writer:
for key in sorted(A_ ):
if key not in ["log", "progress_bar"]:
rank_zero_info("""{} = {}\n""".format(A_ , str(metrics[key] ) ) )
writer.write("""{} = {}\n""".format(A_ , str(metrics[key] ) ) )
def _snake_case ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : str ) -> None:
"""simple docstring"""
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
"""--output_dir""" , default=str(Path(_SCREAMING_SNAKE_CASE ).parent / """test_run""" / """model_checkpoints""" ) , type=_SCREAMING_SNAKE_CASE , help="""The output directory where the model predictions and checkpoints will be written.""" , )
parser.add_argument(
"""--fp16""" , action="""store_true""" , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" , )
parser.add_argument(
"""--fp16_opt_level""" , type=_SCREAMING_SNAKE_CASE , default="""O2""" , help=(
"""For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
"""See details at https://nvidia.github.io/apex/amp.html"""
) , )
parser.add_argument("""--n_tpu_cores""" , dest="""tpu_cores""" , type=_SCREAMING_SNAKE_CASE )
parser.add_argument("""--max_grad_norm""" , dest="""gradient_clip_val""" , default=1.0 , type=_SCREAMING_SNAKE_CASE , help="""Max gradient norm""" )
parser.add_argument("""--do_train""" , action="""store_true""" , help="""Whether to run training.""" )
parser.add_argument("""--do_predict""" , action="""store_true""" , help="""Whether to run predictions on the test set.""" )
parser.add_argument(
"""--gradient_accumulation_steps""" , dest="""accumulate_grad_batches""" , type=_SCREAMING_SNAKE_CASE , default=1 , help="""Number of updates steps to accumulate before performing a backward/update pass.""" , )
parser.add_argument("""--seed""" , type=_SCREAMING_SNAKE_CASE , default=42 , help="""random seed for initialization""" )
parser.add_argument(
"""--data_dir""" , default=str(Path(_SCREAMING_SNAKE_CASE ).parent / """test_run""" / """dummy-train-data""" ) , type=_SCREAMING_SNAKE_CASE , help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task.""" , )
def _snake_case ( _SCREAMING_SNAKE_CASE : BaseTransformer , _SCREAMING_SNAKE_CASE : argparse.Namespace , _SCREAMING_SNAKE_CASE : Tuple=None , _SCREAMING_SNAKE_CASE : List[str]=True , _SCREAMING_SNAKE_CASE : int=[] , _SCREAMING_SNAKE_CASE : Optional[int]=None , _SCREAMING_SNAKE_CASE : List[str]=None , **_SCREAMING_SNAKE_CASE : Dict , ) -> Tuple:
"""simple docstring"""
pl.seed_everything(args.seed )
# init model
lowerCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
# add custom checkpoints
if checkpoint_callback is None:
lowerCAmelCase = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix="""checkpoint""" , monitor="""val_loss""" , mode="""min""" , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_SCREAMING_SNAKE_CASE )
if logging_callback is None:
lowerCAmelCase = LoggingCallback()
lowerCAmelCase = {}
if args.fpaa:
lowerCAmelCase = 16
if args.gpus > 1:
lowerCAmelCase = """auto"""
lowerCAmelCase = """ddp"""
lowerCAmelCase = args.accumulate_grad_batches
lowerCAmelCase = None
lowerCAmelCase = """auto"""
lowerCAmelCase = pl.Trainer.from_argparse_args(
_SCREAMING_SNAKE_CASE , weights_summary=_SCREAMING_SNAKE_CASE , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_SCREAMING_SNAKE_CASE , val_check_interval=1 , num_sanity_val_steps=2 , **_SCREAMING_SNAKE_CASE , )
if args.do_train:
trainer.fit(_SCREAMING_SNAKE_CASE )
else:
print("""RAG modeling tests with new set functions successfuly executed!""" )
return trainer | 344 | 1 |
'''simple docstring'''
from math import ceil
def a__ ( a__ = 10_01 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__SCREAMING_SNAKE_CASE = 2 * i + 1
__SCREAMING_SNAKE_CASE = 2 * i
__SCREAMING_SNAKE_CASE = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
UpperCAmelCase : Tuple = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number')
| 627 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class __a :
def __init__( self : Dict , UpperCAmelCase : int , UpperCAmelCase : int = 13 , UpperCAmelCase : int = 64 , UpperCAmelCase : int = 2 , UpperCAmelCase : int = 3 , UpperCAmelCase : int = 3 , UpperCAmelCase : bool = True , UpperCAmelCase : bool = True , UpperCAmelCase : int = 1_28 , UpperCAmelCase : str=[16, 32, 64, 1_28] , UpperCAmelCase : int = 7 , UpperCAmelCase : int = 4 , UpperCAmelCase : int = 37 , UpperCAmelCase : str = "gelu" , UpperCAmelCase : float = 0.1 , UpperCAmelCase : float = 0.1 , UpperCAmelCase : int = 10 , UpperCAmelCase : float = 0.02 , UpperCAmelCase : int = 2 , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 1_28 , UpperCAmelCase : List[int] = [2, 2, 2, 2] , UpperCAmelCase : int = 2 , UpperCAmelCase : int = 2 , ):
lowerCAmelCase_ : str = parent
lowerCAmelCase_ : Optional[int] = batch_size
lowerCAmelCase_ : Dict = image_size
lowerCAmelCase_ : Optional[Any] = patch_size
lowerCAmelCase_ : List[str] = num_channels
lowerCAmelCase_ : Tuple = is_training
lowerCAmelCase_ : Any = use_labels
lowerCAmelCase_ : List[str] = hidden_size
lowerCAmelCase_ : Union[str, Any] = num_hidden_layers
lowerCAmelCase_ : List[Any] = num_attention_heads
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : List[str] = hidden_act
lowerCAmelCase_ : List[str] = hidden_dropout_prob
lowerCAmelCase_ : Any = attention_probs_dropout_prob
lowerCAmelCase_ : Optional[int] = type_sequence_label_size
lowerCAmelCase_ : Union[str, Any] = initializer_range
lowerCAmelCase_ : str = encoder_stride
lowerCAmelCase_ : List[str] = num_attention_outputs
lowerCAmelCase_ : Any = embed_dim
lowerCAmelCase_ : Tuple = embed_dim + 1
lowerCAmelCase_ : Optional[int] = resolution
lowerCAmelCase_ : List[Any] = depths
lowerCAmelCase_ : Optional[int] = hidden_sizes
lowerCAmelCase_ : Union[str, Any] = dim
lowerCAmelCase_ : Tuple = mlp_expansion_ratio
def A ( self : Optional[int] ):
lowerCAmelCase_ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase_ : Optional[Any] = None
if self.use_labels:
lowerCAmelCase_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase_ : Dict = self.get_config()
return config, pixel_values, labels
def A ( self : Optional[Any] ):
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def A ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple ):
lowerCAmelCase_ : Tuple = TFEfficientFormerModel(config=UpperCAmelCase )
lowerCAmelCase_ : Dict = model(UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : Dict , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] ):
lowerCAmelCase_ : List[Any] = self.type_sequence_label_size
lowerCAmelCase_ : Any = TFEfficientFormerForImageClassification(UpperCAmelCase )
lowerCAmelCase_ : int = model(UpperCAmelCase , labels=UpperCAmelCase , training=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCAmelCase_ : Dict = 1
lowerCAmelCase_ : Optional[int] = TFEfficientFormerForImageClassification(UpperCAmelCase )
lowerCAmelCase_ : Union[str, Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCAmelCase_ : Tuple = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A ( self : List[str] ):
lowerCAmelCase_ : int = self.prepare_config_and_inputs()
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = config_and_inputs
lowerCAmelCase_ : List[str] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __a ( __UpperCamelCase ,__UpperCamelCase ,unittest.TestCase ):
__snake_case : List[str] = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__snake_case : Any = (
{
"""feature-extraction""": TFEfficientFormerModel,
"""image-classification""": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__snake_case : Dict = False
__snake_case : Union[str, Any] = False
__snake_case : Optional[Any] = False
__snake_case : List[str] = False
__snake_case : Union[str, Any] = False
def A ( self : List[Any] ):
lowerCAmelCase_ : str = TFEfficientFormerModelTester(self )
lowerCAmelCase_ : Optional[Any] = ConfigTester(
self , config_class=UpperCAmelCase , has_text_modality=UpperCAmelCase , hidden_size=37 )
def A ( self : str ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def A ( self : Union[str, Any] ):
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def A ( self : Optional[int] ):
pass
def A ( self : Optional[Any] ):
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : int = model_class(UpperCAmelCase )
lowerCAmelCase_ : Optional[Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase_ : List[str] = [*signature.parameters.keys()]
lowerCAmelCase_ : Union[str, Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase )
def A ( self : Tuple ):
def check_hidden_states_output(UpperCAmelCase : Tuple , UpperCAmelCase : List[str] , UpperCAmelCase : int ):
lowerCAmelCase_ : Optional[int] = model_class(UpperCAmelCase )
lowerCAmelCase_ : Dict = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) , training=UpperCAmelCase )
lowerCAmelCase_ : str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCAmelCase_ : Union[str, Any] = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
if hasattr(self.model_tester , """encoder_seq_length""" ):
lowerCAmelCase_ : List[str] = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
lowerCAmelCase_ : List[str] = seq_length * self.model_tester.chunk_length
else:
lowerCAmelCase_ : Any = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
lowerCAmelCase_ : Tuple = outputs.decoder_hidden_states
self.asseretIsInstance(UpperCAmelCase , (list, tuple) )
self.assertEqual(len(UpperCAmelCase ) , UpperCAmelCase )
lowerCAmelCase_ : Optional[int] = getattr(self.model_tester , """seq_length""" , UpperCAmelCase )
lowerCAmelCase_ : Any = getattr(self.model_tester , """decoder_seq_length""" , UpperCAmelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[str] = True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase_ : Tuple = True
check_hidden_states_output(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def A ( self : Tuple , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any]=False ):
lowerCAmelCase_ : List[Any] = super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def A ( self : Union[str, Any] ):
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def A ( self : Optional[int] ):
lowerCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase )
def A ( self : List[str] ):
lowerCAmelCase_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase )
@slow
def A ( self : List[str] ):
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase_ : Optional[int] = TFEfficientFormerModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
def A ( self : Union[str, Any] ):
lowerCAmelCase_ , lowerCAmelCase_ : str = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Union[str, Any] = getattr(self.model_tester , """seq_length""" , UpperCAmelCase )
lowerCAmelCase_ : Tuple = getattr(self.model_tester , """encoder_seq_length""" , UpperCAmelCase )
lowerCAmelCase_ : Optional[Any] = getattr(self.model_tester , """key_length""" , UpperCAmelCase )
lowerCAmelCase_ : int = getattr(self.model_tester , """chunk_length""" , UpperCAmelCase )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
lowerCAmelCase_ : Optional[int] = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Optional[int] = False
lowerCAmelCase_ : Any = True
lowerCAmelCase_ : Union[str, Any] = model_class(UpperCAmelCase )
lowerCAmelCase_ : Dict = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) , training=UpperCAmelCase )
lowerCAmelCase_ : str = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase_ : List[Any] = True
lowerCAmelCase_ : Union[str, Any] = model_class(UpperCAmelCase )
lowerCAmelCase_ : Optional[Any] = model(**self._prepare_for_class(UpperCAmelCase , UpperCAmelCase ) , training=UpperCAmelCase )
lowerCAmelCase_ : Any = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(UpperCAmelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def A ( self : Any ):
# We use a simplified version of this test for EfficientFormer because it requires training=False
# and Keras refuses to let us force that during functional construction
lowerCAmelCase_ , lowerCAmelCase_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
lowerCAmelCase_ : Dict = model_class(UpperCAmelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
lowerCAmelCase_ : Any = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=UpperCAmelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
lowerCAmelCase_ : Optional[Any] = model(UpperCAmelCase )
self.assertTrue(outputs_dict is not None )
def __UpperCamelCase ( ) -> str:
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __a ( unittest.TestCase ):
@cached_property
def A ( self : Optional[Any] ):
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def A ( self : List[Any] ):
lowerCAmelCase_ : Any = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
lowerCAmelCase_ : List[str] = self.default_image_processor
lowerCAmelCase_ : Any = prepare_img()
lowerCAmelCase_ : str = image_processor(images=UpperCAmelCase , return_tensors="""tf""" )
# forward pass
lowerCAmelCase_ : List[str] = model(**UpperCAmelCase , training=UpperCAmelCase )
# verify the logits
lowerCAmelCase_ : List[str] = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase )
lowerCAmelCase_ : Dict = tf.constant([-0.0555, 0.4825, -0.0852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , UpperCAmelCase , atol=1e-4 ) )
@slow
def A ( self : Dict ):
lowerCAmelCase_ : List[str] = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
lowerCAmelCase_ : List[Any] = self.default_image_processor
lowerCAmelCase_ : Tuple = prepare_img()
lowerCAmelCase_ : Dict = image_processor(images=UpperCAmelCase , return_tensors="""tf""" )
# forward pass
lowerCAmelCase_ : str = model(**UpperCAmelCase , training=UpperCAmelCase )
# verify the logits
lowerCAmelCase_ : Any = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase )
lowerCAmelCase_ : List[str] = tf.constant([-0.1312, 0.4353, -1.0499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , UpperCAmelCase , atol=1e-4 ) )
| 600 | 0 |
def _lowerCamelCase ( a_ : float):
return 10 - x * x
def _lowerCamelCase ( a_ : float , a_ : float):
# Bolzano theory in order to find if there is a root between a and b
if equation(a_) * equation(a_) >= 0:
raise ValueError('''Wrong space!''')
lowerCamelCase :List[str] = a
while (b - a) >= 0.01:
# Find middle point
lowerCamelCase :Union[str, Any] = (a + b) / 2
# Check if middle point is root
if equation(a_) == 0.0:
break
# Decide the side to repeat the steps
if equation(a_) * equation(a_) < 0:
lowerCamelCase :str = c
else:
lowerCamelCase :Tuple = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 49 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
lowerCamelCase :Dict = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
lowerCamelCase :Dict = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
lowerCamelCase :Any = numpy.random.rand(3 , 1 )
# Real output values provided.
lowerCamelCase :Union[str, Any] = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
lowerCamelCase :List[str] = numpy.zeros(output_array.shape )
def snake_case ( self : Optional[int] ):
lowerCamelCase :Any = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
lowerCamelCase :Any = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
lowerCamelCase :Dict = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def snake_case ( self : Any ):
lowerCamelCase :Union[str, Any] = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
lowerCamelCase :Dict = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
lowerCamelCase :int = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def snake_case ( self : Dict , __snake_case : numpy.ndarray , __snake_case : int , __snake_case : bool ):
for iteration in range(1 , iterations + 1 ):
lowerCamelCase :Union[str, Any] = self.feedforward()
self.back_propagation()
if give_loss:
lowerCamelCase :Tuple = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F"Iteration {iteration} Loss: {loss}" )
def snake_case ( self : Optional[int] , __snake_case : numpy.ndarray ):
lowerCamelCase :int = input_arr
lowerCamelCase :Union[str, Any] = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
lowerCamelCase :Optional[Any] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
lowerCamelCase :Optional[int] = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def _lowerCamelCase ( a_ : numpy.ndarray):
return 1 / (1 + numpy.exp(-value))
def _lowerCamelCase ( a_ : numpy.ndarray):
return (value) * (1 - (value))
def _lowerCamelCase ( ):
lowerCamelCase :Optional[Any] = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
lowerCamelCase :int = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa)
# Calling neural network class.
lowerCamelCase :List[Any] = TwoHiddenLayerNeuralNetwork(
input_array=a_ , output_array=a_)
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=a_ , iterations=10 , give_loss=a_)
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa))
if __name__ == "__main__":
example()
| 49 | 1 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCamelCase : Union[str, Any] = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="""relu""")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation="""relu"""))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation="""relu"""))
classifier.add(layers.Dense(units=1, activation="""sigmoid"""))
# Compiling the CNN
classifier.compile(
optimizer="""adam""", loss="""binary_crossentropy""", metrics=["""accuracy"""]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
__UpperCamelCase : Optional[Any] = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
__UpperCamelCase : Union[str, Any] = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
__UpperCamelCase : Any = train_datagen.flow_from_directory(
"""dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
)
__UpperCamelCase : str = test_datagen.flow_from_directory(
"""dataset/test_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save("""cnn.h5""")
# Part 3 - Making new predictions
__UpperCamelCase : Optional[Any] = tf.keras.preprocessing.image.load_img(
"""dataset/single_prediction/image.png""", target_size=(64, 64)
)
__UpperCamelCase : List[Any] = tf.keras.preprocessing.image.img_to_array(test_image)
__UpperCamelCase : Dict = np.expand_dims(test_image, axis=0)
__UpperCamelCase : Optional[Any] = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
__UpperCamelCase : Optional[int] = """Normal"""
if result[0][0] == 1:
__UpperCamelCase : Optional[int] = """Abnormality detected""" | 448 |
'''simple docstring'''
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _lowerCAmelCase ):
__a =["input_ids", "attention_mask"]
def __init__( self , lowerCamelCase="</s>" , lowerCamelCase="<unk>" , lowerCamelCase="<pad>" , lowerCamelCase=125 , lowerCamelCase=None , **lowerCamelCase , ) ->None:
'''simple docstring'''
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__a = [F"""<extra_id_{i}>""" for i in range(lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__a = len(set(filter(lambda lowerCamelCase : bool('extra_id' in str(lowerCamelCase ) ) , lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
' extra_ids tokens' )
__a = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else pad_token
__a = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else eos_token
__a = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else unk_token
super().__init__(
eos_token=lowerCamelCase , unk_token=lowerCamelCase , pad_token=lowerCamelCase , extra_ids=lowerCamelCase , additional_special_tokens=lowerCamelCase , **lowerCamelCase , )
__a = extra_ids
__a = 2**8 # utf is 8 bits
# define special tokens dict
__a = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
__a = len(self.special_tokens_encoder )
__a = len(lowerCamelCase )
for i, token in enumerate(lowerCamelCase ):
__a = self.vocab_size + i - n
__a = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def __UpperCamelCase ( self ) ->Any:
'''simple docstring'''
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def __UpperCamelCase ( self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = False ) ->List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase , token_ids_a=lowerCamelCase , already_has_special_tokens=lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(lowerCamelCase )) + [1]
return ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
def __UpperCamelCase ( self , lowerCamelCase ) ->List[int]:
'''simple docstring'''
if len(lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
' eos tokens being added.' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def __UpperCamelCase ( self , lowerCamelCase , lowerCamelCase = None ) ->List[int]:
'''simple docstring'''
__a = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def __UpperCamelCase ( self , lowerCamelCase , lowerCamelCase = None ) ->List[int]:
'''simple docstring'''
__a = self._add_eos_if_not_present(lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__a = self._add_eos_if_not_present(lowerCamelCase )
return token_ids_a + token_ids_a
def __UpperCamelCase ( self , lowerCamelCase ) ->List[str]:
'''simple docstring'''
__a = [chr(lowerCamelCase ) for i in text.encode('utf-8' )]
return tokens
def __UpperCamelCase ( self , lowerCamelCase ) ->Optional[Any]:
'''simple docstring'''
if token in self.special_tokens_encoder:
__a = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
__a = self.added_tokens_encoder[token]
elif len(lowerCamelCase ) != 1:
__a = self.unk_token_id
else:
__a = ord(lowerCamelCase ) + self._num_special_tokens
return token_id
def __UpperCamelCase ( self , lowerCamelCase ) ->Tuple:
'''simple docstring'''
if index in self.special_tokens_decoder:
__a = self.special_tokens_decoder[index]
else:
__a = chr(index - self._num_special_tokens )
return token
def __UpperCamelCase ( self , lowerCamelCase ) ->Optional[Any]:
'''simple docstring'''
__a = B''
for token in tokens:
if token in self.special_tokens_decoder:
__a = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.added_tokens_decoder:
__a = self.special_tokens_decoder[token].encode('utf-8' )
elif token in self.special_tokens_encoder:
__a = token.encode('utf-8' )
elif token in self.added_tokens_encoder:
__a = token.encode('utf-8' )
else:
__a = bytes([ord(lowerCamelCase )] )
bstring += tok_string
__a = bstring.decode('utf-8' , errors='ignore' )
return string
def __UpperCamelCase ( self , lowerCamelCase , lowerCamelCase = None ) ->Tuple[str]:
'''simple docstring'''
return () | 448 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
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 TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class _lowerCamelCase :
_snake_case = MBartConfig
_snake_case = {}
_snake_case = "gelu"
def __init__( self : Union[str, Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : str=1_3 , lowerCamelCase_ : Dict=7 , lowerCamelCase_ : int=True , lowerCamelCase_ : str=False , lowerCamelCase_ : Dict=9_9 , lowerCamelCase_ : Dict=3_2 , lowerCamelCase_ : str=2 , lowerCamelCase_ : Optional[Any]=4 , lowerCamelCase_ : Dict=3_7 , lowerCamelCase_ : str=0.1 , lowerCamelCase_ : List[str]=0.1 , lowerCamelCase_ : str=2_0 , lowerCamelCase_ : Optional[int]=2 , lowerCamelCase_ : Optional[Any]=1 , lowerCamelCase_ : Union[str, Any]=0 , ):
"""simple docstring"""
_lowercase : Union[str, Any] = parent
_lowercase : List[str] = batch_size
_lowercase : Optional[Any] = seq_length
_lowercase : Dict = is_training
_lowercase : Union[str, Any] = use_labels
_lowercase : Tuple = vocab_size
_lowercase : Any = hidden_size
_lowercase : Union[str, Any] = num_hidden_layers
_lowercase : Any = num_attention_heads
_lowercase : Optional[Any] = intermediate_size
_lowercase : Union[str, Any] = hidden_dropout_prob
_lowercase : Dict = attention_probs_dropout_prob
_lowercase : Any = max_position_embeddings
_lowercase : Union[str, Any] = eos_token_id
_lowercase : Any = pad_token_id
_lowercase : Any = bos_token_id
def __UpperCAmelCase ( self : Optional[int] ):
"""simple docstring"""
_lowercase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
_lowercase : List[str] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
_lowercase : Any = tf.concat([input_ids, eos_tensor] , axis=1 )
_lowercase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowercase : Tuple = self.config_cls(
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_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
_lowercase : Tuple = prepare_mbart_inputs_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return config, inputs_dict
def __UpperCAmelCase ( self : Tuple , lowerCamelCase_ : int , lowerCamelCase_ : List[Any] ):
"""simple docstring"""
_lowercase : Optional[Any] = TFMBartModel(config=_lowerCamelCase ).get_decoder()
_lowercase : Dict = inputs_dict['input_ids']
_lowercase : Optional[int] = input_ids[:1, :]
_lowercase : List[Any] = inputs_dict['attention_mask'][:1, :]
_lowercase : List[Any] = inputs_dict['head_mask']
_lowercase : Any = 1
# first forward pass
_lowercase : Dict = model(_lowerCamelCase , attention_mask=_lowerCamelCase , head_mask=_lowerCamelCase , use_cache=_lowerCamelCase )
_lowercase , _lowercase : Dict = outputs.to_tuple()
_lowercase : int = past_key_values[1]
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,):
"""simple docstring"""
if attention_mask is None:
_lowercase : List[Any] = tf.cast(tf.math.not_equal(_lowerCAmelCase ,config.pad_token_id ) ,tf.inta )
if decoder_attention_mask is None:
_lowercase : List[str] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape ,dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,config.pad_token_id ) ,tf.inta ),
] ,axis=-1 ,)
if head_mask is None:
_lowercase : Union[str, Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_lowercase : Optional[int] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_lowercase : List[Any] = tf.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": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _lowerCamelCase (__UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
_snake_case = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
_snake_case = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
_snake_case = (
{
"conversational": TFMBartForConditionalGeneration,
"feature-extraction": TFMBartModel,
"summarization": TFMBartForConditionalGeneration,
"text2text-generation": TFMBartForConditionalGeneration,
"translation": TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
_snake_case = True
_snake_case = False
_snake_case = False
def __UpperCAmelCase ( self : int , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : str ):
"""simple docstring"""
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def __UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
_lowercase : str = TFMBartModelTester(self )
_lowercase : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase )
def __UpperCAmelCase ( self : Tuple ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self : Optional[Any] ):
"""simple docstring"""
_lowercase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class _lowerCamelCase (unittest.TestCase ):
_snake_case = [
" UN Chief Says There Is No Military Solution in Syria",
]
_snake_case = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
]
_snake_case = "facebook/mbart-large-en-ro"
@cached_property
def __UpperCAmelCase ( self : List[Any] ):
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __UpperCAmelCase ( self : str ):
"""simple docstring"""
_lowercase : int = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __UpperCAmelCase ( self : Dict , **lowerCamelCase_ : Optional[Any] ):
"""simple docstring"""
_lowercase : Optional[int] = self.translate_src_text(**_lowerCamelCase )
self.assertListEqual(self.expected_text , _lowerCamelCase )
def __UpperCAmelCase ( self : Union[str, Any] , **lowerCamelCase_ : Optional[Any] ):
"""simple docstring"""
_lowercase : Tuple = self.tokenizer(self.src_text , **_lowerCamelCase , return_tensors='tf' )
_lowercase : List[Any] = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
_lowercase : List[Any] = self.tokenizer.batch_decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
return generated_words
@slow
def __UpperCAmelCase ( self : List[str] ):
"""simple docstring"""
self._assert_generated_batch_equal_expected()
| 701 | """simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natural numbers' )
_lowercase : Optional[Any] = precision
_lowercase : Dict = ceil(precision / 14 )
_lowercase : int = 426_880 * Decimal(10_005 ).sqrt()
_lowercase : Optional[Any] = 1
_lowercase : Union[str, Any] = 13_591_409
_lowercase : Optional[int] = Decimal(__UpperCAmelCase )
for k in range(1 ,__UpperCAmelCase ):
_lowercase : List[str] = factorial(6 * k ) // (factorial(3 * k ) * factorial(__UpperCAmelCase ) ** 3)
linear_term += 545_140_134
exponential_term *= -262_537_412_640_768_000
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = 50
print(f"""The first {n} digits of pi is: {pi(n)}""")
| 283 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
_lowercase = [
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
{'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
{'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
{'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
{'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def _snake_case ( snake_case__ : str=True ):
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_lowercase ) )
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Optional[Any] = None
_lowerCamelCase: str = None
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : int ,A_ : Tuple ) -> Any:
with TemporaryDirectory() as tmp_dir:
A = dataset_module_factory(A_ ,cache_dir=A_ )
A = import_main_class(dataset_module.module_path ,dataset=A_ )
A = builder_cls(
cache_dir=A_ ,config_name=A_ ,hash=dataset_module.hash ,)
A = '/'.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A_ ).replace(os.sep ,'/' ),
config.DATASET_INFO_FILENAME,
] )
A = cached_path(A_ ,cache_dir=A_ )
self.assertTrue(os.path.exists(A_ ) )
@pytest.mark.integration
def _snake_case ( snake_case__ : Optional[int] ):
A = tmp_path_factory.mktemp('test_hf_gcp' ) / 'test_wikipedia_simple'
A = dataset_module_factory('wikipedia' , cache_dir=snake_case__ )
A = import_main_class(dataset_module.module_path )
A = builder_cls(
cache_dir=snake_case__ , config_name='20220301.frr' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
A = None
builder_instance.download_and_prepare()
A = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def _snake_case ( snake_case__ : List[Any] ):
A = dataset_module_factory('wikipedia' , cache_dir=snake_case__ )
A = import_main_class(dataset_module.module_path , dataset=snake_case__ )
A = builder_cls(
cache_dir=snake_case__ , config_name='20220301.frr' , hash=dataset_module.hash , )
A = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(snake_case__ , snake_case__ )
assert "train" in ds
assert isinstance(ds['train'] , snake_case__ )
assert next(iter(ds['train'] ) ) | 91 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def _a ( UpperCamelCase_ : str , UpperCamelCase_ : Dict , UpperCamelCase_ : Union[str, Any]=[] ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ = size[0] - overlap_pixels * 2
lowerCAmelCase__ = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
lowerCAmelCase__ = np.ones((size_y, size_x) , dtype=np.uinta ) * 255
lowerCAmelCase__ = np.pad(UpperCamelCase_ , mode="linear_ramp" , pad_width=UpperCamelCase_ , end_values=0 )
if "l" in remove_borders:
lowerCAmelCase__ = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
lowerCAmelCase__ = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
lowerCAmelCase__ = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
lowerCAmelCase__ = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def _a ( UpperCamelCase_ : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
return max(UpperCamelCase_ , min(UpperCamelCase_ , UpperCamelCase_ ) )
def _a ( UpperCamelCase_ : [int] , UpperCamelCase_ : [int] , UpperCamelCase_ : [int] ) -> Optional[int]:
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def _a ( UpperCamelCase_ : [int] , UpperCamelCase_ : int , UpperCamelCase_ : [int] ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ = list(UpperCamelCase_ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
lowerCAmelCase__ = clamp_rect(UpperCamelCase_ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def _a ( UpperCamelCase_ : Dict , UpperCamelCase_ : List[str] , UpperCamelCase_ : int , UpperCamelCase_ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ = Image.new("RGB" , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(UpperCamelCase_ , (original_slice, 0) )
return result
def _a ( UpperCamelCase_ : int , UpperCamelCase_ : str ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
lowerCAmelCase__ = tile.crop(UpperCamelCase_ )
return tile
def _a ( UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any] ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = n % d
return n - divisor
class lowercase__ ( _UpperCAmelCase ):
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 350 , )-> Tuple:
'''simple docstring'''
super().__init__(
vae=__UpperCAmelCase , text_encoder=__UpperCAmelCase , tokenizer=__UpperCAmelCase , unet=__UpperCAmelCase , low_res_scheduler=__UpperCAmelCase , scheduler=__UpperCAmelCase , max_noise_level=__UpperCAmelCase , )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )-> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
lowerCAmelCase__ = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
lowerCAmelCase__ = add_overlap_rect(__UpperCAmelCase , __UpperCAmelCase , image.size )
lowerCAmelCase__ = image.crop(__UpperCAmelCase )
lowerCAmelCase__ = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
lowerCAmelCase__ = translated_slice_x - (original_image_slice / 2)
lowerCAmelCase__ = max(0 , __UpperCAmelCase )
lowerCAmelCase__ = squeeze_tile(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = to_input.size
lowerCAmelCase__ = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
lowerCAmelCase__ = super(__UpperCAmelCase , self ).__call__(image=__UpperCAmelCase , **__UpperCAmelCase ).images[0]
lowerCAmelCase__ = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
lowerCAmelCase__ = unsqueeze_tile(__UpperCAmelCase , __UpperCAmelCase )
lowerCAmelCase__ = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
lowerCAmelCase__ = []
if x == 0:
remove_borders.append("l" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("r" )
if y == 0:
remove_borders.append("t" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("b" )
lowerCAmelCase__ = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=__UpperCAmelCase ) , mode="L" , )
final_image.paste(
__UpperCAmelCase , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , __UpperCAmelCase )
@torch.no_grad()
def __call__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 75 , __UpperCAmelCase = 9.0 , __UpperCAmelCase = 50 , __UpperCAmelCase = None , __UpperCAmelCase = 1 , __UpperCAmelCase = 0.0 , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = 1 , __UpperCAmelCase = 128 , __UpperCAmelCase = 32 , __UpperCAmelCase = 32 , )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = Image.new("RGB" , (image.size[0] * 4, image.size[1] * 4) )
lowerCAmelCase__ = math.ceil(image.size[0] / tile_size )
lowerCAmelCase__ = math.ceil(image.size[1] / tile_size )
lowerCAmelCase__ = tcx * tcy
lowerCAmelCase__ = 0
for y in range(__UpperCAmelCase ):
for x in range(__UpperCAmelCase ):
self._process_tile(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , prompt=__UpperCAmelCase , num_inference_steps=__UpperCAmelCase , guidance_scale=__UpperCAmelCase , noise_level=__UpperCAmelCase , negative_prompt=__UpperCAmelCase , num_images_per_prompt=__UpperCAmelCase , eta=__UpperCAmelCase , generator=__UpperCAmelCase , latents=__UpperCAmelCase , )
current_count += 1
if callback is not None:
callback({"progress": current_count / total_tile_count, "image": final_image} )
return final_image
def _a ( ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = "stabilityai/stable-diffusion-x4-upscaler"
lowerCAmelCase__ = StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase_ , revision="fp16" , torch_dtype=torch.floataa )
lowerCAmelCase__ = pipe.to("cuda" )
lowerCAmelCase__ = Image.open("../../docs/source/imgs/diffusers_library.jpg" )
def callback(UpperCamelCase_ : List[Any] ):
print(F"progress: {obj['progress']:.4f}" )
obj["image"].save("diffusers_library_progress.jpg" )
lowerCAmelCase__ = pipe(image=UpperCamelCase_ , prompt="Black font, white background, vector" , noise_level=40 , callback=UpperCamelCase_ )
final_image.save("diffusers_library.jpg" )
if __name__ == "__main__":
main()
| 339 | 0 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : str = logging.get_logger(__name__)
A__ : str = {
"""nvidia/segformer-b0-finetuned-ade-512-512""": (
"""https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'segformer'
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=[2, 2, 2, 2] , __UpperCamelCase=[8, 4, 2, 1] , __UpperCamelCase=[32, 64, 1_60, 2_56] , __UpperCamelCase=[7, 3, 3, 3] , __UpperCamelCase=[4, 2, 2, 2] , __UpperCamelCase=[1, 2, 5, 8] , __UpperCamelCase=[4, 4, 4, 4] , __UpperCamelCase="gelu" , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.02 , __UpperCamelCase=0.1 , __UpperCamelCase=1E-6 , __UpperCamelCase=2_56 , __UpperCamelCase=2_55 , **__UpperCamelCase , )-> Optional[int]:
super().__init__(**__UpperCamelCase )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
" removed, as the behaviour will default to that of reshape_last_stage = True." , __UpperCamelCase , )
UpperCAmelCase__ : Optional[int] = num_channels
UpperCAmelCase__ : Union[str, Any] = num_encoder_blocks
UpperCAmelCase__ : Tuple = depths
UpperCAmelCase__ : List[str] = sr_ratios
UpperCAmelCase__ : Union[str, Any] = hidden_sizes
UpperCAmelCase__ : Any = patch_sizes
UpperCAmelCase__ : List[Any] = strides
UpperCAmelCase__ : Optional[int] = mlp_ratios
UpperCAmelCase__ : Tuple = num_attention_heads
UpperCAmelCase__ : Any = hidden_act
UpperCAmelCase__ : Optional[int] = hidden_dropout_prob
UpperCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase__ : Any = classifier_dropout_prob
UpperCAmelCase__ : Dict = initializer_range
UpperCAmelCase__ : Tuple = drop_path_rate
UpperCAmelCase__ : Optional[int] = layer_norm_eps
UpperCAmelCase__ : List[Any] = decoder_hidden_size
UpperCAmelCase__ : int = kwargs.get("reshape_last_stage" , __UpperCamelCase )
UpperCAmelCase__ : List[Any] = semantic_loss_ignore_index
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-4
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 660 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A__ : Tuple = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : nn.ModuleList , lowerCAmelCase : nn.ModuleList , lowerCAmelCase : List[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F"{len(lowerCAmelCase )} != {len(lowerCAmelCase )}"
dest_layers.load_state_dict(layers_to_copy.state_dict() )
A__ : List[Any] = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A__ : List[Any] = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def a__ ( lowerCAmelCase : Dict , lowerCAmelCase : Dict ):
'''simple docstring'''
try:
UpperCAmelCase__ : Tuple = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
F" {n_student}" )
return list(range(lowerCAmelCase ) )
def a__ ( lowerCAmelCase : int , lowerCAmelCase : Tuple ):
'''simple docstring'''
if n_student > n_teacher:
raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}" )
elif n_teacher == n_student:
return list(range(lowerCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def a__ ( lowerCAmelCase : Union[str, PreTrainedModel] , lowerCAmelCase : Union[str, Path] = "student" , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : Union[int, None] = None , lowerCAmelCase : List[str]=False , lowerCAmelCase : List[str]=None , lowerCAmelCase : List[str]=None , **lowerCAmelCase : List[str] , ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
assert (e is not None) or (d is not None), _msg
if isinstance(lowerCAmelCase , lowerCAmelCase ):
AutoTokenizer.from_pretrained(lowerCAmelCase ).save_pretrained(lowerCAmelCase ) # purely for convenience
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
else:
assert isinstance(lowerCAmelCase , lowerCAmelCase ), F"teacher must be a model or string got type {type(lowerCAmelCase )}"
UpperCAmelCase__ : int = teacher.config.to_diff_dict()
try:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
UpperCAmelCase__ : Tuple = teacher_e
if d is None:
UpperCAmelCase__ : str = teacher_d
init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
except AttributeError: # T5
if hasattr(teacher.config , "num_encoder_layers" ):
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
UpperCAmelCase__ : Optional[Any] = teacher_e
if d is None:
UpperCAmelCase__ : Optional[Any] = teacher_d
if hasattr(teacher.config , "num_encoder_layers" ):
init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
else:
init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(lowerCAmelCase )
# Copy weights
UpperCAmelCase__ : Tuple = teacher.config_class(**lowerCAmelCase )
UpperCAmelCase__ : List[str] = AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
UpperCAmelCase__ : Optional[int] = student.load_state_dict(teacher.state_dict() , strict=lowerCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
UpperCAmelCase__ , UpperCAmelCase__ : int = list(range(lowerCAmelCase ) ), list(range(lowerCAmelCase ) )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
F" {save_path}" )
student.save_pretrained(lowerCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
if d_layers_to_copy is None:
UpperCAmelCase__ : List[int] = pick_layers_to_copy(lowerCAmelCase , lowerCAmelCase )
try:
if hasattr(
lowerCAmelCase , "prophetnet" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCAmelCase )
else:
copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCAmelCase )
copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block , student.encoder.block , lowerCAmelCase )
copy_layers(teacher.decoder.block , student.decoder.block , lowerCAmelCase )
logger.info(
F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}" )
UpperCAmelCase__ : int = {
"teacher_type": teacher.config.model_type,
"copied_encoder_layers": e_layers_to_copy,
"copied_decoder_layers": d_layers_to_copy,
}
student.save_pretrained(lowerCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 660 | 1 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __lowercase ( unittest.TestCase):
"""simple docstring"""
def __UpperCamelCase (self ):
snake_case_ : Optional[int] = get_activation("""swish""" )
self.assertIsInstance(lowercase__ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def __UpperCamelCase (self ):
snake_case_ : Optional[int] = get_activation("""silu""" )
self.assertIsInstance(lowercase__ , nn.SiLU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def __UpperCamelCase (self ):
snake_case_ : Tuple = get_activation("""mish""" )
self.assertIsInstance(lowercase__ , nn.Mish )
self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
def __UpperCamelCase (self ):
snake_case_ : Tuple = get_activation("""gelu""" )
self.assertIsInstance(lowercase__ , nn.GELU )
self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 )
self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 )
self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
| 480 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
_A : Union[str, Any] = ["""image_processor""", """tokenizer"""]
_A : Tuple = """LayoutLMv2ImageProcessor"""
_A : Tuple = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__(self , lowercase__=None , lowercase__=None , **lowercase__ ):
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowercase__ , )
snake_case_ : int = kwargs.pop("""feature_extractor""" )
snake_case_ : 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__(lowercase__ , lowercase__ )
def __call__(self , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = True , lowercase__ = False , lowercase__ = None , lowercase__ = None , lowercase__ = 0 , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = False , lowercase__ = False , lowercase__ = False , lowercase__ = False , lowercase__ = True , lowercase__ = None , **lowercase__ , ):
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes """
"""if you initialized the image processor with apply_ocr set to True.""" )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""" )
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""" )
# first, apply the image processor
snake_case_ : Tuple = self.image_processor(images=lowercase__ , return_tensors=lowercase__ )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowercase__ , lowercase__ ):
snake_case_ : List[Any] = [text] # add batch dimension (as the image processor always adds a batch dimension)
snake_case_ : Optional[int] = features["""words"""]
snake_case_ : List[Any] = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=lowercase__ , add_special_tokens=lowercase__ , padding=lowercase__ , truncation=lowercase__ , max_length=lowercase__ , stride=lowercase__ , pad_to_multiple_of=lowercase__ , return_token_type_ids=lowercase__ , return_attention_mask=lowercase__ , return_overflowing_tokens=lowercase__ , return_special_tokens_mask=lowercase__ , return_offsets_mapping=lowercase__ , return_length=lowercase__ , verbose=lowercase__ , return_tensors=lowercase__ , **lowercase__ , )
# add pixel values
snake_case_ : Any = features.pop("""pixel_values""" )
if return_overflowing_tokens is True:
snake_case_ : List[str] = self.get_overflowing_images(lowercase__ , encoded_inputs["""overflow_to_sample_mapping"""] )
snake_case_ : str = images
return encoded_inputs
def __UpperCamelCase (self , lowercase__ , lowercase__ ):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
snake_case_ : Dict = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
f' {len(lowercase__ )} and {len(lowercase__ )}' )
return images_with_overflow
def __UpperCamelCase (self , *lowercase__ , **lowercase__ ):
return self.tokenizer.batch_decode(*lowercase__ , **lowercase__ )
def __UpperCamelCase (self , *lowercase__ , **lowercase__ ):
return self.tokenizer.decode(*lowercase__ , **lowercase__ )
@property
def __UpperCamelCase (self ):
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def __UpperCamelCase (self ):
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowercase__ , )
return self.image_processor_class
@property
def __UpperCamelCase (self ):
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowercase__ , )
return self.image_processor
| 480 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowerCamelCase__ ( __snake_case ):
def _UpperCamelCase ( self , lowerCAmelCase__ ) -> float:
"""simple docstring"""
return 0.0
def _lowerCAmelCase ( __a , __a ) -> tuple[int | float, int | float]:
'''simple docstring'''
_UpperCamelCase :Any =min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_UpperCamelCase :Optional[Any] =max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def _lowerCAmelCase ( __a , __a ) -> None:
'''simple docstring'''
_UpperCamelCase :int =5_12
_UpperCamelCase :List[str] =[1] + [0] * (size - 1)
_UpperCamelCase :List[str] =[filter_type.process(__a ) for item in inputs]
_UpperCamelCase :List[Any] =[0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCamelCase :List[str] =np.abs(np.fft.fft(__a ) )
_UpperCamelCase :List[str] =20 * np.logaa(__a )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("""Frequency (Hz)""" )
plt.xscale("""log""" )
# Display within reasonable bounds
_UpperCamelCase :Any =get_bounds(__a , __a )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("""Gain (dB)""" )
plt.plot(__a )
plt.show()
def _lowerCAmelCase ( __a , __a ) -> None:
'''simple docstring'''
_UpperCamelCase :Dict =5_12
_UpperCamelCase :List[Any] =[1] + [0] * (size - 1)
_UpperCamelCase :str =[filter_type.process(__a ) for item in inputs]
_UpperCamelCase :Optional[int] =[0] * (samplerate - size) # zero-padding
outputs += filler
_UpperCamelCase :Optional[Any] =np.angle(np.fft.fft(__a ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("""Frequency (Hz)""" )
plt.xscale("""log""" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("""Phase shift (Radians)""" )
plt.plot(np.unwrap(__a , -2 * pi ) )
plt.show() | 715 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class lowerCamelCase__ ( __snake_case ):
__UpperCAmelCase = """speech_to_text_2"""
__UpperCAmelCase = ["""past_key_values"""]
__UpperCAmelCase = {"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , lowerCAmelCase__=10_000 , lowerCAmelCase__=6 , lowerCAmelCase__=2_048 , lowerCAmelCase__=4 , lowerCAmelCase__=0.0 , lowerCAmelCase__=True , lowerCAmelCase__="relu" , lowerCAmelCase__=256 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=2 , lowerCAmelCase__=True , lowerCAmelCase__=1 , lowerCAmelCase__=0 , lowerCAmelCase__=2 , lowerCAmelCase__=1_024 , **lowerCAmelCase__ , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :Dict =vocab_size
_UpperCamelCase :Union[str, Any] =d_model
_UpperCamelCase :Tuple =decoder_ffn_dim
_UpperCamelCase :Union[str, Any] =decoder_layers
_UpperCamelCase :Optional[Any] =decoder_attention_heads
_UpperCamelCase :Dict =dropout
_UpperCamelCase :List[Any] =attention_dropout
_UpperCamelCase :Union[str, Any] =activation_dropout
_UpperCamelCase :str =activation_function
_UpperCamelCase :str =init_std
_UpperCamelCase :Any =decoder_layerdrop
_UpperCamelCase :Optional[int] =use_cache
_UpperCamelCase :Dict =decoder_layers
_UpperCamelCase :List[Any] =scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCamelCase :str =max_target_positions
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , ) | 512 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
_lowerCAmelCase = str(lowerCAmelCase )
_lowerCAmelCase = ''.join(sorted(lowerCAmelCase ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != str_n
def UpperCamelCase__ ( lowerCAmelCase = 99 ):
"""simple docstring"""
if not 0 < percent < 1_00:
raise ValueError("""solution() only accepts values from 0 to 100""" )
_lowerCAmelCase = 0
_lowerCAmelCase = 1
while True:
if check_bouncy(lowerCAmelCase ):
bouncy_num += 1
if (bouncy_num / num) * 1_00 >= percent:
return num
num += 1
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(99)}""")
| 207 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase_ : Optional[Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase_ : Union[str, Any] = {
"vocab_file": {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
},
"tokenizer_file": {
"unc-nlp/lxmert-base-uncased": (
"https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase_ : str = {
"unc-nlp/lxmert-base-uncased": 512,
}
UpperCAmelCase_ : Optional[int] = {
"unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : Tuple = VOCAB_FILES_NAMES
__lowerCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Optional[Any] = PRETRAINED_INIT_CONFIGURATION
__lowerCAmelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : List[Any] = LxmertTokenizer
def __init__( self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=True , lowerCamelCase_="[UNK]" , lowerCamelCase_="[SEP]" , lowerCamelCase_="[PAD]" , lowerCamelCase_="[CLS]" , lowerCamelCase_="[MASK]" , lowerCamelCase_=True , lowerCamelCase_=None , **lowerCamelCase_ , ) -> Optional[int]:
super().__init__(
lowerCamelCase_ , tokenizer_file=lowerCamelCase_ , do_lower_case=lowerCamelCase_ , unk_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , tokenize_chinese_chars=lowerCamelCase_ , strip_accents=lowerCamelCase_ , **lowerCamelCase_ , )
_a : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , lowerCamelCase_ ) != do_lower_case
or normalizer_state.get('strip_accents' , lowerCamelCase_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , lowerCamelCase_ ) != tokenize_chinese_chars
):
_a : str = getattr(lowerCamelCase_ , normalizer_state.pop('type' ) )
_a : Tuple = do_lower_case
_a : str = strip_accents
_a : Optional[Any] = tokenize_chinese_chars
_a : Dict = normalizer_class(**lowerCamelCase_ )
_a : Optional[int] = do_lower_case
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_=None ) -> Optional[Any]:
_a : List[str] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> List[int]:
_a : Union[str, Any] = [self.sep_token_id]
_a : Optional[int] = [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 __UpperCamelCase ( self , lowerCamelCase_ , lowerCamelCase_ = None ) -> Tuple[str]:
_a : Optional[Any] = self._tokenizer.model.save(lowerCamelCase_ , name=lowerCamelCase_ )
return tuple(lowerCamelCase_ )
| 120 | 0 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class snake_case__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase):
a_ = [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]
@register_to_config
def __init__( self : List[Any] , _A : int , _A : int , _A : Optional[int] = None , _A : int = 5_02_57 , _A : int = 10_24 , _A : int = 7_68 , _A : int = 12 , _A : int = 12 , _A : Optional[int] = None , _A : str = "gelu_new" , _A : float = 0.1 , _A : float = 0.1 , _A : float = 0.1 , _A : float = 1e-5 , _A : float = 0.02 , _A : bool = True , _A : bool = True , _A : bool = False , _A : bool = False , ) -> List[Any]:
super().__init__()
UpperCAmelCase_ : Dict = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
F"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
F" `n_embd`: {n_embd} are not equal." )
UpperCAmelCase_ : Optional[int] = prefix_inner_dim
UpperCAmelCase_ : List[Any] = prefix_hidden_dim
UpperCAmelCase_ : Dict = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
UpperCAmelCase_ : Union[str, Any] = (
nn.Linear(self.prefix_hidden_dim , _A ) if self.prefix_hidden_dim is not None else nn.Identity()
)
UpperCAmelCase_ : Dict = GPTaConfig(
vocab_size=_A , n_positions=_A , n_embd=_A , n_layer=_A , n_head=_A , n_inner=_A , activation_function=_A , resid_pdrop=_A , embd_pdrop=_A , attn_pdrop=_A , layer_norm_epsilon=_A , initializer_range=_A , scale_attn_weights=_A , use_cache=_A , scale_attn_by_inverse_layer_idx=_A , reorder_and_upcast_attn=_A , )
UpperCAmelCase_ : Optional[int] = GPTaLMHeadModel(_A )
def A ( self : List[Any] , _A : torch.Tensor , _A : torch.Tensor , _A : Optional[torch.Tensor] = None , _A : Optional[torch.Tensor] = None , ) -> List[str]:
UpperCAmelCase_ : str = self.transformer.transformer.wte(_A )
UpperCAmelCase_ : Dict = self.encode_prefix(_A )
UpperCAmelCase_ : Union[str, Any] = self.decode_prefix(_A )
UpperCAmelCase_ : Dict = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
UpperCAmelCase_ : Dict = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
UpperCAmelCase_ : Dict = torch.cat((dummy_token, input_ids) , dim=1 )
UpperCAmelCase_ : List[str] = self.transformer(inputs_embeds=_A , labels=_A , attention_mask=_A )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def A ( self : Tuple , _A : int , _A : torch.device ) -> torch.Tensor:
return torch.zeros(_A , self.prefix_length , dtype=torch.intaa , device=_A )
def A ( self : List[Any] , _A : int ) -> str:
return self.encode_prefix(_A )
@torch.no_grad()
def A ( self : str , _A : str , _A : Any , _A : List[Any] ) -> Any:
UpperCAmelCase_ : int = torch.split(_A , 1 , dim=0 )
UpperCAmelCase_ : Dict = []
UpperCAmelCase_ : Dict = []
for feature in features:
UpperCAmelCase_ : Tuple = self.decode_prefix(feature.to(_A ) ) # back to the clip feature
# Only support beam search for now
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = self.generate_beam(
input_embeds=_A , device=_A , eos_token_id=_A )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
UpperCAmelCase_ : Tuple = torch.stack(_A )
UpperCAmelCase_ : Any = torch.stack(_A )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def A ( self : List[Any] , _A : List[str]=None , _A : Any=None , _A : int=None , _A : int = 5 , _A : int = 67 , _A : float = 1.0 , _A : Optional[int] = None , ) -> str:
UpperCAmelCase_ : Dict = eos_token_id
UpperCAmelCase_ : Any = None
UpperCAmelCase_ : Dict = None
UpperCAmelCase_ : Optional[Any] = torch.ones(_A , device=_A , dtype=torch.int )
UpperCAmelCase_ : Union[str, Any] = torch.zeros(_A , device=_A , dtype=torch.bool )
if input_embeds is not None:
UpperCAmelCase_ : Union[str, Any] = input_embeds
else:
UpperCAmelCase_ : List[str] = self.transformer.transformer.wte(_A )
for i in range(_A ):
UpperCAmelCase_ : List[str] = self.transformer(inputs_embeds=_A )
UpperCAmelCase_ : Optional[Any] = outputs.logits
UpperCAmelCase_ : Optional[int] = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
UpperCAmelCase_ : Dict = logits.softmax(-1 ).log()
if scores is None:
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = logits.topk(_A , -1 )
UpperCAmelCase_ : Any = generated.expand(_A , *generated.shape[1:] )
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
UpperCAmelCase_ : str = next_tokens
else:
UpperCAmelCase_ : List[Any] = tokens.expand(_A , *tokens.shape[1:] )
UpperCAmelCase_ : Any = torch.cat((tokens, next_tokens) , dim=1 )
else:
UpperCAmelCase_ : List[str] = -float(np.inf )
UpperCAmelCase_ : List[str] = 0
UpperCAmelCase_ : Any = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
UpperCAmelCase_ : Dict = scores_sum / seq_lengths[:, None]
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = scores_sum_average.view(-1 ).topk(_A , -1 )
UpperCAmelCase_ : List[str] = next_tokens // scores_sum.shape[1]
UpperCAmelCase_ : List[str] = seq_lengths[next_tokens_source]
UpperCAmelCase_ : Union[str, Any] = next_tokens % scores_sum.shape[1]
UpperCAmelCase_ : List[Any] = next_tokens.unsqueeze(1 )
UpperCAmelCase_ : Optional[int] = tokens[next_tokens_source]
UpperCAmelCase_ : Any = torch.cat((tokens, next_tokens) , dim=1 )
UpperCAmelCase_ : Dict = generated[next_tokens_source]
UpperCAmelCase_ : str = scores_sum_average * seq_lengths
UpperCAmelCase_ : Union[str, Any] = is_stopped[next_tokens_source]
UpperCAmelCase_ : Any = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
UpperCAmelCase_ : List[str] = torch.cat((generated, next_token_embed) , dim=1 )
UpperCAmelCase_ : Any = is_stopped + next_tokens.eq(_A ).squeeze()
if is_stopped.all():
break
UpperCAmelCase_ : Optional[Any] = scores / seq_lengths
UpperCAmelCase_ : Optional[int] = scores.argsort(descending=_A )
# tokens tensors are already padded to max_seq_length
UpperCAmelCase_ : int = [tokens[i] for i in order]
UpperCAmelCase_ : List[str] = torch.stack(_A , dim=0 )
UpperCAmelCase_ : List[str] = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 216 |
'''simple docstring'''
def __UpperCAmelCase ( A : Dict ) -> Union[str, Any]:
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = head.next, head
while fast and fast.next:
UpperCAmelCase_ : str = fast.next.next
UpperCAmelCase_ : Tuple = slow.next
UpperCAmelCase_ : int = slow.next
UpperCAmelCase_ : Optional[int] = None # Don't forget here! But forget still works!
# reverse the second part
UpperCAmelCase_ : List[str] = None
while second:
UpperCAmelCase_ : List[str] = second.next
UpperCAmelCase_ : List[str] = node
UpperCAmelCase_ : int = second
UpperCAmelCase_ : Tuple = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
UpperCAmelCase_ : str = node.next
UpperCAmelCase_ : Optional[int] = head.next
return True
def __UpperCAmelCase ( A : str ) -> Tuple:
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
UpperCAmelCase_ : Optional[Any] = head
while fast and fast.next:
UpperCAmelCase_ , UpperCAmelCase_ : int = fast.next.next, slow.next
# 2. Push the second half into the stack
UpperCAmelCase_ : Union[str, Any] = [slow.val]
while slow.next:
UpperCAmelCase_ : Dict = slow.next
stack.append(slow.val )
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
UpperCAmelCase_ : List[str] = cur.next
return True
def __UpperCAmelCase ( A : int ) -> Union[str, Any]:
if not head or not head.next:
return True
UpperCAmelCase_ : int = {}
UpperCAmelCase_ : List[Any] = 0
while head:
if head.val in d:
d[head.val].append(A )
else:
UpperCAmelCase_ : List[str] = [pos]
UpperCAmelCase_ : List[str] = head.next
pos += 1
UpperCAmelCase_ : int = pos - 1
UpperCAmelCase_ : List[Any] = 0
for v in d.values():
if len(A ) % 2 != 0:
middle += 1
else:
UpperCAmelCase_ : List[str] = 0
for i in range(0 , len(A ) ):
if v[i] + v[len(A ) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True
| 216 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any]=7 ):
a__ = None
if token is not None:
a__ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'Bearer {token}'}
# The id of a workflow (not of a workflow run)
a__ = """636036"""
a__ = F'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'
a__ = requests.get(_UpperCamelCase , headers=_UpperCamelCase ).json()
return result["workflow_runs"]
def __lowercase ( __lowerCAmelCase : Dict ):
a__ = get_daily_ci_runs(_UpperCamelCase )
a__ = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
a__ = workflow_run["""id"""]
break
return workflow_run_id
def __lowercase ( __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple ):
a__ = get_last_daily_ci_runs(_UpperCamelCase )
if workflow_run_id is not None:
a__ = get_artifacts_links(worflow_run_id=_UpperCamelCase , token=_UpperCamelCase )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
a__ = artifacts_links[artifact_name]
download_artifact(
artifact_name=_UpperCamelCase , artifact_url=_UpperCamelCase , output_dir=_UpperCamelCase , token=_UpperCamelCase )
def __lowercase ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Dict , __lowerCAmelCase : Optional[Any] ):
get_last_daily_ci_artifacts(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
a__ = {}
for artifact_name in artifact_names:
a__ = os.path.join(_UpperCamelCase , F'{artifact_name}.zip' )
if os.path.isfile(_UpperCamelCase ):
a__ = {}
with zipfile.ZipFile(_UpperCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(_UpperCamelCase ):
# read the file
with z.open(_UpperCamelCase ) as f:
a__ = f.read().decode('UTF-8' )
return results
| 335 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class lowerCamelCase__ ( A__ ):
__lowerCamelCase = 42
class lowerCamelCase__ ( A__ , A__ ):
@register_to_config
def __init__( self : Optional[int] , __a : int = 16 , __a : int = 88 , __a : Optional[int] = None , __a : Optional[int] = None , __a : int = 1 , __a : float = 0.0 , __a : int = 32 , __a : Optional[int] = None , __a : bool = False , __a : Optional[int] = None , __a : str = "geglu" , __a : bool = True , __a : bool = True , ):
'''simple docstring'''
super().__init__()
lowerCamelCase__: Tuple = num_attention_heads
lowerCamelCase__: Dict = attention_head_dim
lowerCamelCase__: int = num_attention_heads * attention_head_dim
lowerCamelCase__: List[Any] = in_channels
lowerCamelCase__: List[Any] = torch.nn.GroupNorm(num_groups=__a , num_channels=__a , eps=1e-6 , affine=__a )
lowerCamelCase__: Any = nn.Linear(__a , __a )
# 3. Define transformers blocks
lowerCamelCase__: Any = nn.ModuleList(
[
BasicTransformerBlock(
__a , __a , __a , dropout=__a , cross_attention_dim=__a , activation_fn=__a , attention_bias=__a , double_self_attention=__a , norm_elementwise_affine=__a , )
for d in range(__a )
] )
lowerCamelCase__: int = nn.Linear(__a , __a )
def lowerCamelCase_ ( self : Any , __a : Any , __a : int=None , __a : List[Any]=None , __a : Dict=None , __a : Optional[int]=1 , __a : Dict=None , __a : bool = True , ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Union[str, Any] = hidden_states.shape
lowerCamelCase__: Any = batch_frames // num_frames
lowerCamelCase__: Optional[int] = hidden_states
lowerCamelCase__: int = hidden_states[None, :].reshape(__a , __a , __a , __a , __a )
lowerCamelCase__: Union[str, Any] = hidden_states.permute(0 , 2 , 1 , 3 , 4 )
lowerCamelCase__: int = self.norm(__a )
lowerCamelCase__: Union[str, Any] = hidden_states.permute(0 , 3 , 4 , 2 , 1 ).reshape(batch_size * height * width , __a , __a )
lowerCamelCase__: Dict = self.proj_in(__a )
# 2. Blocks
for block in self.transformer_blocks:
lowerCamelCase__: Union[str, Any] = block(
__a , encoder_hidden_states=__a , timestep=__a , cross_attention_kwargs=__a , class_labels=__a , )
# 3. Output
lowerCamelCase__: int = self.proj_out(__a )
lowerCamelCase__: List[Any] = (
hidden_states[None, None, :]
.reshape(__a , __a , __a , __a , __a )
.permute(0 , 3 , 4 , 1 , 2 )
.contiguous()
)
lowerCamelCase__: str = hidden_states.reshape(__a , __a , __a , __a )
lowerCamelCase__: str = hidden_states + residual
if not return_dict:
return (output,)
return TransformerTemporalModelOutput(sample=__a )
| 306 | 0 |
_lowercase : List[Any] ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowercase : List[str] =[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_lowercase : Optional[int] ={
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
} | 704 |
import operator as op
_lowercase : Optional[int] ="""scaler.pt"""
_lowercase : List[Any] ="""pytorch_model"""
_lowercase : Tuple ="""random_states"""
_lowercase : Tuple ="""optimizer"""
_lowercase : Dict ="""scheduler"""
_lowercase : List[str] ="""pytorch_model.bin"""
_lowercase : Optional[int] ="""pytorch_model.bin.index.json"""
_lowercase : List[Any] ="""model.safetensors"""
_lowercase : Union[str, Any] ="""model.safetensors.index.json"""
_lowercase : str ="""1.10.2"""
_lowercase : Optional[int] ="""py38"""
_lowercase : int ="""4.17.0"""
_lowercase : str =["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
_lowercase : int =["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
_lowercase : str =["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
_lowercase : int =["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
_lowercase : Union[str, Any] =["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
_lowercase : str ="""2.0.1"""
_lowercase : Tuple =["""pdsh""", """standard""", """openmpi""", """mvapich"""]
_lowercase : List[Any] =["""default""", """reduce-overhead""", """max-autotune"""]
_lowercase : Union[str, Any] ={""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
_lowercase : Optional[int] =[
"""nnodes""",
"""nproc_per_node""",
"""rdzv_backend""",
"""rdzv_endpoint""",
"""rdzv_id""",
"""rdzv_conf""",
"""standalone""",
"""max_restarts""",
"""monitor_interval""",
"""start_method""",
"""role""",
"""module""",
"""m""",
"""no_python""",
"""run_path""",
"""log_dir""",
"""r""",
"""redirects""",
"""t""",
"""tee""",
"""node_rank""",
"""master_addr""",
"""master_port""",
]
_lowercase : int =["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
_lowercase : Optional[Any] =["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]
| 412 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : str = logging.get_logger(__name__)
lowerCamelCase : int = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class _UpperCamelCase (a_ ):
snake_case_ = """unispeech-sat"""
def __init__( self , __UpperCamelCase=3_2 , __UpperCamelCase=7_6_8 , __UpperCamelCase=1_2 , __UpperCamelCase=1_2 , __UpperCamelCase=3_0_7_2 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0_2 , __UpperCamelCase=1e-5 , __UpperCamelCase="group" , __UpperCamelCase="gelu" , __UpperCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , __UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) , __UpperCamelCase=(1_0, 3, 3, 3, 3, 2, 2) , __UpperCamelCase=False , __UpperCamelCase=1_2_8 , __UpperCamelCase=1_6 , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.0_5 , __UpperCamelCase=1_0 , __UpperCamelCase=2 , __UpperCamelCase=0.0 , __UpperCamelCase=1_0 , __UpperCamelCase=0 , __UpperCamelCase=3_2_0 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , __UpperCamelCase=1_0_0 , __UpperCamelCase=2_5_6 , __UpperCamelCase=2_5_6 , __UpperCamelCase=0.1 , __UpperCamelCase="mean" , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=2_5_6 , __UpperCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , __UpperCamelCase=(5, 3, 3, 1, 1) , __UpperCamelCase=(1, 2, 3, 1, 1) , __UpperCamelCase=5_1_2 , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=2 , __UpperCamelCase=5_0_4 , **__UpperCamelCase , )-> Optional[int]:
super().__init__(**__UpperCamelCase , pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase )
__lowerCAmelCase = hidden_size
__lowerCAmelCase = feat_extract_norm
__lowerCAmelCase = feat_extract_activation
__lowerCAmelCase = list(__UpperCamelCase )
__lowerCAmelCase = list(__UpperCamelCase )
__lowerCAmelCase = list(__UpperCamelCase )
__lowerCAmelCase = conv_bias
__lowerCAmelCase = num_conv_pos_embeddings
__lowerCAmelCase = num_conv_pos_embedding_groups
__lowerCAmelCase = len(self.conv_dim )
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = hidden_dropout
__lowerCAmelCase = attention_dropout
__lowerCAmelCase = activation_dropout
__lowerCAmelCase = feat_proj_dropout
__lowerCAmelCase = final_dropout
__lowerCAmelCase = layerdrop
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = initializer_range
__lowerCAmelCase = vocab_size
__lowerCAmelCase = num_clusters
__lowerCAmelCase = do_stable_layer_norm
__lowerCAmelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
F""" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"""
F""" `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowerCAmelCase = apply_spec_augment
__lowerCAmelCase = mask_time_prob
__lowerCAmelCase = mask_time_length
__lowerCAmelCase = mask_time_min_masks
__lowerCAmelCase = mask_feature_prob
__lowerCAmelCase = mask_feature_length
__lowerCAmelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
__lowerCAmelCase = num_codevectors_per_group
__lowerCAmelCase = num_codevector_groups
__lowerCAmelCase = contrastive_logits_temperature
__lowerCAmelCase = feat_quantizer_dropout
__lowerCAmelCase = num_negatives
__lowerCAmelCase = codevector_dim
__lowerCAmelCase = proj_codevector_dim
__lowerCAmelCase = diversity_loss_weight
# ctc loss
__lowerCAmelCase = ctc_loss_reduction
__lowerCAmelCase = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowerCAmelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowerCAmelCase = list(__UpperCamelCase )
__lowerCAmelCase = list(__UpperCamelCase )
__lowerCAmelCase = list(__UpperCamelCase )
__lowerCAmelCase = xvector_output_dim
@property
def __UpperCAmelCase ( self )-> List[str]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 367 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase : List[Any] = {
'''configuration_owlvit''': [
'''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''OwlViTConfig''',
'''OwlViTOnnxConfig''',
'''OwlViTTextConfig''',
'''OwlViTVisionConfig''',
],
'''processing_owlvit''': ['''OwlViTProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[Any] = ['''OwlViTFeatureExtractor''']
lowerCamelCase : str = ['''OwlViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : List[str] = [
'''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OwlViTModel''',
'''OwlViTPreTrainedModel''',
'''OwlViTTextModel''',
'''OwlViTVisionModel''',
'''OwlViTForObjectDetection''',
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 367 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowercase_ : Tuple = get_tests_dir('''fixtures/spiece.model''')
@require_sentencepiece
@require_tokenizers
class lowercase ( a_ , unittest.TestCase ):
"""simple docstring"""
_UpperCamelCase : Optional[Any] = DebertaVaTokenizer
_UpperCamelCase : List[str] = DebertaVaTokenizerFast
_UpperCamelCase : Dict = True
_UpperCamelCase : Tuple = True
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_snake_case : str = DebertaVaTokenizer(lowerCamelCase_ , unk_token='<unk>' )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self : Union[str, Any] , lowerCamelCase_ : str ):
'''simple docstring'''
_snake_case : Optional[Any] = 'this is a test'
_snake_case : str = 'this is a test'
return input_text, output_text
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
_snake_case : int = '<pad>'
_snake_case : Optional[Any] = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_ ) , lowerCamelCase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_ ) , lowerCamelCase_ )
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
_snake_case : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<pad>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '[PAD]' )
self.assertEqual(len(lowerCamelCase_ ) , 3_00_01 )
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 3_00_00 )
def __UpperCAmelCase ( self : List[Any] ):
'''simple docstring'''
_snake_case : List[str] = ' \tHeLLo!how \n Are yoU? '
_snake_case : Dict = ['▁hello', '!', 'how', '▁are', '▁you', '?']
# fmt: on
_snake_case : Dict = DebertaVaTokenizer(lowerCamelCase_ , do_lower_case=lowerCamelCase_ )
_snake_case : Tuple = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[Any] = DebertaVaTokenizerFast(lowerCamelCase_ , do_lower_case=lowerCamelCase_ )
_snake_case : int = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
pass
@unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.' )
def __UpperCAmelCase ( self : Any ):
'''simple docstring'''
pass
def __UpperCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
_snake_case : Dict = 'I was born in 92000, and this is falsé.'
_snake_case : Dict = ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
_snake_case : Dict = DebertaVaTokenizer(lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : Union[str, Any] = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[str] = DebertaVaTokenizerFast(lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : Union[str, Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : Optional[int] ):
'''simple docstring'''
_snake_case : int = 'I was born in 92000, and this is falsé.'
_snake_case : Dict = ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
_snake_case : Any = DebertaVaTokenizer(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Any = DebertaVaTokenizerFast(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : str = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : Optional[int] ):
'''simple docstring'''
_snake_case : Dict = 'I was born in 92000, and this is falsé.'
_snake_case : str = ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
# fmt: on
_snake_case : Union[str, Any] = DebertaVaTokenizer(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : List[str] = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[Any] = DebertaVaTokenizerFast(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : Union[str, Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
_snake_case : str = 'I was born in 92000, and this is falsé.'
_snake_case : Optional[int] = ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
# fmt: on
_snake_case : int = DebertaVaTokenizer(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : Dict = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Optional[int] = DebertaVaTokenizerFast(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : List[Any] ):
'''simple docstring'''
_snake_case : str = ' \tHeLLo!how \n Are yoU? '
_snake_case : Any = ['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
# fmt: on
_snake_case : Any = DebertaVaTokenizer(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : Any = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Dict = DebertaVaTokenizerFast(lowerCamelCase_ , do_lower_case=lowerCamelCase_ , split_by_punct=lowerCamelCase_ )
_snake_case : List[str] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
_snake_case : List[str] = self.get_tokenizer()
_snake_case : int = self.get_rust_tokenizer()
_snake_case : int = 'I was born in 92000, and this is falsé.'
_snake_case : str = tokenizer.convert_ids_to_tokens(tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
_snake_case : Optional[Any] = rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ ) )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Optional[int] = tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
_snake_case : Optional[Any] = rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Tuple = self.get_rust_tokenizer()
_snake_case : Optional[Any] = tokenizer.encode(lowerCamelCase_ )
_snake_case : List[Any] = rust_tokenizer.encode(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
_snake_case : Optional[int] = 'This is a test'
_snake_case : Optional[Any] = [13, 1, 43_98, 25, 21, 12_89]
_snake_case : Union[str, Any] = ['▁', 'T', 'his', '▁is', '▁a', '▁test']
_snake_case : str = ['▁', '<unk>', 'his', '▁is', '▁a', '▁test']
_snake_case : List[Any] = DebertaVaTokenizer(lowerCamelCase_ , keep_accents=lowerCamelCase_ )
_snake_case : List[Any] = DebertaVaTokenizerFast(lowerCamelCase_ , keep_accents=lowerCamelCase_ )
_snake_case : str = tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Tuple = tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[Any] = tokenizer.convert_ids_to_tokens(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[Any] = rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Any = rust_tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Any = rust_tokenizer.convert_ids_to_tokens(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
# fmt: off
_snake_case : Optional[Any] = 'I was born in 92000, and this is falsé.'
_snake_case : Tuple = [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
_snake_case : List[str] = ['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
_snake_case : Dict = ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
# fmt: on
_snake_case : Any = tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[str] = tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[str] = tokenizer.convert_ids_to_tokens(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Optional[Any] = rust_tokenizer.encode(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : Optional[Any] = rust_tokenizer.tokenize(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
_snake_case : List[str] = rust_tokenizer.convert_ids_to_tokens(lowerCamelCase_ )
self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ )
def __UpperCAmelCase ( self : List[Any] ):
'''simple docstring'''
_snake_case : Tuple = DebertaVaTokenizer(lowerCamelCase_ )
_snake_case : Dict = tokenizer.encode('sequence builders' )
_snake_case : Union[str, Any] = tokenizer.encode('multi-sequence build' )
_snake_case : int = tokenizer.build_inputs_with_special_tokens(lowerCamelCase_ )
_snake_case : int = tokenizer.build_inputs_with_special_tokens(lowerCamelCase_ , lowerCamelCase_ )
self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] , lowerCamelCase_ )
self.assertEqual(
[tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] , lowerCamelCase_ , )
@slow
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
_snake_case : List[Any] = {'input_ids': [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCamelCase_ , model_name='microsoft/deberta-v2-xlarge' , revision='ad6e42c1532ddf3a15c39246b63f5559d558b670' , )
| 652 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __UpperCAmelCase ( self : Optional[int] ):
'''simple docstring'''
_snake_case : Tuple = 1
_snake_case : str = 3
_snake_case : List[str] = (32, 32)
_snake_case : int = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowerCamelCase_ )
return image
@property
def __UpperCAmelCase ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
_snake_case : Union[str, Any] = 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 , )
return model
@property
def __UpperCAmelCase ( self : Dict ):
'''simple docstring'''
torch.manual_seed(0 )
_snake_case : Optional[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 , )
return model
@property
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
_snake_case : List[str] = 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=10_00 , )
return CLIPTextModel(lowerCamelCase_ )
@property
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
def extract(*lowerCamelCase_ : Optional[int] , **lowerCamelCase_ : str ):
class lowercase :
"""simple docstring"""
def __init__( self : Tuple ):
'''simple docstring'''
_snake_case : List[str] = torch.ones([0] )
def __UpperCAmelCase ( self : int , lowerCamelCase_ : Tuple ):
'''simple docstring'''
self.pixel_values.to(lowerCamelCase_ )
return self
return Out()
return extract
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
_snake_case : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case : int = self.dummy_cond_unet
_snake_case : str = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=lowerCamelCase_ , set_alpha_to_one=lowerCamelCase_ , )
_snake_case : Union[str, Any] = self.dummy_vae
_snake_case : Optional[Any] = self.dummy_text_encoder
_snake_case : Optional[Any] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
# make sure here that pndm scheduler skips prk
_snake_case : Union[str, Any] = StableDiffusionPipeline(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , vae=lowerCamelCase_ , text_encoder=lowerCamelCase_ , tokenizer=lowerCamelCase_ , safety_checker=lowerCamelCase_ , feature_extractor=self.dummy_extractor , )
_snake_case : str = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_snake_case : List[str] = 'A painting of a squirrel eating a burger'
_snake_case : Optional[int] = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
_snake_case : Optional[int] = sd_pipe([prompt] , generator=lowerCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' )
_snake_case : Union[str, Any] = output.images
_snake_case : List[str] = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
_snake_case : Any = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' , return_dict=lowerCamelCase_ , )[0]
_snake_case : Tuple = image[0, -3:, -3:, -1]
_snake_case : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_snake_case : Optional[int] = np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
_snake_case : Tuple = 'cpu' # ensure determinism for the device-dependent torch.Generator
_snake_case : List[str] = self.dummy_cond_unet
_snake_case : List[str] = PNDMScheduler(skip_prk_steps=lowerCamelCase_ )
_snake_case : int = self.dummy_vae
_snake_case : List[Any] = self.dummy_text_encoder
_snake_case : Optional[int] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
# make sure here that pndm scheduler skips prk
_snake_case : Any = StableDiffusionPipeline(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , vae=lowerCamelCase_ , text_encoder=lowerCamelCase_ , tokenizer=lowerCamelCase_ , safety_checker=lowerCamelCase_ , feature_extractor=self.dummy_extractor , )
_snake_case : Union[str, Any] = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_snake_case : str = 'A painting of a squirrel eating a burger'
_snake_case : Optional[int] = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
_snake_case : Tuple = sd_pipe([prompt] , generator=lowerCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' )
_snake_case : Optional[Any] = output.images
_snake_case : Optional[Any] = torch.Generator(device=lowerCamelCase_ ).manual_seed(0 )
_snake_case : Tuple = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=6.0 , num_inference_steps=2 , output_type='np' , return_dict=lowerCamelCase_ , )[0]
_snake_case : Dict = image[0, -3:, -3:, -1]
_snake_case : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_snake_case : str = np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
_snake_case : Union[str, Any] = StableDiffusionPipeline.from_pretrained(
'hf-internal-testing/tiny-stable-diffusion-lms-pipe' , safety_checker=lowerCamelCase_ )
assert isinstance(lowerCamelCase_ , lowerCamelCase_ )
assert isinstance(pipe.scheduler , lowerCamelCase_ )
assert pipe.safety_checker is None
_snake_case : Dict = pipe('example prompt' , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(lowerCamelCase_ )
_snake_case : Any = StableDiffusionPipeline.from_pretrained(lowerCamelCase_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
_snake_case : Union[str, Any] = pipe('example prompt' , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != 'cuda' , 'This test requires a GPU' )
def __UpperCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
_snake_case : Union[str, Any] = self.dummy_cond_unet
_snake_case : Optional[Any] = PNDMScheduler(skip_prk_steps=lowerCamelCase_ )
_snake_case : Any = self.dummy_vae
_snake_case : Optional[Any] = self.dummy_text_encoder
_snake_case : Optional[int] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
# put models in fp16
_snake_case : str = unet.half()
_snake_case : Union[str, Any] = vae.half()
_snake_case : Dict = bert.half()
# make sure here that pndm scheduler skips prk
_snake_case : List[str] = StableDiffusionPipeline(
unet=lowerCamelCase_ , scheduler=lowerCamelCase_ , vae=lowerCamelCase_ , text_encoder=lowerCamelCase_ , tokenizer=lowerCamelCase_ , safety_checker=lowerCamelCase_ , feature_extractor=self.dummy_extractor , )
_snake_case : List[str] = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_snake_case : Tuple = 'A painting of a squirrel eating a burger'
_snake_case : Optional[int] = sd_pipe([prompt] , num_inference_steps=2 , output_type='np' ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self : List[str] ):
'''simple docstring'''
_snake_case : Optional[int] = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' , safety_checker=lowerCamelCase_ )
_snake_case : List[str] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_snake_case : Any = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_snake_case : Optional[int] = (
'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
' children from bahnhof zoo, detailed '
)
_snake_case : List[str] = 40_03_66_03_46
_snake_case : int = 7
# without safety guidance (sld_guidance_scale = 0)
_snake_case : Union[str, Any] = torch.manual_seed(lowerCamelCase_ )
_snake_case : Union[str, Any] = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , num_inference_steps=50 , output_type='np' , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
_snake_case : str = output.images
_snake_case : Dict = image[0, -3:, -3:, -1]
_snake_case : Optional[int] = [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
# without safety guidance (strong configuration)
_snake_case : Tuple = torch.manual_seed(lowerCamelCase_ )
_snake_case : int = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , num_inference_steps=50 , output_type='np' , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_snake_case : Tuple = output.images
_snake_case : int = image[0, -3:, -3:, -1]
_snake_case : List[Any] = [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : int ):
'''simple docstring'''
_snake_case : str = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' , safety_checker=lowerCamelCase_ )
_snake_case : Tuple = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
_snake_case : Any = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_snake_case : Union[str, Any] = 'padme amidala taking a bath artwork, safe for work, no nudity'
_snake_case : Optional[Any] = 27_34_97_17_55
_snake_case : Union[str, Any] = 7
_snake_case : Dict = torch.manual_seed(lowerCamelCase_ )
_snake_case : Tuple = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , num_inference_steps=50 , output_type='np' , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
_snake_case : Any = output.images
_snake_case : int = image[0, -3:, -3:, -1]
_snake_case : str = [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
_snake_case : Optional[Any] = torch.manual_seed(lowerCamelCase_ )
_snake_case : Any = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , num_inference_steps=50 , output_type='np' , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_snake_case : str = output.images
_snake_case : List[str] = image[0, -3:, -3:, -1]
_snake_case : Union[str, Any] = [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : Tuple ):
'''simple docstring'''
_snake_case : Optional[int] = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' )
_snake_case : Optional[int] = sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
_snake_case : List[Any] = (
'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
' leyendecker'
)
_snake_case : Union[str, Any] = 10_44_35_52_34
_snake_case : Dict = 12
_snake_case : Optional[int] = torch.manual_seed(lowerCamelCase_ )
_snake_case : Any = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , num_inference_steps=50 , output_type='np' , width=5_12 , height=5_12 , sld_guidance_scale=0 , )
_snake_case : Optional[int] = output.images
_snake_case : int = image[0, -3:, -3:, -1]
_snake_case : Optional[int] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-7
_snake_case : List[Any] = torch.manual_seed(lowerCamelCase_ )
_snake_case : Optional[int] = sd_pipe(
[prompt] , generator=lowerCamelCase_ , guidance_scale=lowerCamelCase_ , num_inference_steps=50 , output_type='np' , width=5_12 , height=5_12 , sld_guidance_scale=20_00 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
_snake_case : str = output.images
_snake_case : List[str] = image[0, -3:, -3:, -1]
_snake_case : int = np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561] )
assert image.shape == (1, 5_12, 5_12, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 652 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 50 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
UpperCamelCase : List[Any] = 'examples/'
UpperCamelCase : int = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
UpperCamelCase : Any = {
'init': 'src/transformers/__init__.py',
'setup': 'setup.py',
}
UpperCamelCase : Any = 'README.md'
def A__ ( __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ):
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ , lowerCamelCase__ = REPLACE_PATTERNS[pattern]
lowerCamelCase__ = replace.replace("""VERSION""" , __lowerCAmelCase )
lowerCamelCase__ = re_pattern.sub(__lowerCAmelCase , __lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : str ):
for folder, directories, fnames in os.walk(__lowerCAmelCase ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase , pattern="""examples""" )
def A__ ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[Any]=False ):
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not patch:
update_version_in_examples(__lowerCAmelCase )
def A__ ( ):
lowerCamelCase__ = """🤗 Transformers currently provides the following architectures"""
lowerCamelCase__ = """1. Want to contribute a new model?"""
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCamelCase__ = f.readlines()
# Find the start of the list.
lowerCamelCase__ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
lowerCamelCase__ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
lowerCamelCase__ = lines[index].replace(
"""https://huggingface.co/docs/transformers/main/model_doc""" , """https://huggingface.co/docs/transformers/model_doc""" , )
index += 1
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
def A__ ( ):
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
lowerCamelCase__ = f.read()
lowerCamelCase__ = REPLACE_PATTERNS["""init"""][0].search(__lowerCAmelCase ).groups()[0]
return packaging.version.parse(__lowerCAmelCase )
def A__ ( __lowerCAmelCase : Union[str, Any]=False ):
lowerCamelCase__ = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
lowerCamelCase__ = default_version.base_version
elif patch:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
lowerCamelCase__ = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
lowerCamelCase__ = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase , patch=__lowerCAmelCase )
if not patch:
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
def A__ ( ):
lowerCamelCase__ = get_version()
lowerCamelCase__ = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
lowerCamelCase__ = current_version.base_version
# Check with the user we got that right.
lowerCamelCase__ = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__lowerCAmelCase ) == 0:
lowerCamelCase__ = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__lowerCAmelCase )
print("""Cleaning main README, don't forget to run `make fix-copies`.""" )
clean_main_ref_in_model_list()
if __name__ == "__main__":
UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
UpperCamelCase : Any = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 50 | 1 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = len(matrix[0] )
__SCREAMING_SNAKE_CASE = min(lowerCAmelCase_ , lowerCAmelCase_ )
for row in range(lowerCAmelCase_ ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = matrix[col][row] / matrix[row][row]
for i in range(lowerCAmelCase_ , lowerCAmelCase_ ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
__SCREAMING_SNAKE_CASE = True
for i in range(row + 1 , lowerCAmelCase_ ):
if matrix[i][row] != 0:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = matrix[i], matrix[row]
__SCREAMING_SNAKE_CASE = False
break
if reduce:
rank -= 1
for i in range(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 553 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = str(lowerCAmelCase_ )
return n == n[::-1]
def UpperCAmelCase__ (lowerCAmelCase_ = 100_0000 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
for i in range(1 , lowerCAmelCase_ ):
if is_palindrome(lowerCAmelCase_ ) and is_palindrome(bin(lowerCAmelCase_ ).split("b" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 553 | 1 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ ) -> list:
if len(lowerCAmelCase_ ) <= 1:
return [tuple(lowerCAmelCase_ )]
_snake_case = []
def generate(lowerCAmelCase_ , lowerCAmelCase_ ):
_snake_case = [0] * n
res.append(tuple(lowerCAmelCase_ ) )
_snake_case = 0
while i < n:
if c[i] < i:
if i % 2 == 0:
_snake_case , _snake_case = arr[i], arr[0]
else:
_snake_case , _snake_case = arr[i], arr[c[i]]
res.append(tuple(lowerCAmelCase_ ) )
c[i] += 1
_snake_case = 0
else:
_snake_case = 0
i += 1
generate(len(lowerCAmelCase_ ) , lowerCAmelCase_ )
return res
if __name__ == "__main__":
snake_case = input('''Enter numbers separated by a comma:\n''').strip()
snake_case = [int(item) for item in user_input.split(''',''')]
print(heaps(arr))
| 103 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__lowerCamelCase :str = logging.get_logger(__name__)
__lowerCamelCase :Union[str, Any] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
__lowerCamelCase :str = {
'vocab_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
),
'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
),
'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
'bert-base-multilingual-uncased': (
'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
),
'bert-base-multilingual-cased': (
'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
),
'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
'bert-large-uncased-whole-word-masking': (
'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking': (
'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
),
'bert-large-uncased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-large-cased-whole-word-masking-finetuned-squad': (
'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
),
'bert-base-cased-finetuned-mrpc': (
'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-cased': (
'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
),
'bert-base-german-dbmdz-uncased': (
'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-cased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
),
'TurkuNLP/bert-base-finnish-uncased-v1': (
'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
),
'wietsedv/bert-base-dutch-cased': (
'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
),
},
}
__lowerCamelCase :List[Any] = {
'bert-base-uncased': 512,
'bert-large-uncased': 512,
'bert-base-cased': 512,
'bert-large-cased': 512,
'bert-base-multilingual-uncased': 512,
'bert-base-multilingual-cased': 512,
'bert-base-chinese': 512,
'bert-base-german-cased': 512,
'bert-large-uncased-whole-word-masking': 512,
'bert-large-cased-whole-word-masking': 512,
'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
'bert-large-cased-whole-word-masking-finetuned-squad': 512,
'bert-base-cased-finetuned-mrpc': 512,
'bert-base-german-dbmdz-cased': 512,
'bert-base-german-dbmdz-uncased': 512,
'TurkuNLP/bert-base-finnish-cased-v1': 512,
'TurkuNLP/bert-base-finnish-uncased-v1': 512,
'wietsedv/bert-base-dutch-cased': 512,
}
__lowerCamelCase :Tuple = {
'bert-base-uncased': {'do_lower_case': True},
'bert-large-uncased': {'do_lower_case': True},
'bert-base-cased': {'do_lower_case': False},
'bert-large-cased': {'do_lower_case': False},
'bert-base-multilingual-uncased': {'do_lower_case': True},
'bert-base-multilingual-cased': {'do_lower_case': False},
'bert-base-chinese': {'do_lower_case': False},
'bert-base-german-cased': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
'bert-large-cased-whole-word-masking': {'do_lower_case': False},
'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
'bert-base-german-dbmdz-cased': {'do_lower_case': False},
'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}
class A__ ( __lowercase):
"""simple docstring"""
snake_case__ : Union[str, Any] =VOCAB_FILES_NAMES
snake_case__ : Union[str, Any] =PRETRAINED_VOCAB_FILES_MAP
snake_case__ : int =PRETRAINED_INIT_CONFIGURATION
snake_case__ : Tuple =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ : str =BertTokenizer
def __init__( self: str , __a: Union[str, Any]=None , __a: Tuple=None , __a: int=True , __a: List[str]="[UNK]" , __a: Optional[Any]="[SEP]" , __a: Union[str, Any]="[PAD]" , __a: Optional[Any]="[CLS]" , __a: Optional[Any]="[MASK]" , __a: List[Any]=True , __a: int=None , **__a: Union[str, Any] , )-> int:
super().__init__(
__a , tokenizer_file=__a , do_lower_case=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , tokenize_chinese_chars=__a , strip_accents=__a , **__a , )
lowerCamelCase : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , __a ) != do_lower_case
or normalizer_state.get("""strip_accents""" , __a ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , __a ) != tokenize_chinese_chars
):
lowerCamelCase : int = getattr(__a , normalizer_state.pop("""type""" ) )
lowerCamelCase : str = do_lower_case
lowerCamelCase : List[Any] = strip_accents
lowerCamelCase : Tuple = tokenize_chinese_chars
lowerCamelCase : str = normalizer_class(**__a )
lowerCamelCase : Union[str, Any] = do_lower_case
def a__ ( self: Tuple , __a: List[Any] , __a: int=None )-> Optional[Any]:
lowerCamelCase : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def a__ ( self: Any , __a: List[int] , __a: Optional[List[int]] = None )-> List[int]:
lowerCamelCase : Dict = [self.sep_token_id]
lowerCamelCase : 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: Optional[int] , __a: str , __a: Optional[str] = None )-> Tuple[str]:
lowerCamelCase : Optional[Any] = self._tokenizer.model.save(__a , name=__a )
return tuple(__a )
| 222 | 0 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
snake_case = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCAmelCase ( UpperCamelCase_ ):
def __init__( self : List[Any] , *a__ : Union[str, Any] , a__ : Union[str, Any]=None , a__ : Optional[Any]=None , a__ : Dict=None , **a__ : List[str] ):
'''simple docstring'''
super().__init__(*a__ , **a__ )
lowerCAmelCase__ : Optional[Any] = eval_examples
lowerCAmelCase__ : List[str] = post_process_function
lowerCAmelCase__ : Dict = quant_trainer_args
lowerCAmelCase__ : Optional[int] = 128 # default number of calibration samples
def _A ( self : str , a__ : Any=None ):
'''simple docstring'''
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("Trainer: calibration requires an calib_dataset." )
lowerCAmelCase__ : Dict = calib_dataset if calib_dataset is not None else self.calib_dataset
lowerCAmelCase__ : Tuple = self._remove_unused_columns(a__ , description="Calibration" )
return DataLoader(
a__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=a__ , )
def _A ( self : Optional[Any] , a__ : List[Any]=None ):
'''simple docstring'''
lowerCAmelCase__ : int = self.train_dataset if calib_dataset is None else calib_dataset
lowerCAmelCase__ : Dict = self.get_calib_dataloader(a__ )
lowerCAmelCase__ : str = self.model
quant_trainer.configure_model(a__ , self.quant_trainer_args , calib=a__ )
model.eval()
quant_trainer.enable_calibration(a__ )
logger.info("***** Running calibration *****" )
logger.info(F''' Num examples = {self.calib_num}''' )
logger.info(F''' Batch size = {calib_dataloader.batch_size}''' )
for step, inputs in enumerate(a__ ):
# Prediction step
lowerCAmelCase__ : int = self.prediction_step(a__ , a__ , prediction_loss_only=a__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(a__ , self.quant_trainer_args )
lowerCAmelCase__ : Any = model
def _A ( self : Any , a__ : List[str]=None , a__ : Union[str, Any]=None , a__ : List[Any]=None , a__ : str = "eval" ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = self.eval_dataset if eval_dataset is None else eval_dataset
lowerCAmelCase__ : Optional[Any] = self.get_eval_dataloader(a__ )
lowerCAmelCase__ : Tuple = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
lowerCAmelCase__ : Optional[Any] = self.compute_metrics
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : Optional[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCAmelCase__ : Union[str, Any] = eval_loop(
a__ , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a__ , )
finally:
lowerCAmelCase__ : Tuple = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
lowerCAmelCase__ : Union[str, Any] = self.post_process_function(a__ , a__ , output.predictions )
lowerCAmelCase__ : List[Any] = self.compute_metrics(a__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
lowerCAmelCase__ : Union[str, Any] = metrics.pop(a__ )
self.log(a__ )
else:
lowerCAmelCase__ : Optional[int] = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
lowerCAmelCase__ : List[Any] = self.callback_handler.on_evaluate(self.args , self.state , self.control , a__ )
return metrics
def _A ( self : int , a__ : Tuple , a__ : Union[str, Any] , a__ : List[str]=None , a__ : str = "test" ):
'''simple docstring'''
lowerCAmelCase__ : int = self.get_test_dataloader(a__ )
# Temporarily disable metric computation, we will do it in the loop here.
lowerCAmelCase__ : int = self.compute_metrics
lowerCAmelCase__ : Tuple = None
lowerCAmelCase__ : List[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
lowerCAmelCase__ : Optional[Any] = eval_loop(
a__ , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a__ , )
finally:
lowerCAmelCase__ : Dict = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
lowerCAmelCase__ : int = self.post_process_function(a__ , a__ , output.predictions , "predict" )
lowerCAmelCase__ : List[str] = self.compute_metrics(a__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F'''{metric_key_prefix}_''' ):
lowerCAmelCase__ : Tuple = metrics.pop(a__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=a__ )
def _A ( self : List[str] , a__ : int="./" ):
'''simple docstring'''
lowerCAmelCase__ : Dict = self.eval_dataset
lowerCAmelCase__ : Any = self.get_eval_dataloader(a__ )
lowerCAmelCase__ : Union[str, Any] = next(iter(a__ ) )
# saving device - to make it consistent
lowerCAmelCase__ : List[Any] = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
# convert to tuple
lowerCAmelCase__ : Tuple = tuple(v.to(a__ ) for k, v in batch.items() )
logger.info("Converting model to be onnx compatible" )
from pytorch_quantization.nn import TensorQuantizer
lowerCAmelCase__ : str = True
lowerCAmelCase__ : Tuple = self.model.to(a__ )
model.eval()
model.float()
lowerCAmelCase__ : Union[str, Any] = model.module if hasattr(a__ , "module" ) else model
quant_trainer.configure_model(a__ , self.quant_trainer_args )
lowerCAmelCase__ : List[str] = os.path.join(a__ , "model.onnx" )
logger.info(F'''exporting model to {output_model_file}''' )
lowerCAmelCase__ : Union[str, Any] = {0: "batch_size", 1: "seq_len"}
torch.onnx.export(
a__ , a__ , a__ , export_params=a__ , opset_version=13 , do_constant_folding=a__ , input_names=["input_ids", "attention_mask", "token_type_ids"] , output_names=["output_start_logits", "output_end_logits"] , dynamic_axes={
"input_ids": axes,
"attention_mask": axes,
"token_type_ids": axes,
"output_start_logits": axes,
"output_end_logits": axes,
} , verbose=a__ , )
logger.info("onnx export finished" )
| 702 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = ["""ConvNextFeatureExtractor"""]
snake_case = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case = [
"""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
snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 568 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ : str = logging.get_logger(__name__)
lowercase_ : Any = {
'microsoft/table-transformer-detection': (
'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
),
}
class _lowerCamelCase ( UpperCamelCase_ ):
__a = "table-transformer"
__a = ["past_key_values"]
__a = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self , lowerCAmelCase=True , lowerCAmelCase=None , lowerCAmelCase=3 , lowerCAmelCase=100 , lowerCAmelCase=6 , lowerCAmelCase=2048 , lowerCAmelCase=8 , lowerCAmelCase=6 , lowerCAmelCase=2048 , lowerCAmelCase=8 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=True , lowerCAmelCase="relu" , lowerCAmelCase=256 , lowerCAmelCase=0.1 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=0.02 , lowerCAmelCase=1.0 , lowerCAmelCase=False , lowerCAmelCase="sine" , lowerCAmelCase="resnet50" , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=1 , lowerCAmelCase=5 , lowerCAmelCase=2 , lowerCAmelCase=1 , lowerCAmelCase=1 , lowerCAmelCase=5 , lowerCAmelCase=2 , lowerCAmelCase=0.1 , **lowerCAmelCase , ) -> Union[str, Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
SCREAMING_SNAKE_CASE__: Optional[Any]= CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(lowerCAmelCase , lowerCAmelCase ):
SCREAMING_SNAKE_CASE__: Optional[int]= backbone_config.get('''model_type''' )
SCREAMING_SNAKE_CASE__: int= CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__: Union[str, Any]= config_class.from_dict(lowerCAmelCase )
# set timm attributes to None
SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__: Union[str, Any]= None, None, None
SCREAMING_SNAKE_CASE__: Optional[Any]= use_timm_backbone
SCREAMING_SNAKE_CASE__: Optional[Any]= backbone_config
SCREAMING_SNAKE_CASE__: Dict= num_channels
SCREAMING_SNAKE_CASE__: Optional[Any]= num_queries
SCREAMING_SNAKE_CASE__: Tuple= d_model
SCREAMING_SNAKE_CASE__: Dict= encoder_ffn_dim
SCREAMING_SNAKE_CASE__: Optional[int]= encoder_layers
SCREAMING_SNAKE_CASE__: Union[str, Any]= encoder_attention_heads
SCREAMING_SNAKE_CASE__: Optional[Any]= decoder_ffn_dim
SCREAMING_SNAKE_CASE__: Optional[int]= decoder_layers
SCREAMING_SNAKE_CASE__: int= decoder_attention_heads
SCREAMING_SNAKE_CASE__: Optional[int]= dropout
SCREAMING_SNAKE_CASE__: Any= attention_dropout
SCREAMING_SNAKE_CASE__: Tuple= activation_dropout
SCREAMING_SNAKE_CASE__: Union[str, Any]= activation_function
SCREAMING_SNAKE_CASE__: Optional[Any]= init_std
SCREAMING_SNAKE_CASE__: Tuple= init_xavier_std
SCREAMING_SNAKE_CASE__: List[str]= encoder_layerdrop
SCREAMING_SNAKE_CASE__: Dict= decoder_layerdrop
SCREAMING_SNAKE_CASE__: int= encoder_layers
SCREAMING_SNAKE_CASE__: Optional[Any]= auxiliary_loss
SCREAMING_SNAKE_CASE__: Dict= position_embedding_type
SCREAMING_SNAKE_CASE__: List[str]= backbone
SCREAMING_SNAKE_CASE__: Union[str, Any]= use_pretrained_backbone
SCREAMING_SNAKE_CASE__: Any= dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE__: Any= class_cost
SCREAMING_SNAKE_CASE__: Any= bbox_cost
SCREAMING_SNAKE_CASE__: Optional[Any]= giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE__: Optional[Any]= mask_loss_coefficient
SCREAMING_SNAKE_CASE__: str= dice_loss_coefficient
SCREAMING_SNAKE_CASE__: Optional[int]= bbox_loss_coefficient
SCREAMING_SNAKE_CASE__: Tuple= giou_loss_coefficient
SCREAMING_SNAKE_CASE__: Optional[Any]= eos_coefficient
super().__init__(is_encoder_decoder=lowerCAmelCase , **lowerCAmelCase )
@property
def UpperCamelCase_ ( self ) -> int:
return self.encoder_attention_heads
@property
def UpperCamelCase_ ( self ) -> int:
return self.d_model
class _lowerCamelCase ( UpperCamelCase_ ):
__a = version.parse("1.11" )
@property
def UpperCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''pixel_mask''', {0: '''batch'''}),
] )
@property
def UpperCamelCase_ ( self ) -> float:
return 1e-5
@property
def UpperCamelCase_ ( self ) -> int:
return 12
| 64 |
'''simple docstring'''
import sys
UpperCamelCase_ : Union[str, Any] = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def _lowerCAmelCase (_lowercase = N ):
"""simple docstring"""
a__ = -sys.maxsize - 1
for i in range(len(_lowercase ) - 12 ):
a__ = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
a__ = product
return largest_product
if __name__ == "__main__":
print(F"{solution() = }")
| 331 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = IFInpaintingSuperResolutionPipeline
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"} )
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {"latents"}
def _snake_case ( self : Tuple ):
return self._get_superresolution_dummy_components()
def _snake_case ( self : int , __lowerCamelCase : List[Any] , __lowerCamelCase : int=0 ):
if str(__lowerCamelCase ).startswith("mps" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(__lowerCamelCase )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 16, 16) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
SCREAMING_SNAKE_CASE = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"original_image": original_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _snake_case ( self : Optional[Any] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _snake_case ( self : int ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def _snake_case ( self : Optional[int] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _snake_case ( self : Optional[Any] ):
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _snake_case ( self : List[Any] ):
self._test_save_load_local()
def _snake_case ( self : Any ):
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , ) | 698 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("CPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("GPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Model" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , buff=0.5 , aligned_edge=__lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(__lowerCamelCase )
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
rect.set_stroke(__lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(__lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=__lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=__lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=__lowerCamelCase , buff=0.0 )
self.add(__lowerCamelCase )
cpu_targs.append(__lowerCamelCase )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*__lowerCamelCase ).arrange(__lowerCamelCase , buff=0 )
SCREAMING_SNAKE_CASE = Text("Loaded Checkpoint" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(__lowerCamelCase , __lowerCamelCase ).arrange(__lowerCamelCase , aligned_edge=__lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
SCREAMING_SNAKE_CASE = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(__lowerCamelCase , __lowerCamelCase )
SCREAMING_SNAKE_CASE = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(__lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
SCREAMING_SNAKE_CASE = MarkupText(
f"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(__lowerCamelCase ) , Write(__lowerCamelCase ) )
self.play(Write(__lowerCamelCase , run_time=1 ) , Create(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(__lowerCamelCase ):
SCREAMING_SNAKE_CASE = fill.copy().set_fill(__lowerCamelCase , opacity=0.7 )
target.move_to(__lowerCamelCase )
first_animations.append(GrowFromCenter(__lowerCamelCase , run_time=1 ) )
SCREAMING_SNAKE_CASE = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(__lowerCamelCase , run_time=1.5 ) )
self.play(*__lowerCamelCase )
self.play(*__lowerCamelCase )
self.wait() | 698 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCamelCase__ : Dict = logging.get_logger(__name__)
def UpperCAmelCase_ ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : List[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = b.T
SCREAMING_SNAKE_CASE_ = np.sum(np.square(__UpperCAmelCase ) , axis=1 )
SCREAMING_SNAKE_CASE_ = np.sum(np.square(__UpperCAmelCase ) , axis=0 )
SCREAMING_SNAKE_CASE_ = np.matmul(__UpperCAmelCase , __UpperCAmelCase )
SCREAMING_SNAKE_CASE_ = aa[:, None] - 2 * ab + ba[None, :]
return d
def UpperCAmelCase_ ( __UpperCAmelCase : Dict , __UpperCAmelCase : Optional[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = x.reshape(-1 , 3 )
SCREAMING_SNAKE_CASE_ = squared_euclidean_distance(__UpperCAmelCase , __UpperCAmelCase )
return np.argmin(__UpperCAmelCase , axis=1 )
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = ["pixel_values"]
def __init__( self : List[Any] , _lowerCAmelCase : Optional[Union[List[List[int]], np.ndarray]] = None , _lowerCAmelCase : bool = True , _lowerCAmelCase : Dict[str, int] = None , _lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _lowerCAmelCase : bool = True , _lowerCAmelCase : bool = True , **_lowerCAmelCase : List[Any] , ):
super().__init__(**_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = size if size is not None else {'height': 256, 'width': 256}
SCREAMING_SNAKE_CASE_ = get_size_dict(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = np.array(_lowerCAmelCase ) if clusters is not None else None
SCREAMING_SNAKE_CASE_ = do_resize
SCREAMING_SNAKE_CASE_ = size
SCREAMING_SNAKE_CASE_ = resample
SCREAMING_SNAKE_CASE_ = do_normalize
SCREAMING_SNAKE_CASE_ = do_color_quantize
def lowerCAmelCase_ ( self : str , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : Dict[str, int] , _lowerCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_lowerCAmelCase : Tuple , ):
SCREAMING_SNAKE_CASE_ = get_size_dict(_lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}" )
return resize(
_lowerCAmelCase , size=(size['height'], size['width']) , resample=_lowerCAmelCase , data_format=_lowerCAmelCase , **_lowerCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , ):
SCREAMING_SNAKE_CASE_ = rescale(image=_lowerCAmelCase , scale=1 / 127.5 , data_format=_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = image - 1
return image
def lowerCAmelCase_ ( self : List[Any] , _lowerCAmelCase : ImageInput , _lowerCAmelCase : bool = None , _lowerCAmelCase : Dict[str, int] = None , _lowerCAmelCase : PILImageResampling = None , _lowerCAmelCase : bool = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : Optional[Union[List[List[int]], np.ndarray]] = None , _lowerCAmelCase : Optional[Union[str, TensorType]] = None , _lowerCAmelCase : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **_lowerCAmelCase : Optional[Any] , ):
SCREAMING_SNAKE_CASE_ = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE_ = size if size is not None else self.size
SCREAMING_SNAKE_CASE_ = get_size_dict(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE_ = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE_ = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
SCREAMING_SNAKE_CASE_ = clusters if clusters is not None else self.clusters
SCREAMING_SNAKE_CASE_ = np.array(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = make_list_of_images(_lowerCAmelCase )
if not valid_images(_lowerCAmelCase ):
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_color_quantize and clusters is None:
raise ValueError('Clusters must be specified if do_color_quantize is True.' )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE_ = [to_numpy_array(_lowerCAmelCase ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE_ = [self.resize(image=_lowerCAmelCase , size=_lowerCAmelCase , resample=_lowerCAmelCase ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE_ = [self.normalize(image=_lowerCAmelCase ) for image in images]
if do_color_quantize:
SCREAMING_SNAKE_CASE_ = [to_channel_dimension_format(_lowerCAmelCase , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
SCREAMING_SNAKE_CASE_ = np.array(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = color_quantize(_lowerCAmelCase , _lowerCAmelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
SCREAMING_SNAKE_CASE_ = images.shape[0]
SCREAMING_SNAKE_CASE_ = images.reshape(_lowerCAmelCase , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
SCREAMING_SNAKE_CASE_ = list(_lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE_ = [to_channel_dimension_format(_lowerCAmelCase , _lowerCAmelCase ) for image in images]
SCREAMING_SNAKE_CASE_ = {'input_ids': images}
return BatchFeature(data=_lowerCAmelCase , tensor_type=_lowerCAmelCase ) | 31 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
# TODO Update this
__lowerCAmelCase = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : Any = 'esm'
def __init__( self : Optional[int] ,_UpperCAmelCase : Optional[Any]=None ,_UpperCAmelCase : Dict=None ,_UpperCAmelCase : Union[str, Any]=None ,_UpperCAmelCase : List[Any]=768 ,_UpperCAmelCase : Union[str, Any]=12 ,_UpperCAmelCase : List[str]=12 ,_UpperCAmelCase : Tuple=3072 ,_UpperCAmelCase : Dict=0.1 ,_UpperCAmelCase : Tuple=0.1 ,_UpperCAmelCase : List[str]=1026 ,_UpperCAmelCase : List[str]=0.02 ,_UpperCAmelCase : Optional[int]=1E-12 ,_UpperCAmelCase : List[str]="absolute" ,_UpperCAmelCase : Tuple=True ,_UpperCAmelCase : Tuple=None ,_UpperCAmelCase : List[Any]=False ,_UpperCAmelCase : int=False ,_UpperCAmelCase : int=None ,_UpperCAmelCase : Union[str, Any]=None ,**_UpperCAmelCase : List[Any] ,):
super().__init__(pad_token_id=_UpperCAmelCase ,mask_token_id=_UpperCAmelCase ,**_UpperCAmelCase )
_a : Optional[Any] = vocab_size
_a : Union[str, Any] = hidden_size
_a : Dict = num_hidden_layers
_a : int = num_attention_heads
_a : Dict = intermediate_size
_a : List[Any] = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Optional[Any] = max_position_embeddings
_a : Optional[int] = initializer_range
_a : List[Any] = layer_norm_eps
_a : int = position_embedding_type
_a : Optional[int] = use_cache
_a : Any = emb_layer_norm_before
_a : List[str] = token_dropout
_a : List[str] = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('No esmfold_config supplied for folding model, using default values.' )
_a : Dict = EsmFoldConfig()
elif isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
_a : Dict = EsmFoldConfig(**_UpperCAmelCase )
_a : Optional[int] = esmfold_config
if vocab_list is None:
logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!' )
_a : Optional[int] = get_default_vocab_list()
else:
_a : Optional[int] = vocab_list
else:
_a : Optional[Any] = None
_a : Union[str, Any] = None
if self.esmfold_config is not None and getattr(self.esmfold_config ,'use_esm_attn_map' ,_UpperCAmelCase ):
raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!' )
def __lowercase ( self : Any ):
_a : str = super().to_dict()
if isinstance(self.esmfold_config ,_UpperCAmelCase ):
_a : List[str] = self.esmfold_config.to_dict()
return output
@dataclass
class __magic_name__ :
lowerCAmelCase : str = None
lowerCAmelCase : bool = True
lowerCAmelCase : bool = False
lowerCAmelCase : bool = False
lowerCAmelCase : bool = False
lowerCAmelCase : float = 0
lowerCAmelCase : bool = True
lowerCAmelCase : bool = False
lowerCAmelCase : int = 1_2_8
lowerCAmelCase : "TrunkConfig" = None
def __lowercase ( self : List[str] ):
if self.trunk is None:
_a : Dict = TrunkConfig()
elif isinstance(self.trunk ,_UpperCAmelCase ):
_a : str = TrunkConfig(**self.trunk )
def __lowercase ( self : List[Any] ):
_a : List[str] = asdict(self )
_a : List[str] = self.trunk.to_dict()
return output
@dataclass
class __magic_name__ :
lowerCAmelCase : int = 4_8
lowerCAmelCase : int = 1_0_2_4
lowerCAmelCase : int = 1_2_8
lowerCAmelCase : int = 3_2
lowerCAmelCase : int = 3_2
lowerCAmelCase : int = 3_2
lowerCAmelCase : float = 0
lowerCAmelCase : float = 0
lowerCAmelCase : bool = False
lowerCAmelCase : int = 4
lowerCAmelCase : Optional[int] = 1_2_8
lowerCAmelCase : "StructureModuleConfig" = None
def __lowercase ( self : str ):
if self.structure_module is None:
_a : Tuple = StructureModuleConfig()
elif isinstance(self.structure_module ,_UpperCAmelCase ):
_a : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
F""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
F""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
_a : Optional[int] = self.sequence_state_dim // self.sequence_head_width
_a : int = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
F""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
F""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(F"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def __lowercase ( self : Optional[int] ):
_a : Optional[Any] = asdict(self )
_a : Optional[Any] = self.structure_module.to_dict()
return output
@dataclass
class __magic_name__ :
lowerCAmelCase : int = 3_8_4
lowerCAmelCase : int = 1_2_8
lowerCAmelCase : int = 1_6
lowerCAmelCase : int = 1_2_8
lowerCAmelCase : int = 1_2
lowerCAmelCase : int = 4
lowerCAmelCase : int = 8
lowerCAmelCase : float = 0.1
lowerCAmelCase : int = 8
lowerCAmelCase : int = 1
lowerCAmelCase : int = 2
lowerCAmelCase : int = 7
lowerCAmelCase : int = 1_0
lowerCAmelCase : float = 1e-8
lowerCAmelCase : float = 1e5
def __lowercase ( self : str ):
return asdict(self )
def __lowerCamelCase ( ) -> Optional[int]:
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 358 | 0 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def A ( lowercase ) -> List[str]:
'''simple docstring'''
if is_torch_version('<' , '2.0.0' ) or not hasattr(lowercase , '_dynamo' ):
return False
return isinstance(lowercase , torch._dynamo.eval_frame.OptimizedModule )
def A ( lowercase , lowercase = True ) -> Any:
'''simple docstring'''
UpperCamelCase = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
UpperCamelCase = is_compiled_module(lowercase )
if is_compiled:
UpperCamelCase = model
UpperCamelCase = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase , lowercase ):
UpperCamelCase = model.module
if not keep_fpaa_wrapper:
UpperCamelCase = getattr(lowercase , 'forward' )
UpperCamelCase = model.__dict__.pop('_original_forward' , lowercase )
if original_forward is not None:
while hasattr(lowercase , '__wrapped__' ):
UpperCamelCase = forward.__wrapped__
if forward == original_forward:
break
UpperCamelCase = forward
if getattr(lowercase , '_converted_to_transformer_engine' , lowercase ):
convert_model(lowercase , to_transformer_engine=lowercase )
if is_compiled:
UpperCamelCase = model
UpperCamelCase = compiled_model
return model
def A ( ) -> Optional[int]:
'''simple docstring'''
PartialState().wait_for_everyone()
def A ( lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase , lowercase )
elif PartialState().local_process_index == 0:
torch.save(lowercase , lowercase )
@contextmanager
def A ( **lowercase ) -> Optional[Any]:
'''simple docstring'''
for key, value in kwargs.items():
UpperCamelCase = str(lowercase )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def A ( lowercase ) -> str:
'''simple docstring'''
if not hasattr(lowercase , '__qualname__' ) and not hasattr(lowercase , '__name__' ):
UpperCamelCase = getattr(lowercase , '__class__' , lowercase )
if hasattr(lowercase , '__qualname__' ):
return obj.__qualname__
if hasattr(lowercase , '__name__' ):
return obj.__name__
return str(lowercase )
def A ( lowercase , lowercase ) -> int:
'''simple docstring'''
for key, value in source.items():
if isinstance(lowercase , lowercase ):
UpperCamelCase = destination.setdefault(lowercase , {} )
merge_dicts(lowercase , lowercase )
else:
UpperCamelCase = value
return destination
def A ( lowercase = None ) -> bool:
'''simple docstring'''
if port is None:
UpperCamelCase = 29_500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 3 |
import re
def A ( lowercase ) -> str:
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase ) ) != len(lowercase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 3 | 1 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__SCREAMING_SNAKE_CASE : Optional[int] = {"""UserAgent""": UserAgent().random}
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> dict:
"""simple docstring"""
_UpperCAmelCase : List[Any] = script.contents[0]
_UpperCAmelCase : List[Any] = json.loads(data[data.find("{\"config\"" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , A : List[Any] ):
_UpperCAmelCase : int = F"""https://www.instagram.com/{username}/"""
_UpperCAmelCase : Tuple = self.get_json()
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = requests.get(self.url , headers=A ).text
_UpperCAmelCase : Union[str, Any] = BeautifulSoup(A , "html.parser" ).find_all("script" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Tuple ):
return F"""{self.__class__.__name__}('{self.username}')"""
def __str__( self : int ):
return F"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def _A ( self : List[str] ):
return self.user_data["username"]
@property
def _A ( self : Dict ):
return self.user_data["full_name"]
@property
def _A ( self : Tuple ):
return self.user_data["biography"]
@property
def _A ( self : Tuple ):
return self.user_data["business_email"]
@property
def _A ( self : str ):
return self.user_data["external_url"]
@property
def _A ( self : Union[str, Any] ):
return self.user_data["edge_followed_by"]["count"]
@property
def _A ( self : List[Any] ):
return self.user_data["edge_follow"]["count"]
@property
def _A ( self : int ):
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def _A ( self : Optional[Any] ):
return self.user_data["profile_pic_url_hd"]
@property
def _A ( self : str ):
return self.user_data["is_verified"]
@property
def _A ( self : Tuple ):
return self.user_data["is_private"]
def UpperCamelCase_ ( _UpperCAmelCase : str = "github" ) -> None:
"""simple docstring"""
import os
if os.environ.get("CI" ):
return # test failing on GitHub Actions
_UpperCAmelCase : List[Any] = InstagramUser(_UpperCAmelCase )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , _UpperCAmelCase )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120_000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("https://instagram." )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE : Any = InstagramUser("""github""")
print(instagram_user)
print(F'{instagram_user.number_of_posts = }')
print(F'{instagram_user.number_of_followers = }')
print(F'{instagram_user.number_of_followings = }')
print(F'{instagram_user.email = }')
print(F'{instagram_user.website = }')
print(F'{instagram_user.profile_picture_url = }')
print(F'{instagram_user.is_verified = }')
print(F'{instagram_user.is_private = }')
| 244 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return number | (1 << position)
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return number & ~(1 << position)
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return number ^ (1 << position)
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
"""simple docstring"""
return ((number >> position) & 1) == 1
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 244 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Union[str, Any] = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_config.json',
'merges_file': 'merges.txt',
}
a_ : List[str] = {
'vocab_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
),
},
'tokenizer_config_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
),
},
'merges_file': {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
),
},
}
a_ : int = '</w>'
a_ : List[Any] = '@@ '
def __a ( __UpperCAmelCase ):
a__ = set()
a__ = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
a__ = char
return pairs
# Speech2Text2 has no max input length
a_ : Dict = {'facebook/s2t-wav2vec2-large-en-de': 10_24}
class __UpperCamelCase ( snake_case__ ):
_lowercase : int = VOCAB_FILES_NAMES
_lowercase : str = PRETRAINED_VOCAB_FILES_MAP
_lowercase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowercase : Optional[Any] = ["""input_ids""", """attention_mask"""]
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="<s>" , SCREAMING_SNAKE_CASE="<pad>" , SCREAMING_SNAKE_CASE="</s>" , SCREAMING_SNAKE_CASE="<unk>" , SCREAMING_SNAKE_CASE=False , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , ) -> List[str]:
super().__init__(
unk_token=UpperCAmelCase_ , bos_token=UpperCAmelCase_ , eos_token=UpperCAmelCase_ , pad_token=UpperCAmelCase_ , do_lower_case=UpperCAmelCase_ , **UpperCAmelCase_ , )
a__ = do_lower_case
with open(UpperCAmelCase_ , encoding='''utf-8''' ) as vocab_handle:
a__ = json.load(UpperCAmelCase_ )
a__ = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding." )
a__ = None
a__ = None
else:
with open(UpperCAmelCase_ , encoding='''utf-8''' ) as merges_handle:
a__ = merges_handle.read().split('''\n''' )[:-1]
a__ = [tuple(merge.split()[:2] ) for merge in merges]
a__ = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
a__ = {}
@property
def _UpperCAmelCase ( self ) -> int:
return len(self.decoder )
def _UpperCAmelCase ( self ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[Any]:
a__ = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
a__ = get_pairs(UpperCAmelCase_ )
if not pairs:
return token
while True:
a__ = min(UpperCAmelCase_ , key=lambda SCREAMING_SNAKE_CASE : self.bpe_ranks.get(UpperCAmelCase_ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
a__ , a__ = bigram
a__ = []
a__ = 0
while i < len(UpperCAmelCase_ ):
try:
a__ = word.index(UpperCAmelCase_ , UpperCAmelCase_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
a__ = j
if word[i] == first and i < len(UpperCAmelCase_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
a__ = tuple(UpperCAmelCase_ )
a__ = new_word
if len(UpperCAmelCase_ ) == 1:
break
else:
a__ = get_pairs(UpperCAmelCase_ )
a__ = ''' '''.join(UpperCAmelCase_ )
if word == "\n " + BPE_TOKEN_MERGES:
a__ = '''\n''' + BPE_TOKEN_MERGES
if word.endswith(UpperCAmelCase_ ):
a__ = word.replace(UpperCAmelCase_ , '''''' )
a__ = word.replace(''' ''' , UpperCAmelCase_ )
a__ = word
return word
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> List[str]:
if self.bpe_ranks is None:
raise ValueError(
'''This tokenizer was instantiated without a `merges.txt` file, so'''
''' that it can only be used for decoding, not for encoding.'''
'''Make sure to provide `merges.txt` file at instantiation to enable '''
'''encoding.''' )
if self.do_lower_case:
a__ = text.lower()
a__ = text.split()
a__ = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(UpperCAmelCase_ ).split(''' ''' ) ) )
return split_tokens
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> int:
return self.encoder.get(UpperCAmelCase_ , self.encoder.get(self.unk_token ) )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> str:
a__ = self.decoder.get(UpperCAmelCase_ , self.unk_token )
return result
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> str:
a__ = ''' '''.join(UpperCAmelCase_ )
# make sure @@ tokens are concatenated
a__ = ''''''.join(string.split(UpperCAmelCase_ ) )
return string
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
if not os.path.isdir(UpperCAmelCase_ ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
a__ = os.path.join(
UpperCAmelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
a__ = os.path.join(
UpperCAmelCase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(UpperCAmelCase_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=UpperCAmelCase_ , ensure_ascii=UpperCAmelCase_ ) + '''\n''' )
a__ = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(UpperCAmelCase_ , '''w''' , encoding='''utf-8''' ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda SCREAMING_SNAKE_CASE : kv[1] ):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."
''' Please check that the tokenizer is not corrupted!''' )
a__ = token_index
writer.write(''' '''.join(UpperCAmelCase_ ) + '''\n''' )
index += 1
return (vocab_file, merges_file)
| 702 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __UpperCamelCase :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE ) -> Dict:
a__ = data
a__ = None
class __UpperCamelCase :
"""simple docstring"""
def __init__( self ) -> Optional[int]:
a__ = None
a__ = None
def __iter__( self ) -> Iterator[Any]:
a__ = self.head
while self.head:
yield node.data
a__ = node.next
if node == self.head:
break
def __len__( self ) -> int:
return sum(1 for _ in self )
def __repr__( self ) -> int:
return "->".join(str(SCREAMING_SNAKE_CASE ) for item in iter(self ) )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> None:
self.insert_nth(len(self ) , SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> None:
self.insert_nth(0 , SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> None:
if index < 0 or index > len(self ):
raise IndexError('''list index out of range.''' )
a__ = Node(SCREAMING_SNAKE_CASE )
if self.head is None:
a__ = new_node # first node points itself
a__ = a__ = new_node
elif index == 0: # insert at head
a__ = self.head
a__ = a__ = new_node
else:
a__ = self.head
for _ in range(index - 1 ):
a__ = temp.next
a__ = temp.next
a__ = new_node
if index == len(self ) - 1: # insert at tail
a__ = new_node
def _UpperCAmelCase ( self ) -> int:
return self.delete_nth(0 )
def _UpperCAmelCase ( self ) -> Any:
return self.delete_nth(len(self ) - 1 )
def _UpperCAmelCase ( self , SCREAMING_SNAKE_CASE = 0 ) -> Any:
if not 0 <= index < len(self ):
raise IndexError('''list index out of range.''' )
a__ = self.head
if self.head == self.tail: # just one node
a__ = a__ = None
elif index == 0: # delete head node
a__ = self.tail.next.next
a__ = self.head.next
else:
a__ = self.head
for _ in range(index - 1 ):
a__ = temp.next
a__ = temp.next
a__ = temp.next.next
if index == len(self ) - 1: # delete at tail
a__ = temp
return delete_node.data
def _UpperCAmelCase ( self ) -> bool:
return len(self ) == 0
def __a ( ):
a__ = CircularLinkedList()
assert len(__UpperCAmelCase ) == 0
assert circular_linked_list.is_empty() is True
assert str(__UpperCAmelCase ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(__UpperCAmelCase ) == i
circular_linked_list.insert_nth(__UpperCAmelCase , i + 1 )
assert str(__UpperCAmelCase ) == "->".join(str(__UpperCAmelCase ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(__UpperCAmelCase ) == "->".join(str(__UpperCAmelCase ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(__UpperCAmelCase ) == "->".join(str(__UpperCAmelCase ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(__UpperCAmelCase ) == "->".join(str(__UpperCAmelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(__UpperCAmelCase ) == "->".join(str(__UpperCAmelCase ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 148 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __A ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
_UpperCamelCase : Dict = None
_UpperCamelCase : int = BloomTokenizerFast
_UpperCamelCase : str = BloomTokenizerFast
_UpperCamelCase : str = True
_UpperCamelCase : Dict = False
_UpperCamelCase : Union[str, Any] = "tokenizer_file"
_UpperCamelCase : Optional[int] = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
def __A ( self ):
super().setUp()
_lowerCAmelCase : Union[str, Any] = BloomTokenizerFast.from_pretrained("""bigscience/tokenizer""" )
tokenizer.save_pretrained(self.tmpdirname )
def __A ( self , **a__ ):
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **a__ )
def __A ( self ):
_lowerCAmelCase : List[Any] = self.get_rust_tokenizer()
_lowerCAmelCase : str = ["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
_lowerCAmelCase : str = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
_lowerCAmelCase : List[str] = tokenizer.batch_encode_plus(a__ )["""input_ids"""]
self.assertListEqual(a__ , a__ )
_lowerCAmelCase : Union[str, Any] = tokenizer.batch_decode(a__ )
self.assertListEqual(a__ , a__ )
def __A ( self , a__=6 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_lowerCAmelCase : str = self.rust_tokenizer_class.from_pretrained(a__ , **a__ )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
_lowerCAmelCase : Optional[Any] = """This is a simple input"""
_lowerCAmelCase : int = ["""This is a simple input 1""", """This is a simple input 2"""]
_lowerCAmelCase : Any = ("""This is a simple input""", """This is a pair""")
_lowerCAmelCase : Any = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
try:
tokenizer_r.encode(a__ , max_length=a__ )
tokenizer_r.encode_plus(a__ , max_length=a__ )
tokenizer_r.batch_encode_plus(a__ , max_length=a__ )
tokenizer_r.encode(a__ , max_length=a__ )
tokenizer_r.batch_encode_plus(a__ , max_length=a__ )
except ValueError:
self.fail("""Bloom Tokenizer should be able to deal with padding""" )
_lowerCAmelCase : str = None # Hotfixing padding = None
self.assertRaises(a__ , tokenizer_r.encode , a__ , max_length=a__ , padding="""max_length""" )
# Simple input
self.assertRaises(a__ , tokenizer_r.encode_plus , a__ , max_length=a__ , padding="""max_length""" )
# Simple input
self.assertRaises(
a__ , tokenizer_r.batch_encode_plus , a__ , max_length=a__ , padding="""max_length""" , )
# Pair input
self.assertRaises(a__ , tokenizer_r.encode , a__ , max_length=a__ , padding="""max_length""" )
# Pair input
self.assertRaises(a__ , tokenizer_r.encode_plus , a__ , max_length=a__ , padding="""max_length""" )
# Pair input
self.assertRaises(
a__ , tokenizer_r.batch_encode_plus , a__ , max_length=a__ , padding="""max_length""" , )
def __A ( self ):
_lowerCAmelCase : Tuple = self.get_rust_tokenizer()
_lowerCAmelCase : Optional[int] = load_dataset("""xnli""" , """all_languages""" , split="""test""" , streaming=a__ )
_lowerCAmelCase : Any = next(iter(a__ ) )["""premise"""] # pick up one data
_lowerCAmelCase : List[Any] = list(sample_data.values() )
_lowerCAmelCase : Tuple = list(map(tokenizer.encode , a__ ) )
_lowerCAmelCase : Optional[int] = [tokenizer.decode(a__ , clean_up_tokenization_spaces=a__ ) for x in output_tokens]
self.assertListEqual(a__ , a__ )
def __A ( self ):
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 213 | """simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a : int = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class __A ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
_UpperCamelCase : str = GPTSwaTokenizer
_UpperCamelCase : Tuple = False
_UpperCamelCase : Union[str, Any] = True
_UpperCamelCase : Union[str, Any] = False
def __A ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase : Any = GPTSwaTokenizer(a__ , eos_token="""<unk>""" , bos_token="""<unk>""" , pad_token="""<unk>""" )
tokenizer.save_pretrained(self.tmpdirname )
def __A ( self , a__ ):
_lowerCAmelCase : Optional[int] = """This is a test"""
_lowerCAmelCase : Optional[int] = """This is a test"""
return input_text, output_text
def __A ( self ):
_lowerCAmelCase : List[Any] = """<s>"""
_lowerCAmelCase : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__ ) , a__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__ ) , a__ )
def __A ( self ):
_lowerCAmelCase : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(a__ ) , 2000 )
def __A ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 2000 )
def __A ( self ):
_lowerCAmelCase : Any = GPTSwaTokenizer(a__ )
_lowerCAmelCase : Optional[int] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(a__ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a__ ) , [465, 287, 265, 631, 842] )
_lowerCAmelCase : Tuple = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
# fmt: off
self.assertListEqual(
a__ , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] , )
# fmt: on
_lowerCAmelCase : List[str] = tokenizer.convert_tokens_to_ids(a__ )
self.assertListEqual(
a__ , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] , )
_lowerCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(a__ )
# fmt: off
self.assertListEqual(
a__ , ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] )
# fmt: on
def __A ( self ):
_lowerCAmelCase : Optional[Any] = GPTSwaTokenizer(a__ )
_lowerCAmelCase : str = ["""This is a test""", """I was born in 92000, and this is falsé."""]
_lowerCAmelCase : List[Any] = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(a__ , a__ ):
self.assertListEqual(tokenizer.encode_fast(a__ ) , a__ )
# Test that decode_fast returns the input text
for text, token_ids in zip(a__ , a__ ):
self.assertEqual(tokenizer.decode_fast(a__ ) , a__ )
@slow
def __A ( self ):
_lowerCAmelCase : str = [
"""<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')""",
"""Hey there, how are you doing this fine day?""",
"""This is a text with a trailing spaces followed by a dot .""",
"""Häj sväjs lillebrör! =)""",
"""Det är inget fel på Mr. Cool""",
]
# fmt: off
_lowerCAmelCase : List[Any] = {"""input_ids""": [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=a__ , model_name="""AI-Sweden/gpt-sw3-126m""" , sequences=a__ , )
| 213 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : List[Any] = ["pixel_values"]
def __init__( self , lowercase__ = True , lowercase__ = None , lowercase__ = None , lowercase__ = PILImageResampling.BILINEAR , lowercase__ = True , lowercase__ = 1 / 255 , lowercase__ = True , lowercase__ = None , lowercase__ = None , **lowercase__ , ) -> None:
super().__init__(**lowercase__ )
SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'shortest_edge': 384}
SCREAMING_SNAKE_CASE : Union[str, Any] = get_size_dict(lowercase__ , default_to_square=lowercase__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = do_resize
SCREAMING_SNAKE_CASE : List[Any] = size
# Default value set here for backwards compatibility where the value in config is None
SCREAMING_SNAKE_CASE : List[Any] = crop_pct if crop_pct is not None else 224 / 256
SCREAMING_SNAKE_CASE : int = resample
SCREAMING_SNAKE_CASE : Tuple = do_rescale
SCREAMING_SNAKE_CASE : Any = rescale_factor
SCREAMING_SNAKE_CASE : Any = do_normalize
SCREAMING_SNAKE_CASE : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE : Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _UpperCamelCase ( self , lowercase__ , lowercase__ , lowercase__ , lowercase__ = PILImageResampling.BICUBIC , lowercase__ = None , **lowercase__ , ) -> np.ndarray:
SCREAMING_SNAKE_CASE : List[str] = get_size_dict(lowercase__ , default_to_square=lowercase__ )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
SCREAMING_SNAKE_CASE : str = size['shortest_edge']
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
SCREAMING_SNAKE_CASE : Tuple = int(shortest_edge / crop_pct )
SCREAMING_SNAKE_CASE : Optional[int] = get_resize_output_image_size(lowercase__ , size=lowercase__ , default_to_square=lowercase__ )
SCREAMING_SNAKE_CASE : int = resize(image=lowercase__ , size=lowercase__ , resample=lowercase__ , data_format=lowercase__ , **lowercase__ )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=lowercase__ , size=(shortest_edge, shortest_edge) , data_format=lowercase__ , **lowercase__ )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
lowercase__ , size=(shortest_edge, shortest_edge) , resample=lowercase__ , data_format=lowercase__ , **lowercase__ )
def _UpperCamelCase ( self , lowercase__ , lowercase__ , lowercase__ = None , **lowercase__ , ) -> int:
return rescale(lowercase__ , scale=lowercase__ , data_format=lowercase__ , **lowercase__ )
def _UpperCamelCase ( self , lowercase__ , lowercase__ , lowercase__ , lowercase__ = None , **lowercase__ , ) -> np.ndarray:
return normalize(lowercase__ , mean=lowercase__ , std=lowercase__ , data_format=lowercase__ , **lowercase__ )
def _UpperCamelCase ( self , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = ChannelDimension.FIRST , **lowercase__ , ) -> PIL.Image.Image:
SCREAMING_SNAKE_CASE : int = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE : List[Any] = crop_pct if crop_pct is not None else self.crop_pct
SCREAMING_SNAKE_CASE : Optional[Any] = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE : List[str] = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE : str = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE : str = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE : str = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE : List[str] = size if size is not None else self.size
SCREAMING_SNAKE_CASE : Dict = get_size_dict(lowercase__ , default_to_square=lowercase__ )
SCREAMING_SNAKE_CASE : str = make_list_of_images(lowercase__ )
if not valid_images(lowercase__ ):
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_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError('crop_pct must be specified if size < 384.' )
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.
SCREAMING_SNAKE_CASE : List[Any] = [to_numpy_array(lowercase__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE : Dict = [self.resize(image=lowercase__ , size=lowercase__ , crop_pct=lowercase__ , resample=lowercase__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE : Optional[int] = [self.rescale(image=lowercase__ , scale=lowercase__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE : str = [self.normalize(image=lowercase__ , mean=lowercase__ , std=lowercase__ ) for image in images]
SCREAMING_SNAKE_CASE : Tuple = [to_channel_dimension_format(lowercase__ , lowercase__ ) for image in images]
SCREAMING_SNAKE_CASE : str = {'pixel_values': images}
return BatchFeature(data=lowercase__ , tensor_type=lowercase__ )
| 179 | '''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
snake_case__ : int = CLIPTokenizer
snake_case__ : Union[str, Any] = CLIPTokenizerFast
snake_case__ : str = True
snake_case__ : Optional[int] = {}
snake_case__ : int = False
def _UpperCamelCase ( self ) -> Tuple:
super().setUp()
# fmt: off
SCREAMING_SNAKE_CASE : Optional[int] = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
SCREAMING_SNAKE_CASE : int = dict(zip(lowercase__ , range(len(lowercase__ ) ) ) )
SCREAMING_SNAKE_CASE : Dict = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
SCREAMING_SNAKE_CASE : List[str] = {'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowercase__ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(lowercase__ ) )
def _UpperCamelCase ( self , **lowercase__ ) -> Dict:
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowercase__ )
def _UpperCamelCase ( self , **lowercase__ ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowercase__ )
def _UpperCamelCase ( self , lowercase__ ) -> List[Any]:
SCREAMING_SNAKE_CASE : Optional[Any] = 'lower newer'
SCREAMING_SNAKE_CASE : Dict = 'lower newer'
return input_text, output_text
def _UpperCamelCase ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE : str = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE : Optional[int] = 'lower newer'
SCREAMING_SNAKE_CASE : List[Any] = ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer.tokenize(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
SCREAMING_SNAKE_CASE : Any = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE : int = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__ ) , lowercase__ )
@require_ftfy
def _UpperCamelCase ( self ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
SCREAMING_SNAKE_CASE : Dict = self.tokenizer_class.from_pretrained(lowercase__ , **lowercase__ )
SCREAMING_SNAKE_CASE : List[Any] = self.rust_tokenizer_class.from_pretrained(lowercase__ , **lowercase__ )
SCREAMING_SNAKE_CASE : Tuple = 'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
SCREAMING_SNAKE_CASE : List[str] = tokenizer_s.tokenize(lowercase__ )
SCREAMING_SNAKE_CASE : List[str] = tokenizer_r.tokenize(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
SCREAMING_SNAKE_CASE : Tuple = 'xa\u0303y' + ' ' + 'x\xe3y'
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_s.tokenize(lowercase__ )
SCREAMING_SNAKE_CASE : Dict = tokenizer_r.tokenize(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
# Test that the tokenization is identical on unicode of space type
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'\u0009', # (horizontal tab, '\t')
'\u000B', # (vertical tab)
'\u000C', # (form feed)
'\u0020', # (space, ' ')
'\u200E', # (left-to-right mark):w
'\u200F', # (right-to-left mark)
]
for unicode_seq in spaces_unicodes:
SCREAMING_SNAKE_CASE : Tuple = tokenizer_s.tokenize(lowercase__ )
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer_r.tokenize(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
# Test that the tokenization is identical on unicode of line break type
SCREAMING_SNAKE_CASE : int = [
'\u000A', # (line feed, '\n')
'\r\n', # (carriage return and line feed, '\r\n')
'\u000D', # (carriage return, '\r')
'\r', # (carriage return, '\r')
'\u000D', # (carriage return, '\r')
'\u2028', # (line separator)
'\u2029', # (paragraph separator)
# "\u0085", # (next line)
]
# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
# space (and thus into an empty list).
for unicode_seq in line_break_unicodes:
SCREAMING_SNAKE_CASE : List[Any] = tokenizer_s.tokenize(lowercase__ )
SCREAMING_SNAKE_CASE : int = tokenizer_r.tokenize(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
def _UpperCamelCase ( self ) -> int:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
SCREAMING_SNAKE_CASE : str = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
SCREAMING_SNAKE_CASE : Union[str, Any] = F"""{text_of_1_token} {text_of_1_token}"""
SCREAMING_SNAKE_CASE : List[str] = self.rust_tokenizer_class.from_pretrained(
lowercase__ , use_fast=lowercase__ , )
SCREAMING_SNAKE_CASE : Tuple = tokenizer_r(lowercase__ , return_offsets_mapping=lowercase__ , add_special_tokens=lowercase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowercase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowercase__ ) + 1, len(lowercase__ ) + 1 + len(lowercase__ )) , )
SCREAMING_SNAKE_CASE : Optional[int] = F""" {text}"""
SCREAMING_SNAKE_CASE : Any = self.rust_tokenizer_class.from_pretrained(
lowercase__ , use_fast=lowercase__ , )
SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer_r(lowercase__ , return_offsets_mapping=lowercase__ , add_special_tokens=lowercase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowercase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowercase__ ) + 1, 1 + len(lowercase__ ) + 1 + len(lowercase__ )) , )
def _UpperCamelCase ( self ) -> int:
# Test related to the breaking change introduced in transformers v4.17.0
# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
with self.assertRaises(lowercase__ ) as context:
self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer' )
self.assertTrue(
context.exception.args[0].startswith(
'The `backend_tokenizer` provided does not match the expected format.' ) )
@require_ftfy
def _UpperCamelCase ( self ) -> Union[str, Any]:
super().test_tokenization_python_rust_equals()
def _UpperCamelCase ( self ) -> int:
# CLIP always lower cases letters
pass
| 179 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, 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 MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __snake_case :
def __init__( self ,a_ ,a_=2 ,a_=True ,a_=False ,a_=10 ,a_=3 ,a_=32 * 4 ,a_=32 * 6 ,a_=4 ,a_=32 ,):
"""simple docstring"""
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_auxiliary_loss
lowerCAmelCase__ = num_queries
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = min_size
lowerCAmelCase__ = max_size
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case_ )
lowerCAmelCase__ = torch.ones([self.batch_size, self.min_size, self.max_size] ,device=snake_case_ )
lowerCAmelCase__ = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,device=snake_case_ ) > 0.5
).float()
lowerCAmelCase__ = (torch.rand((self.batch_size, self.num_labels) ,device=snake_case_ ) > 0.5).long()
lowerCAmelCase__ = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] ,) ,decoder_config=DetrConfig(
decoder_ffn_dim=128 ,num_queries=self.num_queries ,decoder_attention_heads=2 ,d_model=self.mask_feature_size ,) ,mask_feature_size=self.mask_feature_size ,fpn_feature_size=self.mask_feature_size ,num_channels=self.num_channels ,num_labels=self.num_labels ,)
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = output.encoder_hidden_states
lowerCAmelCase__ = output.pixel_decoder_hidden_states
lowerCAmelCase__ = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case_ ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case_ ) ,config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ,a_ ,a_=False ):
"""simple docstring"""
with torch.no_grad():
lowerCAmelCase__ = MaskFormerModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
lowerCAmelCase__ = model(pixel_values=snake_case_ ,pixel_mask=snake_case_ )
lowerCAmelCase__ = model(snake_case_ ,output_hidden_states=snake_case_ )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape ,(self.batch_size, self.num_queries, self.mask_feature_size) ,)
# 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(snake_case_ ,snake_case_ )
def SCREAMING_SNAKE_CASE_ ( self ,a_ ,a_ ,a_ ,a_ ,a_ ):
"""simple docstring"""
lowerCAmelCase__ = MaskFormerForInstanceSegmentation(config=snake_case_ )
model.to(snake_case_ )
model.eval()
def comm_check_on_output(a_ ):
# 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():
lowerCAmelCase__ = model(pixel_values=snake_case_ ,pixel_mask=snake_case_ )
lowerCAmelCase__ = model(snake_case_ )
comm_check_on_output(snake_case_ )
lowerCAmelCase__ = model(
pixel_values=snake_case_ ,pixel_mask=snake_case_ ,mask_labels=snake_case_ ,class_labels=snake_case_ )
comm_check_on_output(snake_case_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape ,torch.Size([1] ) )
@require_torch
class __snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
SCREAMING_SNAKE_CASE__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
SCREAMING_SNAKE_CASE__ = (
{'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = MaskFormerModelTester(self )
lowerCAmelCase__ = ConfigTester(self ,config_class=snake_case_ ,has_text_modality=snake_case_ )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ ,**snake_case_ ,output_hidden_states=snake_case_ )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case_ )
@unittest.skip(reason='MaskFormer does not use inputs_embeds' )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method' )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MaskFormer is not a generative model' )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='MaskFormer does not use token embeddings' )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case_ )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] ,snake_case_ )
@slow
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
lowerCAmelCase__ = MaskFormerModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = (self.model_tester.min_size,) * 2
lowerCAmelCase__ = {
'''pixel_values''': torch.randn((2, 3, *size) ,device=snake_case_ ),
'''mask_labels''': torch.randn((2, 10, *size) ,device=snake_case_ ),
'''class_labels''': torch.zeros(2 ,10 ,device=snake_case_ ).long(),
}
lowerCAmelCase__ = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case_ )
lowerCAmelCase__ = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case_ ,**snake_case_ ,output_hidden_states=snake_case_ )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(snake_case_ ).to(snake_case_ )
lowerCAmelCase__ = model(**snake_case_ ,output_attentions=snake_case_ )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
lowerCAmelCase__ = self.all_model_classes[1]
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
lowerCAmelCase__ = model(snake_case_ ,mask_labels=snake_case_ ,class_labels=snake_case_ ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = self.all_model_classes[1]
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
lowerCAmelCase__ = model(snake_case_ ,mask_labels=snake_case_ ,class_labels=snake_case_ )
lowerCAmelCase__ = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
lowerCAmelCase__ = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
lowerCAmelCase__ = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
lowerCAmelCase__ = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=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 )
_lowerCAmelCase : Dict = 1e-4
def UpperCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class __snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco' ).to(snake_case_ )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(snake_case_ ,return_tensors='pt' ).to(snake_case_ )
lowerCAmelCase__ = 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(snake_case_ ,(1, 3, 800, 1088) )
with torch.no_grad():
lowerCAmelCase__ = model(**snake_case_ )
lowerCAmelCase__ = torch.tensor(
[[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] ,snake_case_ ,atol=snake_case_ ) )
lowerCAmelCase__ = torch.tensor(
[[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,snake_case_ ,atol=snake_case_ ) )
lowerCAmelCase__ = torch.tensor(
[[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]] ).to(snake_case_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,snake_case_ ,atol=snake_case_ ) )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco' )
.to(snake_case_ )
.eval()
)
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(snake_case_ ,return_tensors='pt' ).to(snake_case_ )
lowerCAmelCase__ = 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(snake_case_ ,(1, 3, 800, 1088) )
with torch.no_grad():
lowerCAmelCase__ = model(**snake_case_ )
# masks_queries_logits
lowerCAmelCase__ = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,)
lowerCAmelCase__ = [
[-1.3737124, -1.7724937, -1.9364233],
[-1.5977281, -1.9867939, -2.1523695],
[-1.5795398, -1.9269832, -2.093942],
]
lowerCAmelCase__ = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,snake_case_ ,atol=snake_case_ ) )
# class_queries_logits
lowerCAmelCase__ = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase__ = torch.tensor(
[
[1.6_5_1_2e0_0, -5.2_5_7_2e0_0, -3.3_5_1_9e0_0],
[3.6_1_6_9e-0_2, -5.9_0_2_5e0_0, -2.9_3_1_3e0_0],
[1.0_7_6_6e-0_4, -7.7_6_3_0e0_0, -5.1_2_6_3e0_0],
] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,snake_case_ ,atol=snake_case_ ) )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff' )
.to(snake_case_ )
.eval()
)
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(snake_case_ ,return_tensors='pt' ).to(snake_case_ )
lowerCAmelCase__ = 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(snake_case_ ,(1, 3, 800, 1088) )
with torch.no_grad():
lowerCAmelCase__ = model(**snake_case_ )
# masks_queries_logits
lowerCAmelCase__ = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape ,(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,)
lowerCAmelCase__ = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
lowerCAmelCase__ = torch.tensor(snake_case_ ).to(snake_case_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,snake_case_ ,atol=snake_case_ ) )
# class_queries_logits
lowerCAmelCase__ = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape ,(1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
lowerCAmelCase__ = torch.tensor(
[[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,snake_case_ ,atol=snake_case_ ) )
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
lowerCAmelCase__ = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco' )
.to(snake_case_ )
.eval()
)
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = 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' ,)
lowerCAmelCase__ = inputs['''pixel_values'''].to(snake_case_ )
lowerCAmelCase__ = [el.to(snake_case_ ) for el in inputs['''mask_labels''']]
lowerCAmelCase__ = [el.to(snake_case_ ) for el in inputs['''class_labels''']]
with torch.no_grad():
lowerCAmelCase__ = model(**snake_case_ )
self.assertTrue(outputs.loss is not None )
| 193 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class _SCREAMING_SNAKE_CASE ( snake_case ):
lowerCamelCase_ = 'markuplm'
def __init__( self : List[Any] , snake_case_ : List[str]=3_0522 , snake_case_ : str=768 , snake_case_ : str=12 , snake_case_ : Optional[Any]=12 , snake_case_ : Any=3072 , snake_case_ : Dict="gelu" , snake_case_ : Dict=0.1 , snake_case_ : Optional[int]=0.1 , snake_case_ : int=512 , snake_case_ : Optional[Any]=2 , snake_case_ : int=0.02 , snake_case_ : Optional[Any]=1E-12 , snake_case_ : Dict=0 , snake_case_ : Optional[int]=0 , snake_case_ : Union[str, Any]=2 , snake_case_ : List[Any]=256 , snake_case_ : Union[str, Any]=1024 , snake_case_ : Optional[Any]=216 , snake_case_ : Optional[Any]=1001 , snake_case_ : Tuple=32 , snake_case_ : str=50 , snake_case_ : int="absolute" , snake_case_ : List[Any]=True , snake_case_ : List[Any]=None , **snake_case_ : Optional[Any] , ):
"""simple docstring"""
super().__init__(
pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ , )
A : int = vocab_size
A : Dict = hidden_size
A : str = num_hidden_layers
A : List[Any] = num_attention_heads
A : int = hidden_act
A : List[Any] = intermediate_size
A : Optional[Any] = hidden_dropout_prob
A : Tuple = attention_probs_dropout_prob
A : str = max_position_embeddings
A : Dict = type_vocab_size
A : Optional[int] = initializer_range
A : Optional[Any] = layer_norm_eps
A : Any = position_embedding_type
A : List[Any] = use_cache
A : List[str] = classifier_dropout
# additional properties
A : Optional[Any] = max_depth
A : Tuple = max_xpath_tag_unit_embeddings
A : str = max_xpath_subs_unit_embeddings
A : Dict = tag_pad_id
A : Dict = subs_pad_id
A : List[str] = xpath_unit_hidden_size | 256 | 0 |
"""simple docstring"""
import os
import sys
import unittest
snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
snake_case = os.path.join(git_repo_path, 'src', 'diffusers')
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
def A ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = find_backend(' if not is_torch_available():' )
self.assertEqual(_UpperCAmelCase , 'torch' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
SCREAMING_SNAKE_CASE = find_backend(' if not (is_torch_available() and is_transformers_available()):' )
self.assertEqual(_UpperCAmelCase , 'torch_and_transformers' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
SCREAMING_SNAKE_CASE = find_backend(
' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):' )
self.assertEqual(_UpperCAmelCase , 'torch_and_transformers_and_onnx' )
def A ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('torch' , _UpperCAmelCase )
self.assertIn('torch_and_transformers' , _UpperCAmelCase )
self.assertIn('flax_and_transformers' , _UpperCAmelCase )
self.assertIn('torch_and_transformers_and_onnx' , _UpperCAmelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn('UNet2DModel' , objects['torch'] )
self.assertIn('FlaxUNet2DConditionModel' , objects['flax'] )
self.assertIn('StableDiffusionPipeline' , objects['torch_and_transformers'] )
self.assertIn('FlaxStableDiffusionPipeline' , objects['flax_and_transformers'] )
self.assertIn('LMSDiscreteScheduler' , objects['torch_and_scipy'] )
self.assertIn('OnnxStableDiffusionPipeline' , objects['torch_and_transformers_and_onnx'] )
def A ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE = create_dummy_object('CONSTANT' , '\'torch\'' )
self.assertEqual(_UpperCAmelCase , '\nCONSTANT = None\n' )
SCREAMING_SNAKE_CASE = create_dummy_object('function' , '\'torch\'' )
self.assertEqual(
_UpperCAmelCase , '\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n' )
SCREAMING_SNAKE_CASE = '\nclass FakeClass(metaclass=DummyObject):\n _backends = \'torch\'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, \'torch\')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, \'torch\')\n'
SCREAMING_SNAKE_CASE = create_dummy_object('FakeClass' , '\'torch\'' )
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def A ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE = '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n'
SCREAMING_SNAKE_CASE = create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
self.assertEqual(dummy_files['torch'] , _UpperCAmelCase )
| 714 |
"""simple docstring"""
import random
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = [], [], []
for element in data:
if element < pivot:
less.append(SCREAMING_SNAKE_CASE_ )
elif element > pivot:
greater.append(SCREAMING_SNAKE_CASE_ )
else:
equal.append(SCREAMING_SNAKE_CASE_ )
return less, equal, greater
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
# index = len(items) // 2 when trying to find the median
# (value of index when items is sorted)
# invalid input
if index >= len(SCREAMING_SNAKE_CASE_ ) or index < 0:
return None
SCREAMING_SNAKE_CASE = items[random.randint(0, len(SCREAMING_SNAKE_CASE_ ) - 1 )]
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = _partition(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
# must be in larger
else:
return quick_select(SCREAMING_SNAKE_CASE_, index - (m + count) )
| 406 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCAmelCase__( lowercase : Optional[int] , lowercase : Union[str, Any]=False ) -> List[Any]:
__snake_case : List[str] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
("module.cls_token", "vit.embeddings.cls_token"),
("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("module.pos_embed", "vit.embeddings.position_embeddings"),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("module.norm.weight", "layernorm.weight"),
("module.norm.bias", "layernorm.bias"),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__snake_case : Any = [(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def lowerCAmelCase__( lowercase : Optional[Any] , lowercase : Optional[Any] , lowercase : Optional[int]=False ) -> Dict:
for i in range(config.num_hidden_layers ):
if base_model:
__snake_case : Union[str, Any] = ""
else:
__snake_case : Optional[int] = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__snake_case : Any = state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight""" )
__snake_case : Optional[int] = state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__snake_case : List[str] = in_proj_weight[
: config.hidden_size, :
]
__snake_case : Dict = in_proj_bias[: config.hidden_size]
__snake_case : List[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__snake_case : int = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__snake_case : Any = in_proj_weight[
-config.hidden_size :, :
]
__snake_case : List[str] = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase__( lowercase : Union[str, Any] ) -> Any:
__snake_case : Dict = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(lowercase , lowercase )
def lowerCAmelCase__( lowercase : Union[str, Any] ) -> Tuple:
# projection head is used in the self-supervised pre-training in MSN,
# for downstream task it's not needed.
__snake_case : int = [
"module.fc.fc1.weight",
"module.fc.fc1.bias",
"module.fc.bn1.weight",
"module.fc.bn1.bias",
"module.fc.bn1.running_mean",
"module.fc.bn1.running_var",
"module.fc.bn1.num_batches_tracked",
"module.fc.fc2.weight",
"module.fc.fc2.bias",
"module.fc.bn2.weight",
"module.fc.bn2.bias",
"module.fc.bn2.running_mean",
"module.fc.bn2.running_var",
"module.fc.bn2.num_batches_tracked",
"module.fc.fc3.weight",
"module.fc.fc3.bias",
]
for k in ignore_keys:
state_dict.pop(lowercase , lowercase )
def lowerCAmelCase__( lowercase : Optional[Any] , lowercase : Tuple , lowercase : Tuple ) -> List[Any]:
__snake_case : int = dct.pop(lowercase )
__snake_case : Optional[Any] = val
def lowerCAmelCase__( lowercase : Optional[Any] , lowercase : str ) -> int:
__snake_case : Optional[int] = ViTMSNConfig()
__snake_case : Dict = 1000
__snake_case : Optional[Any] = "datasets/huggingface/label-files"
__snake_case : int = "imagenet-1k-id2label.json"
__snake_case : Dict = json.load(open(hf_hub_download(lowercase , lowercase ) , "r" ) )
__snake_case : List[str] = {int(lowercase ): v for k, v in idalabel.items()}
__snake_case : Tuple = idalabel
__snake_case : Any = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
__snake_case : int = 384
__snake_case : List[str] = 1536
__snake_case : Any = 6
elif "l16" in checkpoint_url:
__snake_case : Dict = 1024
__snake_case : Dict = 4096
__snake_case : Dict = 24
__snake_case : Optional[int] = 16
__snake_case : Optional[int] = 0.1
elif "b4" in checkpoint_url:
__snake_case : Dict = 4
elif "l7" in checkpoint_url:
__snake_case : Optional[int] = 7
__snake_case : Union[str, Any] = 1024
__snake_case : Dict = 4096
__snake_case : Any = 24
__snake_case : Any = 16
__snake_case : Tuple = 0.1
__snake_case : Dict = ViTMSNModel(lowercase )
__snake_case : List[str] = torch.hub.load_state_dict_from_url(lowercase , map_location="cpu" )["target_encoder"]
__snake_case : Optional[Any] = ViTImageProcessor(size=config.image_size )
remove_projection_head(lowercase )
__snake_case : Optional[Any] = create_rename_keys(lowercase , base_model=lowercase )
for src, dest in rename_keys:
rename_key(lowercase , lowercase , lowercase )
read_in_q_k_v(lowercase , lowercase , base_model=lowercase )
model.load_state_dict(lowercase )
model.eval()
__snake_case : Dict = "http://images.cocodataset.org/val2017/000000039769.jpg"
__snake_case : Dict = Image.open(requests.get(lowercase , stream=lowercase ).raw )
__snake_case : str = ViTImageProcessor(
size=config.image_size , image_mean=lowercase , image_std=lowercase )
__snake_case : str = image_processor(images=lowercase , return_tensors="pt" )
# forward pass
torch.manual_seed(2 )
__snake_case : int = model(**lowercase )
__snake_case : Any = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
__snake_case : Tuple = torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]] )
elif "b16" in checkpoint_url:
__snake_case : int = torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]] )
elif "l16" in checkpoint_url:
__snake_case : List[str] = torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]] )
elif "b4" in checkpoint_url:
__snake_case : Union[str, Any] = torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]] )
else:
__snake_case : Optional[Any] = torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , lowercase , atol=1E-4 )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
_UpperCamelCase = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 243 |
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 ( a , a , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Any =StableDiffusionDiffEditPipeline
UpperCAmelCase_ : Optional[int] =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
UpperCAmelCase_ : Any =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
UpperCAmelCase_ : Dict =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCAmelCase_ : Union[str, Any] =frozenset([] )
def UpperCAmelCase ( self ) -> List[str]:
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : 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=UpperCAmelCase , )
__snake_case : int = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCAmelCase , set_alpha_to_one=UpperCAmelCase , )
__snake_case : Optional[int] = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCAmelCase , set_alpha_to_zero=UpperCAmelCase , )
torch.manual_seed(0 )
__snake_case : List[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 )
__snake_case : List[Any] = 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 , )
__snake_case : int = CLIPTextModel(UpperCAmelCase )
__snake_case : str = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
__snake_case : List[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 UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase=0 ) -> Dict:
'''simple docstring'''
__snake_case : Optional[int] = floats_tensor((1, 16, 16) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
__snake_case : Optional[Any] = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
if str(UpperCAmelCase ).startswith("mps" ):
__snake_case : int = torch.manual_seed(UpperCAmelCase )
else:
__snake_case : str = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
__snake_case : Tuple = {
"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 UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase=0 ) -> Optional[Any]:
'''simple docstring'''
__snake_case : Union[str, Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
__snake_case : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__snake_case : Dict = Image.fromarray(np.uinta(UpperCAmelCase ) ).convert("RGB" )
if str(UpperCAmelCase ).startswith("mps" ):
__snake_case : str = torch.manual_seed(UpperCAmelCase )
else:
__snake_case : Dict = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
__snake_case : Union[str, 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 UpperCAmelCase ( self , UpperCAmelCase , UpperCAmelCase=0 ) -> Tuple:
'''simple docstring'''
__snake_case : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCAmelCase ) ).to(UpperCAmelCase )
__snake_case : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__snake_case : Union[str, Any] = Image.fromarray(np.uinta(UpperCAmelCase ) ).convert("RGB" )
if str(UpperCAmelCase ).startswith("mps" ):
__snake_case : Tuple = torch.manual_seed(UpperCAmelCase )
else:
__snake_case : Any = torch.Generator(device=UpperCAmelCase ).manual_seed(UpperCAmelCase )
__snake_case : Dict = {
"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 UpperCAmelCase ( self ) -> Optional[Any]:
'''simple docstring'''
if not hasattr(self.pipeline_class , "_optional_components" ):
return
__snake_case : str = self.get_dummy_components()
__snake_case : Union[str, Any] = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
__snake_case : Tuple = self.get_dummy_inputs(UpperCAmelCase )
__snake_case : List[str] = pipe(**UpperCAmelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCAmelCase )
__snake_case : Any = self.pipeline_class.from_pretrained(UpperCAmelCase )
pipe_loaded.to(UpperCAmelCase )
pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCAmelCase , UpperCAmelCase ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
__snake_case : int = self.get_dummy_inputs(UpperCAmelCase )
__snake_case : List[Any] = pipe_loaded(**UpperCAmelCase )[0]
__snake_case : Any = np.abs(output - output_loaded ).max()
self.assertLess(UpperCAmelCase , 1E-4 )
def UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : int = "cpu"
__snake_case : str = self.get_dummy_components()
__snake_case : Tuple = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__snake_case : Dict = self.get_dummy_mask_inputs(UpperCAmelCase )
__snake_case : List[str] = pipe.generate_mask(**UpperCAmelCase )
__snake_case : str = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
__snake_case : Union[str, Any] = np.array([0] * 9 )
__snake_case : Any = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
__snake_case : List[Any] = "cpu"
__snake_case : str = self.get_dummy_components()
__snake_case : str = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__snake_case : Optional[int] = self.get_dummy_inversion_inputs(UpperCAmelCase )
__snake_case : Dict = pipe.invert(**UpperCAmelCase ).images
__snake_case : List[str] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__snake_case : List[Any] = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
__snake_case : Dict = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1E-3 )
def UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
__snake_case : Any = "cpu"
__snake_case : Tuple = self.get_dummy_components()
__snake_case : str = {"beta_start": 0.00_085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
__snake_case : List[Any] = DPMSolverMultistepScheduler(**UpperCAmelCase )
__snake_case : Optional[int] = DPMSolverMultistepInverseScheduler(**UpperCAmelCase )
__snake_case : Tuple = self.pipeline_class(**UpperCAmelCase )
pipe.to(UpperCAmelCase )
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__snake_case : Optional[int] = self.get_dummy_inversion_inputs(UpperCAmelCase )
__snake_case : Any = pipe.invert(**UpperCAmelCase ).images
__snake_case : Optional[int] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__snake_case : List[Any] = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
__snake_case : Any = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase , 1E-3 )
@require_torch_gpu
@slow
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def UpperCAmelCase ( cls ) -> Optional[Any]:
'''simple docstring'''
__snake_case : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
__snake_case : Dict = raw_image.convert("RGB" ).resize((768, 768) )
__snake_case : int = raw_image
def UpperCAmelCase ( self ) -> str:
'''simple docstring'''
__snake_case : Optional[int] = torch.manual_seed(0 )
__snake_case : Dict = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=UpperCAmelCase , torch_dtype=torch.floataa )
__snake_case : Optional[int] = DDIMScheduler.from_config(pipe.scheduler.config )
__snake_case : List[Any] = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__snake_case : Dict = "a bowl of fruit"
__snake_case : Any = "a bowl of pears"
__snake_case : Optional[int] = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCAmelCase , target_prompt=UpperCAmelCase , generator=UpperCAmelCase , )
__snake_case : Optional[int] = pipe.invert(
prompt=UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCAmelCase ).latents
__snake_case : Union[str, Any] = pipe(
prompt=UpperCAmelCase , mask_image=UpperCAmelCase , image_latents=UpperCAmelCase , generator=UpperCAmelCase , negative_prompt=UpperCAmelCase , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
__snake_case : List[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
def UpperCAmelCase ( self ) -> int:
'''simple docstring'''
__snake_case : Union[str, Any] = torch.manual_seed(0 )
__snake_case : Optional[int] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=UpperCAmelCase , torch_dtype=torch.floataa )
__snake_case : List[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__snake_case : Tuple = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCAmelCase )
__snake_case : List[str] = "a bowl of fruit"
__snake_case : Optional[int] = "a bowl of pears"
__snake_case : Optional[Any] = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCAmelCase , target_prompt=UpperCAmelCase , generator=UpperCAmelCase , )
__snake_case : Any = pipe.invert(
prompt=UpperCAmelCase , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCAmelCase , num_inference_steps=25 , ).latents
__snake_case : Optional[int] = pipe(
prompt=UpperCAmelCase , mask_image=UpperCAmelCase , image_latents=UpperCAmelCase , generator=UpperCAmelCase , negative_prompt=UpperCAmelCase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="numpy" , ).images[0]
__snake_case : List[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
| 243 | 1 |
"""simple docstring"""
lowerCamelCase = """Input must be a string of 8 numbers plus letter"""
lowerCamelCase = """TRWAGMYFPDXBNJZSQVHLCKE"""
def a__ ( lowerCAmelCase__ ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = f"""Expected string as input, found {type(lowerCAmelCase__ ).__name__}"""
raise TypeError(lowerCAmelCase__ )
UpperCAmelCase_ = spanish_id.replace("-" , "" ).upper()
if len(lowerCAmelCase__ ) != 9:
raise ValueError(lowerCAmelCase__ )
try:
UpperCAmelCase_ = int(spanish_id_clean[0:8] )
UpperCAmelCase_ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(lowerCAmelCase__ ) from ex
if letter.isdigit():
raise ValueError(lowerCAmelCase__ )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715 |
"""simple docstring"""
lowerCamelCase = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": 4_186_800.00,
"electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
"britishthermalunit_it": 1_055.05_585,
"footpound": 1.355_818,
}
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
UpperCAmelCase_ = (
f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
f"""Valid values are: {', '.join(lowerCAmelCase__ )}"""
)
raise ValueError(lowerCAmelCase__ )
return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 14 | 0 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCamelCase( _a, unittest.TestCase ):
lowercase_ : Optional[Any] = FunnelTokenizer
lowercase_ : Union[str, Any] = FunnelTokenizerFast
lowercase_ : List[Any] = True
lowercase_ : Optional[int] = True
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
super().setUp()
_lowercase : Optional[int] = [
'<unk>',
'<cls>',
'<sep>',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
_lowercase : Tuple = 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 UpperCamelCase ( self, **lowerCamelCase) -> Tuple:
"""simple docstring"""
return FunnelTokenizer.from_pretrained(self.tmpdirname, **lowerCamelCase)
def UpperCamelCase ( self, **lowerCamelCase) -> Any:
"""simple docstring"""
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase) -> Optional[Any]:
"""simple docstring"""
_lowercase : int = 'UNwant\u00E9d,running'
_lowercase : str = 'unwanted, running'
return input_text, output_text
def UpperCamelCase ( self) -> Tuple:
"""simple docstring"""
_lowercase : List[str] = self.tokenizer_class(self.vocab_file)
_lowercase : int = tokenizer.tokenize('UNwant\u00E9d,running')
self.assertListEqual(lowerCamelCase, ['un', '##want', '##ed', ',', 'runn', '##ing'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase), [7, 4, 5, 10, 8, 9])
def UpperCamelCase ( self) -> Any:
"""simple docstring"""
_lowercase : str = self.get_tokenizers(do_lower_case=lowerCamelCase)
for tokenizer in tokenizers:
_lowercase : List[Any] = tokenizer('UNwant\u00E9d,running')
_lowercase : List[Any] = len(inputs['input_ids']) - 1
self.assertListEqual(inputs['token_type_ids'], [2] + [0] * sentence_len)
_lowercase : Union[str, Any] = tokenizer('UNwant\u00E9d,running', 'UNwant\u00E9d,running')
self.assertListEqual(inputs['token_type_ids'], [2] + [0] * sentence_len + [1] * sentence_len)
| 89 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class UpperCamelCase ( unittest.TestCase ):
def __A ( self ):
A__ = tempfile.mkdtemp()
A__ = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
A__ = 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] ) )
A__ = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
A__ = os.path.join(self.tmpdirname , UpperCAmelCase__ )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(UpperCAmelCase__ , UpperCAmelCase__ )
def __A ( self , **UpperCAmelCase__ ):
return BertTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def __A ( self , **UpperCAmelCase__ ):
return BertTokenizerFast.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def __A ( self , **UpperCAmelCase__ ):
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase__ )
def __A ( self ):
shutil.rmtree(self.tmpdirname )
def __A ( self ):
A__ = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A__ = [Image.fromarray(np.moveaxis(UpperCAmelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __A ( self ):
A__ = self.get_tokenizer()
A__ = self.get_rust_tokenizer()
A__ = self.get_image_processor()
A__ = AlignProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
processor_slow.save_pretrained(self.tmpdirname )
A__ = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCAmelCase__ )
A__ = AlignProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
processor_fast.save_pretrained(self.tmpdirname )
A__ = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , UpperCAmelCase__ )
self.assertIsInstance(processor_fast.tokenizer , UpperCAmelCase__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , UpperCAmelCase__ )
self.assertIsInstance(processor_fast.image_processor , UpperCAmelCase__ )
def __A ( self ):
A__ = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A__ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
A__ = self.get_image_processor(do_normalize=UpperCAmelCase__ , padding_value=1.0 )
A__ = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=UpperCAmelCase__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase__ )
def __A ( self ):
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = AlignProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
A__ = self.prepare_image_inputs()
A__ = image_processor(UpperCAmelCase__ , return_tensors="np" )
A__ = processor(images=UpperCAmelCase__ , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def __A ( self ):
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = AlignProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
A__ = "lower newer"
A__ = processor(text=UpperCAmelCase__ )
A__ = tokenizer(UpperCAmelCase__ , padding="max_length" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __A ( self ):
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = AlignProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
A__ = "lower newer"
A__ = self.prepare_image_inputs()
A__ = processor(text=UpperCAmelCase__ , images=UpperCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase__ ):
processor()
def __A ( self ):
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = AlignProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
A__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A__ = processor.batch_decode(UpperCAmelCase__ )
A__ = tokenizer.batch_decode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def __A ( self ):
A__ = self.get_image_processor()
A__ = self.get_tokenizer()
A__ = AlignProcessor(tokenizer=UpperCAmelCase__ , image_processor=UpperCAmelCase__ )
A__ = "lower newer"
A__ = self.prepare_image_inputs()
A__ = processor(text=UpperCAmelCase__ , images=UpperCAmelCase__ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 491 | 0 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
a : Dict = {'UserAgent': UserAgent().random}
def __magic_name__ ( __UpperCAmelCase ) -> dict:
'''simple docstring'''
snake_case_ = script.contents[0]
snake_case_ = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class a :
def __init__( self : Optional[Any] , lowercase_ : str ):
snake_case_ = F"https://www.instagram.com/{username}/"
snake_case_ = self.get_json()
def A_ ( self : Any ):
snake_case_ = requests.get(self.url , headers=lowercase_ ).text
snake_case_ = BeautifulSoup(lowercase_ , '''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Dict ):
return F"{self.__class__.__name__}('{self.username}')"
def __str__( self : int ):
return F"{self.fullname} ({self.username}) is {self.biography}"
@property
def A_ ( self : str ):
return self.user_data["username"]
@property
def A_ ( self : Optional[Any] ):
return self.user_data["full_name"]
@property
def A_ ( self : str ):
return self.user_data["biography"]
@property
def A_ ( self : Any ):
return self.user_data["business_email"]
@property
def A_ ( self : Optional[Any] ):
return self.user_data["external_url"]
@property
def A_ ( self : Any ):
return self.user_data["edge_followed_by"]["count"]
@property
def A_ ( self : Tuple ):
return self.user_data["edge_follow"]["count"]
@property
def A_ ( self : List[str] ):
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def A_ ( self : Optional[int] ):
return self.user_data["profile_pic_url_hd"]
@property
def A_ ( self : Dict ):
return self.user_data["is_verified"]
@property
def A_ ( self : Optional[Any] ):
return self.user_data["is_private"]
def __magic_name__ ( __UpperCAmelCase = "github" ) -> None:
'''simple docstring'''
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
snake_case_ = InstagramUser(__UpperCAmelCase )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data, __UpperCAmelCase )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 12_0000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
a : Any = InstagramUser('github')
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 593 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __magic_name__ ( *__UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase=True, __UpperCAmelCase=2 ) -> int:
'''simple docstring'''
from .. import __version__
snake_case_ = take_from
snake_case_ = ()
if not isinstance(args[0], __UpperCAmelCase ):
snake_case_ = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__UpperCAmelCase ).base_version ) >= version.parse(__UpperCAmelCase ):
raise ValueError(
F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
F" version {__version__} is >= {version_name}" )
snake_case_ = None
if isinstance(__UpperCAmelCase, __UpperCAmelCase ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__UpperCAmelCase ),)
snake_case_ = F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
elif hasattr(__UpperCAmelCase, __UpperCAmelCase ):
values += (getattr(__UpperCAmelCase, __UpperCAmelCase ),)
snake_case_ = F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
elif deprecated_kwargs is None:
snake_case_ = F"`{attribute}` is deprecated and will be removed in version {version_name}."
if warning is not None:
snake_case_ = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message, __UpperCAmelCase, stacklevel=__UpperCAmelCase )
if isinstance(__UpperCAmelCase, __UpperCAmelCase ) and len(__UpperCAmelCase ) > 0:
snake_case_ = inspect.getouterframes(inspect.currentframe() )[1]
snake_case_ = call_frame.filename
snake_case_ = call_frame.lineno
snake_case_ = call_frame.function
snake_case_ ,snake_case_ = next(iter(deprecated_kwargs.items() ) )
raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`" )
if len(__UpperCAmelCase ) == 0:
return
elif len(__UpperCAmelCase ) == 1:
return values[0]
return values
| 593 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Dict = logging.get_logger(__name__)
_lowercase : List[Any] = {
'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : List[str] = "data2vec-text"
def __init__( self : Dict , _lowercase : Union[str, Any]=3_05_22 , _lowercase : Tuple=7_68 , _lowercase : Union[str, Any]=12 , _lowercase : Dict=12 , _lowercase : int=30_72 , _lowercase : Any="gelu" , _lowercase : List[str]=0.1 , _lowercase : Optional[int]=0.1 , _lowercase : Dict=5_12 , _lowercase : Optional[Any]=2 , _lowercase : Optional[int]=0.02 , _lowercase : str=1E-12 , _lowercase : List[str]=1 , _lowercase : List[str]=0 , _lowercase : List[Any]=2 , _lowercase : List[str]="absolute" , _lowercase : str=True , _lowercase : Optional[int]=None , **_lowercase : int , ):
super().__init__(pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase )
__UpperCAmelCase = vocab_size
__UpperCAmelCase = hidden_size
__UpperCAmelCase = num_hidden_layers
__UpperCAmelCase = num_attention_heads
__UpperCAmelCase = hidden_act
__UpperCAmelCase = intermediate_size
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = max_position_embeddings
__UpperCAmelCase = type_vocab_size
__UpperCAmelCase = initializer_range
__UpperCAmelCase = layer_norm_eps
__UpperCAmelCase = position_embedding_type
__UpperCAmelCase = use_cache
__UpperCAmelCase = classifier_dropout
class _UpperCAmelCase ( _lowerCAmelCase ):
@property
def a ( self : Dict ):
if self.task == "multiple-choice":
__UpperCAmelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__UpperCAmelCase = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 49 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_lowercase : int = logging.get_logger(__name__)
_lowercase : Optional[int] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
_lowercase : str = {
'vocab_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
),
},
}
_lowercase : int = {
'yjernite/retribert-base-uncased': 5_12,
}
_lowercase : Any = {
'yjernite/retribert-base-uncased': {'do_lower_case': True},
}
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : str = VOCAB_FILES_NAMES
a__ : Dict = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : str = PRETRAINED_INIT_CONFIGURATION
a__ : Optional[Any] = RetriBertTokenizer
a__ : List[Any] = ["input_ids", "attention_mask"]
def __init__( self : List[str] , _lowercase : str=None , _lowercase : Any=None , _lowercase : Tuple=True , _lowercase : Optional[Any]="[UNK]" , _lowercase : int="[SEP]" , _lowercase : List[str]="[PAD]" , _lowercase : Union[str, Any]="[CLS]" , _lowercase : Any="[MASK]" , _lowercase : Optional[Any]=True , _lowercase : List[Any]=None , **_lowercase : str , ):
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
__UpperCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , _lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' , _lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , _lowercase ) != tokenize_chinese_chars
):
__UpperCAmelCase = getattr(_lowercase , normalizer_state.pop('''type''' ) )
__UpperCAmelCase = do_lower_case
__UpperCAmelCase = strip_accents
__UpperCAmelCase = tokenize_chinese_chars
__UpperCAmelCase = normalizer_class(**_lowercase )
__UpperCAmelCase = do_lower_case
def a ( self : List[Any] , _lowercase : Dict , _lowercase : Union[str, Any]=None ):
__UpperCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def a ( self : List[str] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ):
__UpperCAmelCase = [self.sep_token_id]
__UpperCAmelCase = [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 : Union[str, Any] , _lowercase : str , _lowercase : Optional[str] = None ):
__UpperCAmelCase = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 49 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = {
"EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class _lowerCamelCase( _a ):
lowercase_ : Optional[int] = """gpt_neo"""
lowercase_ : List[str] = ["""past_key_values"""]
lowercase_ : int = {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self, lowerCamelCase=5_02_57, lowerCamelCase=20_48, lowerCamelCase=20_48, lowerCamelCase=24, lowerCamelCase=[[["global", "local"], 12]], lowerCamelCase=16, lowerCamelCase=None, lowerCamelCase=2_56, lowerCamelCase="gelu_new", lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.0, lowerCamelCase=0.1, lowerCamelCase=1E-5, lowerCamelCase=0.0_2, lowerCamelCase=True, lowerCamelCase=5_02_56, lowerCamelCase=5_02_56, **lowerCamelCase, ) -> Tuple:
"""simple docstring"""
_lowercase : int = vocab_size
_lowercase : str = max_position_embeddings
_lowercase : str = hidden_size
_lowercase : Optional[int] = num_layers
_lowercase : List[Any] = num_heads
_lowercase : str = intermediate_size
_lowercase : Optional[Any] = window_size
_lowercase : Any = activation_function
_lowercase : Union[str, Any] = resid_dropout
_lowercase : Union[str, Any] = embed_dropout
_lowercase : Optional[int] = attention_dropout
_lowercase : Any = classifier_dropout
_lowercase : List[str] = layer_norm_epsilon
_lowercase : List[Any] = initializer_range
_lowercase : Any = use_cache
_lowercase : List[str] = bos_token_id
_lowercase : int = eos_token_id
_lowercase : int = attention_types
_lowercase : Optional[Any] = self.expand_attention_types_params(lowerCamelCase)
if len(self.attention_layers) != self.num_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.attention_layers)` == `config.num_layers` '
F'''but is `len(config.attention_layers) = {len(self.attention_layers)}`, '''
F'''`config.num_layers = {self.num_layers}`. '''
'`config.attention_layers` is prepared using `config.attention_types`. '
'Please verify the value of `config.attention_types` argument.')
super().__init__(bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, **lowerCamelCase)
@staticmethod
def UpperCamelCase ( lowerCamelCase) -> Any:
"""simple docstring"""
_lowercase : List[Any] = []
for item in attention_types:
for _ in range(item[1]):
attentions.extend(item[0])
return attentions
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Tuple:
import torch
_lowercase : List[str] = input.size()
_lowercase : Any = len(lowerCamelCase_ )
_lowercase : List[str] = shape[dimension]
_lowercase : Optional[int] = torch.arange(0 , lowerCamelCase_ , lowerCamelCase_ )
_lowercase : Union[str, Any] = torch.div(sizedim - size , lowerCamelCase_ , rounding_mode='floor' ) + 1
_lowercase : Tuple = torch.arange(lowerCamelCase_ ) + low_indices[:min_length][:, None]
_lowercase : List[Any] = [slice(lowerCamelCase_ )] * rank
_lowercase : Optional[int] = indices
_lowercase : Dict = input[s]
_lowercase : Dict = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
import torch
_lowercase : str = torch.arange(1 , lowerCamelCase_ )
_lowercase : str = torch.remainder(lowerCamelCase_ , lowerCamelCase_ )
_lowercase : int = remainders == 0
_lowercase : str = candidates[divisor_indices]
_lowercase : Optional[int] = torch.max(lowerCamelCase_ )
return largest_divisor, torch.div(lowerCamelCase_ , lowerCamelCase_ , rounding_mode='floor' )
class _lowerCamelCase( _a ):
@property
def UpperCamelCase ( self) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
_lowercase : Any = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase, direction='inputs')
_lowercase : Tuple = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_lowercase : str = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def UpperCamelCase ( self) -> int:
"""simple docstring"""
return self._config.num_heads
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = -1, lowerCamelCase = -1, lowerCamelCase = False, lowerCamelCase = None, ) -> Mapping[str, Any]:
"""simple docstring"""
_lowercase : Dict = super(lowerCamelCase, self).generate_dummy_inputs(
lowerCamelCase, batch_size=lowerCamelCase, seq_length=lowerCamelCase, is_pair=lowerCamelCase, framework=lowerCamelCase)
# We need to order the input in the way they appears in the forward()
_lowercase : Tuple = OrderedDict({'input_ids': common_inputs['input_ids']})
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
else:
import torch
_lowercase : str = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_lowercase : List[Any] = seqlen + 2
_lowercase : Union[str, Any] = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_lowercase : Any = [
(torch.zeros(lowerCamelCase), torch.zeros(lowerCamelCase)) for _ in range(self.num_layers)
]
_lowercase : Optional[Any] = common_inputs['attention_mask']
if self.use_past:
_lowercase : int = ordered_inputs['attention_mask'].dtype
_lowercase : int = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(lowerCamelCase, lowerCamelCase, dtype=lowerCamelCase)], dim=1)
return ordered_inputs
@property
def UpperCamelCase ( self) -> int:
"""simple docstring"""
return 13
| 720 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowercase ( __UpperCAmelCase , unittest.TestCase ):
_lowerCamelCase = FunnelTokenizer
_lowerCamelCase = FunnelTokenizerFast
_lowerCamelCase = True
_lowerCamelCase = True
def lowerCAmelCase__ ( self ):
super().setUp()
__magic_name__ = [
'''<unk>''',
'''<cls>''',
'''<sep>''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__magic_name__ = 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 lowerCAmelCase__ ( self , **UpperCamelCase_ ):
return FunnelTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCAmelCase__ ( self , **UpperCamelCase_ ):
return FunnelTokenizerFast.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
__magic_name__ = '''UNwant\u00E9d,running'''
__magic_name__ = '''unwanted, running'''
return input_text, output_text
def lowerCAmelCase__ ( self ):
__magic_name__ = self.tokenizer_class(self.vocab_file )
__magic_name__ = tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(UpperCamelCase_ , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [7, 4, 5, 10, 8, 9] )
def lowerCAmelCase__ ( self ):
__magic_name__ = self.get_tokenizers(do_lower_case=UpperCamelCase_ )
for tokenizer in tokenizers:
__magic_name__ = tokenizer('''UNwant\u00E9d,running''' )
__magic_name__ = len(inputs['''input_ids'''] ) - 1
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len )
__magic_name__ = tokenizer('''UNwant\u00E9d,running''' , '''UNwant\u00E9d,running''' )
self.assertListEqual(inputs['''token_type_ids'''] , [2] + [0] * sentence_len + [1] * sentence_len )
| 490 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__lowerCamelCase = "src/diffusers"
# Matches is_xxx_available()
__lowerCamelCase = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
__lowerCamelCase = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
__lowerCamelCase = "\n{0} = None\n"
__lowerCamelCase = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n"
__lowerCamelCase = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
def lowercase ( __UpperCamelCase ) -> Tuple:
__magic_name__ = _re_backend.findall(__UpperCamelCase )
if len(__UpperCamelCase ) == 0:
return None
return "_and_".join(__UpperCamelCase )
def lowercase ( ) -> List[str]:
with open(os.path.join(__UpperCamelCase , '''__init__.py''' ) , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
__magic_name__ = f.readlines()
# Get to the point we do the actual imports for type checking
__magic_name__ = 0
__magic_name__ = {}
# Go through the end of the file
while line_index < len(__UpperCamelCase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
__magic_name__ = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('''else:''' ):
line_index += 1
line_index += 1
__magic_name__ = []
# Until we unindent, add backend objects to the list
while line_index < len(__UpperCamelCase ) and len(lines[line_index] ) > 1:
__magic_name__ = lines[line_index]
__magic_name__ = _re_single_line_import.search(__UpperCamelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(__UpperCamelCase ) > 0:
__magic_name__ = objects
else:
line_index += 1
return backend_specific_objects
def lowercase ( __UpperCamelCase , __UpperCamelCase ) -> str:
if name.isupper():
return DUMMY_CONSTANT.format(__UpperCamelCase )
elif name.islower():
return DUMMY_FUNCTION.format(__UpperCamelCase , __UpperCamelCase )
else:
return DUMMY_CLASS.format(__UpperCamelCase , __UpperCamelCase )
def lowercase ( __UpperCamelCase=None ) -> List[Any]:
if backend_specific_objects is None:
__magic_name__ = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
__magic_name__ = {}
for backend, objects in backend_specific_objects.items():
__magic_name__ = '''[''' + ''', '''.join(f'''"{b}"''' for b in backend.split('''_and_''' ) ) + ''']'''
__magic_name__ = '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(__UpperCamelCase , __UpperCamelCase ) for o in objects] )
__magic_name__ = dummy_file
return dummy_files
def lowercase ( __UpperCamelCase=False ) -> List[str]:
__magic_name__ = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
__magic_name__ = {'''torch''': '''pt'''}
# Locate actual dummy modules and read their content.
__magic_name__ = os.path.join(__UpperCamelCase , '''utils''' )
__magic_name__ = {
backend: os.path.join(__UpperCamelCase , f'''dummy_{short_names.get(__UpperCamelCase , __UpperCamelCase )}_objects.py''' )
for backend in dummy_files.keys()
}
__magic_name__ = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(__UpperCamelCase ):
with open(__UpperCamelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
__magic_name__ = f.read()
else:
__magic_name__ = ''''''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f'''Updating diffusers.utils.dummy_{short_names.get(__UpperCamelCase , __UpperCamelCase )}_objects.py as the main '''
'''__init__ has new objects.''' )
with open(dummy_file_paths[backend] , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'''The main __init__ has objects that are not present in '''
f'''diffusers.utils.dummy_{short_names.get(__UpperCamelCase , __UpperCamelCase )}_objects.py. Run `make fix-copies` '''
'''to fix this.''' )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
__lowerCamelCase = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 490 | 1 |
"""simple docstring"""
import os
import time
import numpy as np
import onnxruntime as ort
lowercase__ : List[str] = """1"""
lowercase__ : Optional[int] = """0"""
lowercase__ : Optional[Any] = """1"""
lowercase__ : str = ort.SessionOptions()
lowercase__ : Union[str, Any] = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
lowercase__ : Tuple = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
lowercase__ : List[str] = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
lowercase__ : List[str] = ort.RunOptions()
lowercase__ : int = 1_2_8
lowercase__ : int = 1
lowercase__ : List[Any] = np.ones((batch, sequence), dtype=np.intaa)
lowercase__ : List[Any] = np.ones((batch, sequence), dtype=np.intaa)
lowercase__ : Tuple = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
lowercase__ : Optional[int] = time.time()
lowercase__ : Optional[int] = 2_0_0_0
lowercase__ : Union[str, Any] = {}
for iter in range(max_iters):
lowercase__ : int = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1_0_0_0 / max_iters))
| 317 |
"""simple docstring"""
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.
lowercase__ : Dict = {"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_SCREAMING_SNAKE_CASE = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
_SCREAMING_SNAKE_CASE = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
_SCREAMING_SNAKE_CASE = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
lowerCAmelCase_ : Any = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' )
lowerCAmelCase_ : Tuple = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
lowerCAmelCase_ : str = text_classifier('This is great !' , top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}] )
lowerCAmelCase_ : int = text_classifier(['This is great !', 'This is bad'] , top_k=2 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
] , )
lowerCAmelCase_ : Optional[Any] = text_classifier('This is great !' , top_k=1 )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
# Legacy behavior
lowerCAmelCase_ : Union[str, Any] = text_classifier('This is great !' , return_all_scores=SCREAMING_SNAKE_CASE_ )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
lowerCAmelCase_ : int = text_classifier('This is great !' , return_all_scores=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]] )
lowerCAmelCase_ : List[Any] = text_classifier(['This is great !', 'Something else'] , return_all_scores=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
] , )
lowerCAmelCase_ : Optional[int] = text_classifier(['This is great !', 'Something else'] , return_all_scores=SCREAMING_SNAKE_CASE_ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [
{'label': 'LABEL_0', 'score': 0.5_04},
{'label': 'LABEL_0', 'score': 0.5_04},
] , )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
import torch
lowerCAmelCase_ : List[Any] = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='pt' , device=torch.device('cpu' ) , )
lowerCAmelCase_ : List[Any] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
@require_tf
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
lowerCAmelCase_ : int = pipeline(
task='text-classification' , model='hf-internal-testing/tiny-random-distilbert' , framework='tf' )
lowerCAmelCase_ : Optional[Any] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'LABEL_0', 'score': 0.5_04}] )
@slow
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
lowerCAmelCase_ : Dict = pipeline('text-classification' )
lowerCAmelCase_ : List[Any] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'POSITIVE', 'score': 1.0}] )
lowerCAmelCase_ : Any = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
lowerCAmelCase_ : List[str] = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'POSITIVE', 'score': 0.9_88}] )
@slow
@require_tf
def SCREAMING_SNAKE_CASE__ ( self : str ):
lowerCAmelCase_ : str = pipeline('text-classification' , framework='tf' )
lowerCAmelCase_ : List[str] = text_classifier('This is great !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'POSITIVE', 'score': 1.0}] )
lowerCAmelCase_ : Union[str, Any] = text_classifier('This is bad !' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'NEGATIVE', 'score': 1.0}] )
lowerCAmelCase_ : str = text_classifier('Birds are a type of animal' )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': 'POSITIVE', 'score': 0.9_88}] )
def SCREAMING_SNAKE_CASE__ ( self : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str ):
lowerCAmelCase_ : str = TextClassificationPipeline(model=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
return text_classifier, ["HuggingFace is in", "This is another test"]
def SCREAMING_SNAKE_CASE__ ( self : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str ):
lowerCAmelCase_ : Optional[Any] = text_classifier.model
# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
lowerCAmelCase_ : int = 'HuggingFace is in'
lowerCAmelCase_ : Optional[Any] = text_classifier(SCREAMING_SNAKE_CASE_ )
self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': ANY(SCREAMING_SNAKE_CASE_ ), 'score': ANY(SCREAMING_SNAKE_CASE_ )}] )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
lowerCAmelCase_ : Union[str, Any] = ['HuggingFace is in ', 'Paris is in France']
lowerCAmelCase_ : Optional[Any] = text_classifier(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': ANY(SCREAMING_SNAKE_CASE_ ), 'score': ANY(SCREAMING_SNAKE_CASE_ )}, {'label': ANY(SCREAMING_SNAKE_CASE_ ), 'score': ANY(SCREAMING_SNAKE_CASE_ )}] , )
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
lowerCAmelCase_ : Union[str, Any] = text_classifier(SCREAMING_SNAKE_CASE_ , top_k=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : int = len(model.config.idalabel.values() )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [[{'label': ANY(SCREAMING_SNAKE_CASE_ ), 'score': ANY(SCREAMING_SNAKE_CASE_ )}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE_ ), 'score': ANY(SCREAMING_SNAKE_CASE_ )}] * N] , )
lowerCAmelCase_ : Dict = {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
lowerCAmelCase_ : Dict = text_classifier(SCREAMING_SNAKE_CASE_ )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {'label': ANY(SCREAMING_SNAKE_CASE_ ), 'score': ANY(SCREAMING_SNAKE_CASE_ )} , )
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.
lowerCAmelCase_ : Union[str, Any] = [['HuggingFace is in ', 'Paris is in France']]
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
text_classifier(SCREAMING_SNAKE_CASE_ )
# This used to be valid for doing text pairs
# We're keeping it working because of backward compatibility
lowerCAmelCase_ : Tuple = text_classifier([[['HuggingFace is in ', 'Paris is in France']]] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , [{'label': ANY(SCREAMING_SNAKE_CASE_ ), 'score': ANY(SCREAMING_SNAKE_CASE_ )}] , )
self.assertTrue(outputs[0]['label'] in model.config.idalabel.values() )
| 317 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
snake_case_ = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0.bias', 'time_embedding.linear_1.bias'),
('time_embed.2.weight', 'time_embedding.linear_2.weight'),
('time_embed.2.bias', 'time_embedding.linear_2.bias'),
('input_blocks.0.0.weight', 'conv_in.weight'),
('input_blocks.0.0.bias', 'conv_in.bias'),
('out.0.weight', 'conv_norm_out.weight'),
('out.0.bias', 'conv_norm_out.bias'),
('out.2.weight', 'conv_out.weight'),
('out.2.bias', 'conv_out.bias'),
]
snake_case_ = [
# (stable-diffusion, HF Diffusers)
('in_layers.0', 'norm1'),
('in_layers.2', 'conv1'),
('out_layers.0', 'norm2'),
('out_layers.3', 'conv2'),
('emb_layers.1', 'time_emb_proj'),
('skip_connection', 'conv_shortcut'),
]
snake_case_ = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
snake_case_ = F'''down_blocks.{i}.resnets.{j}.'''
snake_case_ = F'''input_blocks.{3*i + j + 1}.0.'''
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
snake_case_ = F'''down_blocks.{i}.attentions.{j}.'''
snake_case_ = F'''input_blocks.{3*i + j + 1}.1.'''
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
snake_case_ = F'''up_blocks.{i}.resnets.{j}.'''
snake_case_ = F'''output_blocks.{3*i + j}.0.'''
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
snake_case_ = F'''up_blocks.{i}.attentions.{j}.'''
snake_case_ = F'''output_blocks.{3*i + j}.1.'''
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
snake_case_ = F'''down_blocks.{i}.downsamplers.0.conv.'''
snake_case_ = F'''input_blocks.{3*(i+1)}.0.op.'''
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
snake_case_ = F'''up_blocks.{i}.upsamplers.0.'''
snake_case_ = F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
snake_case_ = 'mid_block.attentions.0.'
snake_case_ = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
snake_case_ = F'''mid_block.resnets.{j}.'''
snake_case_ = F'''middle_block.{2*j}.'''
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Any ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
SCREAMING_SNAKE_CASE_ : Dict = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
SCREAMING_SNAKE_CASE_ : List[str] = v.replace(a__ , a__ )
SCREAMING_SNAKE_CASE_ : Optional[int] = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
SCREAMING_SNAKE_CASE_ : List[str] = v.replace(a__ , a__ )
SCREAMING_SNAKE_CASE_ : int = v
SCREAMING_SNAKE_CASE_ : Tuple = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
snake_case_ = [
# (stable-diffusion, HF Diffusers)
('nin_shortcut', 'conv_shortcut'),
('norm_out', 'conv_norm_out'),
('mid.attn_1.', 'mid_block.attentions.0.'),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
snake_case_ = F'''encoder.down_blocks.{i}.resnets.{j}.'''
snake_case_ = F'''encoder.down.{i}.block.{j}.'''
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
snake_case_ = F'''down_blocks.{i}.downsamplers.0.'''
snake_case_ = F'''down.{i}.downsample.'''
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
snake_case_ = F'''up_blocks.{i}.upsamplers.0.'''
snake_case_ = F'''up.{3-i}.upsample.'''
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
snake_case_ = F'''decoder.up_blocks.{i}.resnets.{j}.'''
snake_case_ = F'''decoder.up.{3-i}.block.{j}.'''
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
snake_case_ = F'''mid_block.resnets.{i}.'''
snake_case_ = F'''mid.block_{i+1}.'''
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
snake_case_ = [
# (stable-diffusion, HF Diffusers)
('norm.', 'group_norm.'),
('q.', 'query.'),
('k.', 'key.'),
('v.', 'value.'),
('proj_out.', 'proj_attn.'),
]
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return w.reshape(*w.shape , 1 , 1 )
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
SCREAMING_SNAKE_CASE_ : List[str] = v.replace(a__ , a__ )
SCREAMING_SNAKE_CASE_ : List[str] = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
SCREAMING_SNAKE_CASE_ : str = v.replace(a__ , a__ )
SCREAMING_SNAKE_CASE_ : Any = v
SCREAMING_SNAKE_CASE_ : Optional[int] = {v: vae_state_dict[k] for k, v in mapping.items()}
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F"mid.attn_1.{weight_name}.weight" in k:
print(F"Reshaping {k} for SD format" )
SCREAMING_SNAKE_CASE_ : Tuple = reshape_weight_for_sd(a__ )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
snake_case_ = [
# (stable-diffusion, HF Diffusers)
('resblocks.', 'text_model.encoder.layers.'),
('ln_1', 'layer_norm1'),
('ln_2', 'layer_norm2'),
('.c_fc.', '.fc1.'),
('.c_proj.', '.fc2.'),
('.attn', '.self_attn'),
('ln_final.', 'transformer.text_model.final_layer_norm.'),
('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
snake_case_ = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
snake_case_ = re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
snake_case_ = {'q': 0, 'k': 1, 'v': 2}
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = {}
SCREAMING_SNAKE_CASE_ : Optional[Any] = {}
SCREAMING_SNAKE_CASE_ : Dict = {}
for k, v in text_enc_dict.items():
if (
k.endswith(".self_attn.q_proj.weight" )
or k.endswith(".self_attn.k_proj.weight" )
or k.endswith(".self_attn.v_proj.weight" )
):
SCREAMING_SNAKE_CASE_ : Optional[int] = k[: -len(".q_proj.weight" )]
SCREAMING_SNAKE_CASE_ : List[Any] = k[-len("q_proj.weight" )]
if k_pre not in capture_qkv_weight:
SCREAMING_SNAKE_CASE_ : List[Any] = [None, None, None]
SCREAMING_SNAKE_CASE_ : Tuple = v
continue
if (
k.endswith(".self_attn.q_proj.bias" )
or k.endswith(".self_attn.k_proj.bias" )
or k.endswith(".self_attn.v_proj.bias" )
):
SCREAMING_SNAKE_CASE_ : str = k[: -len(".q_proj.bias" )]
SCREAMING_SNAKE_CASE_ : int = k[-len("q_proj.bias" )]
if k_pre not in capture_qkv_bias:
SCREAMING_SNAKE_CASE_ : str = [None, None, None]
SCREAMING_SNAKE_CASE_ : Any = v
continue
SCREAMING_SNAKE_CASE_ : List[Any] = textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_ : protected[re.escape(m.group(0 ) )] , a__ )
SCREAMING_SNAKE_CASE_ : Dict = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
SCREAMING_SNAKE_CASE_ : Optional[int] = textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_ : protected[re.escape(m.group(0 ) )] , a__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.cat(a__ )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
SCREAMING_SNAKE_CASE_ : str = textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE_ : protected[re.escape(m.group(0 ) )] , a__ )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.cat(a__ )
return new_state_dict
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str ) -> Optional[Any]:
"""simple docstring"""
return text_enc_dict
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
snake_case_ = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
snake_case_ = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
snake_case_ = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
snake_case_ = osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
snake_case_ = load_file(unet_path, device='cpu')
else:
snake_case_ = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
snake_case_ = torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
snake_case_ = load_file(vae_path, device='cpu')
else:
snake_case_ = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
snake_case_ = torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
snake_case_ = load_file(text_enc_path, device='cpu')
else:
snake_case_ = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
snake_case_ = torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
snake_case_ = convert_unet_state_dict(unet_state_dict)
snake_case_ = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
snake_case_ = convert_vae_state_dict(vae_state_dict)
snake_case_ = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
snake_case_ = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
snake_case_ = {'transformer.' + k: v for k, v in text_enc_dict.items()}
snake_case_ = convert_text_enc_state_dict_vaa(text_enc_dict)
snake_case_ = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
else:
snake_case_ = convert_text_enc_state_dict(text_enc_dict)
snake_case_ = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
snake_case_ = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
snake_case_ = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
snake_case_ = {'state_dict': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 421 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class __A( a ):
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=5 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , ) -> Tuple:
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_input_mask
__a = use_token_type_ids
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = type_sequence_label_size
__a = initializer_range
__a = num_labels
__a = num_choices
__a = scope
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a = None
if self.use_input_mask:
__a = random_attention_mask([self.batch_size, self.seq_length] )
__a = None
__a = None
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a = ids_tensor([self.batch_size] , self.num_choices )
__a = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
return 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 , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = DistilBertModel(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , _snake_case )
__a = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> int:
'''simple docstring'''
__a = DistilBertForMaskedLM(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> str:
'''simple docstring'''
__a = DistilBertForQuestionAnswering(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(
_snake_case , attention_mask=_snake_case , start_positions=_snake_case , end_positions=_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 SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> List[Any]:
'''simple docstring'''
__a = self.num_labels
__a = DistilBertForSequenceClassification(_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> str:
'''simple docstring'''
__a = self.num_labels
__a = DistilBertForTokenClassification(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , attention_mask=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.num_choices
__a = DistilBertForMultipleChoice(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a = model(
_snake_case , attention_mask=_snake_case , labels=_snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
((__a) , (__a) , (__a) , (__a) , (__a) , (__a)) = config_and_inputs
__a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __A( a , a , unittest.TestCase ):
snake_case_ = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
snake_case_ = (
{
'''feature-extraction''': DistilBertModel,
'''fill-mask''': DistilBertForMaskedLM,
'''question-answering''': DistilBertForQuestionAnswering,
'''text-classification''': DistilBertForSequenceClassification,
'''token-classification''': DistilBertForTokenClassification,
'''zero-shot''': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = True
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = DistilBertModelTester(self )
__a = ConfigTester(self , config_class=_snake_case , dim=37 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_snake_case )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = DistilBertModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@slow
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
__a = True
__a = model_class(config=_snake_case )
__a = self._prepare_for_class(_snake_case , _snake_case )
__a = torch.jit.trace(
_snake_case , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_snake_case , os.path.join(_snake_case , '''traced_model.pt''' ) )
__a = torch.jit.load(os.path.join(_snake_case , '''traced_model.pt''' ) , map_location=_snake_case )
loaded(inputs_dict['''input_ids'''].to(_snake_case ) , inputs_dict['''attention_mask'''].to(_snake_case ) )
@require_torch
class __A( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = DistilBertModel.from_pretrained('''distilbert-base-uncased''' )
__a = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
__a = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__a = model(_snake_case , attention_mask=_snake_case )[0]
__a = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _snake_case )
__a = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _snake_case , atol=1E-4 ) ) | 219 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--txt2img_unclip",
default="kakaobrain/karlo-v1-alpha",
type=str,
required=False,
help="The pretrained txt2img unclip.",
)
_A = parser.parse_args()
_A = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
_A = CLIPImageProcessor()
_A = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
_A = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 294 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
"feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
"processing_wav2vec2": ["Wav2Vec2Processor"],
"tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Wav2Vec2ForAudioFrameClassification",
"Wav2Vec2ForCTC",
"Wav2Vec2ForMaskedLM",
"Wav2Vec2ForPreTraining",
"Wav2Vec2ForSequenceClassification",
"Wav2Vec2ForXVector",
"Wav2Vec2Model",
"Wav2Vec2PreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFWav2Vec2ForCTC",
"TFWav2Vec2Model",
"TFWav2Vec2PreTrainedModel",
"TFWav2Vec2ForSequenceClassification",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"FlaxWav2Vec2ForCTC",
"FlaxWav2Vec2ForPreTraining",
"FlaxWav2Vec2Model",
"FlaxWav2Vec2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .processing_wavaveca import WavaVecaProcessor
from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavaveca import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
WavaVecaForAudioFrameClassification,
WavaVecaForCTC,
WavaVecaForMaskedLM,
WavaVecaForPreTraining,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
WavaVecaModel,
WavaVecaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWavaVecaForCTC,
TFWavaVecaForSequenceClassification,
TFWavaVecaModel,
TFWavaVecaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_wavaveca import (
FlaxWavaVecaForCTC,
FlaxWavaVecaForPreTraining,
FlaxWavaVecaModel,
FlaxWavaVecaPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 294 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCamelCase:
snake_case_ : int
snake_case_ : int
class UpperCamelCase:
def __init__( self : Tuple , SCREAMING_SNAKE_CASE : int ) -> Any:
'''simple docstring'''
__snake_case = [[] for _ in range(SCREAMING_SNAKE_CASE )]
__snake_case = size
def __getitem__( self : List[Any] , SCREAMING_SNAKE_CASE : int ) -> Iterator[Edge]:
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
return self._size
def SCREAMING_SNAKE_CASE_ ( self : Any , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> Optional[Any]:
'''simple docstring'''
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 SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ) -> int | None:
'''simple docstring'''
__snake_case = deque([start_vertex] )
__snake_case = [None] * self.size
__snake_case = 0
while queue:
__snake_case = queue.popleft()
__snake_case = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
__snake_case = current_distance + edge.weight
__snake_case = distances[edge.destination_vertex]
if (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
and new_distance >= dest_vertex_distance
):
continue
__snake_case = 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()
| 371 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
A : List[str] = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "csv"] )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Any:
'''simple docstring'''
inspect_dataset(_lowerCAmelCase , _lowerCAmelCase )
__snake_case = path + ".py"
assert script_name in os.listdir(_lowerCAmelCase )
assert "__pycache__" not in os.listdir(_lowerCAmelCase )
@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.parametrize("path" , ["accuracy"] )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> List[str]:
'''simple docstring'''
inspect_metric(_lowerCAmelCase , _lowerCAmelCase )
__snake_case = path + ".py"
assert script_name in os.listdir(_lowerCAmelCase )
assert "__pycache__" not in os.listdir(_lowerCAmelCase )
@pytest.mark.parametrize(
"path, config_name, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
__snake_case = get_dataset_config_info(_lowerCAmelCase , config_name=_lowerCAmelCase )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
with pytest.raises(_lowerCAmelCase ):
get_dataset_config_info(_lowerCAmelCase , config_name=_lowerCAmelCase )
@pytest.mark.parametrize(
"path, expected" , [
("squad", "plain_text"),
("acronym_identification", "default"),
("lhoestq/squad", "plain_text"),
("lhoestq/test", "default"),
("lhoestq/demo1", "lhoestq--demo1"),
("dalle-mini/wit", "dalle-mini--wit"),
] , )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> Tuple:
'''simple docstring'''
__snake_case = get_dataset_config_names(_lowerCAmelCase )
assert expected in config_names
@pytest.mark.parametrize(
"path, expected_configs, expected_splits_in_first_config" , [
("squad", ["plain_text"], ["train", "validation"]),
("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
] , )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Any:
'''simple docstring'''
__snake_case = get_dataset_infos(_lowerCAmelCase )
assert list(infos.keys() ) == expected_configs
__snake_case = expected_configs[0]
assert expected_config in infos
__snake_case = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"path, expected_config, expected_splits" , [
("squad", "plain_text", ["train", "validation"]),
("dalle-mini/wit", "dalle-mini--wit", ["train"]),
("paws", "labeled_final", ["train", "test", "validation"]),
] , )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
'''simple docstring'''
__snake_case = get_dataset_infos(_lowerCAmelCase )
assert expected_config in infos
__snake_case = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"path, config_name, expected_exception" , [
("paws", None, ValueError),
] , )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
'''simple docstring'''
with pytest.raises(_lowerCAmelCase ):
get_dataset_split_names(_lowerCAmelCase , config_name=_lowerCAmelCase )
| 371 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ : str = logging.get_logger(__name__)
def _lowerCAmelCase(a : Optional[int] , a : Any=False , a : Dict=False , a : Dict=False ) -> int:
_SCREAMING_SNAKE_CASE =[]
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""transformer.blocks.{i}.norm1.weight""", f"""vilt.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.norm1.bias""", f"""vilt.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""transformer.blocks.{i}.attn.proj.weight""", f"""vilt.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(f"""transformer.blocks.{i}.attn.proj.bias""", f"""vilt.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""transformer.blocks.{i}.norm2.weight""", f"""vilt.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.norm2.bias""", f"""vilt.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(f"""transformer.blocks.{i}.mlp.fc1.weight""", f"""vilt.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc1.bias""", f"""vilt.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.weight""", f"""vilt.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""transformer.blocks.{i}.mlp.fc2.bias""", f"""vilt.encoder.layer.{i}.output.dense.bias""") )
# embeddings
rename_keys.extend(
[
# text embeddings
('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
(
'''text_embeddings.position_embeddings.weight''',
'''vilt.embeddings.text_embeddings.position_embeddings.weight''',
),
('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
(
'''text_embeddings.token_type_embeddings.weight''',
'''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
),
('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
# patch embeddings
('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
# token type embeddings
('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
] )
# final layernorm + pooler
rename_keys.extend(
[
('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
] )
else:
pass
return rename_keys
def _lowerCAmelCase(a : Optional[int] , a : Optional[int] ) -> Union[str, Any]:
for i in range(config.num_hidden_layers ):
_SCREAMING_SNAKE_CASE ='''vilt.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_SCREAMING_SNAKE_CASE =state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.weight""" )
_SCREAMING_SNAKE_CASE =state_dict.pop(f"""transformer.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE =in_proj_weight[
: config.hidden_size, :
]
_SCREAMING_SNAKE_CASE =in_proj_bias[: config.hidden_size]
_SCREAMING_SNAKE_CASE =in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_SCREAMING_SNAKE_CASE =in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_SCREAMING_SNAKE_CASE =in_proj_weight[
-config.hidden_size :, :
]
_SCREAMING_SNAKE_CASE =in_proj_bias[-config.hidden_size :]
def _lowerCAmelCase(a : Optional[int] ) -> str:
_SCREAMING_SNAKE_CASE =['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowerCAmelCase(a : Dict , a : Any , a : Tuple ) -> Any:
_SCREAMING_SNAKE_CASE =dct.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE =val
@torch.no_grad()
def _lowerCAmelCase(a : Optional[Any] , a : str ) -> str:
_SCREAMING_SNAKE_CASE =ViltConfig(image_size=384 , patch_size=32 , tie_word_embeddings=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_CASE =False
if "vqa" in checkpoint_url:
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =3129
_SCREAMING_SNAKE_CASE ='''huggingface/label-files'''
_SCREAMING_SNAKE_CASE ='''vqa2-id2label.json'''
_SCREAMING_SNAKE_CASE =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type='''dataset''' ) , '''r''' ) )
_SCREAMING_SNAKE_CASE ={int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE =idalabel
_SCREAMING_SNAKE_CASE ={v: k for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE =ViltForQuestionAnswering(SCREAMING_SNAKE_CASE_ )
elif "nlvr" in checkpoint_url:
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =2
_SCREAMING_SNAKE_CASE ={0: '''False''', 1: '''True'''}
_SCREAMING_SNAKE_CASE ={v: k for k, v in config.idalabel.items()}
_SCREAMING_SNAKE_CASE =3
_SCREAMING_SNAKE_CASE =ViltForImagesAndTextClassification(SCREAMING_SNAKE_CASE_ )
elif "irtr" in checkpoint_url:
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =ViltForImageAndTextRetrieval(SCREAMING_SNAKE_CASE_ )
elif "mlm_itm" in checkpoint_url:
_SCREAMING_SNAKE_CASE =True
_SCREAMING_SNAKE_CASE =ViltForMaskedLM(SCREAMING_SNAKE_CASE_ )
else:
raise ValueError('''Unknown model type''' )
# load state_dict of original model, remove and rename some keys
_SCREAMING_SNAKE_CASE =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location='''cpu''' )['''state_dict''']
_SCREAMING_SNAKE_CASE =create_rename_keys(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if mlm_model or irtr_model:
_SCREAMING_SNAKE_CASE =['''itm_score.fc.weight''', '''itm_score.fc.bias''']
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# Define processor
_SCREAMING_SNAKE_CASE =ViltImageProcessor(size=384 )
_SCREAMING_SNAKE_CASE =BertTokenizer.from_pretrained('''bert-base-uncased''' )
_SCREAMING_SNAKE_CASE =ViltProcessor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Forward pass on example inputs (image + text)
if nlvr_model:
_SCREAMING_SNAKE_CASE =Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' , stream=SCREAMING_SNAKE_CASE_ ).raw )
_SCREAMING_SNAKE_CASE =Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg''' , stream=SCREAMING_SNAKE_CASE_ ).raw )
_SCREAMING_SNAKE_CASE =(
'''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
''' standing.'''
)
_SCREAMING_SNAKE_CASE =processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
_SCREAMING_SNAKE_CASE =processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
_SCREAMING_SNAKE_CASE =model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
_SCREAMING_SNAKE_CASE =Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg''' , stream=SCREAMING_SNAKE_CASE_ ).raw )
if mlm_model:
_SCREAMING_SNAKE_CASE ='''a bunch of [MASK] laying on a [MASK].'''
else:
_SCREAMING_SNAKE_CASE ='''How many cats are there?'''
_SCREAMING_SNAKE_CASE =processor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' )
_SCREAMING_SNAKE_CASE =model(**SCREAMING_SNAKE_CASE_ )
# Verify outputs
if mlm_model:
_SCREAMING_SNAKE_CASE =torch.Size([1, 11, 3_0522] )
_SCREAMING_SNAKE_CASE =torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 )
# verify masked token prediction equals "cats"
_SCREAMING_SNAKE_CASE =outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
_SCREAMING_SNAKE_CASE =torch.Size([1, 3129] )
_SCREAMING_SNAKE_CASE =torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 )
# verify vqa prediction equals "2"
_SCREAMING_SNAKE_CASE =outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
_SCREAMING_SNAKE_CASE =torch.Size([1, 2] )
_SCREAMING_SNAKE_CASE =torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(f"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
UpperCAmelCase_ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
UpperCAmelCase_ : str = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 700 |
"""simple docstring"""
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
UpperCAmelCase_ : Tuple = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
UpperCAmelCase_ : List[Any] = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def _lowerCAmelCase(a : Union[str, Any] ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE =numpy.dtype(numpy.uintaa ).newbyteorder('''>''' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=a )[0]
@deprecated(a , '''Please use tf.data to implement this functionality.''' )
def _lowerCAmelCase(a : str ) -> Optional[int]:
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=a ) as bytestream:
_SCREAMING_SNAKE_CASE =_readaa(a )
if magic != 2051:
raise ValueError(
'''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name) )
_SCREAMING_SNAKE_CASE =_readaa(a )
_SCREAMING_SNAKE_CASE =_readaa(a )
_SCREAMING_SNAKE_CASE =_readaa(a )
_SCREAMING_SNAKE_CASE =bytestream.read(rows * cols * num_images )
_SCREAMING_SNAKE_CASE =numpy.frombuffer(a , dtype=numpy.uinta )
_SCREAMING_SNAKE_CASE =data.reshape(a , a , a , 1 )
return data
@deprecated(a , '''Please use tf.one_hot on tensors.''' )
def _lowerCAmelCase(a : Tuple , a : Dict ) -> Dict:
_SCREAMING_SNAKE_CASE =labels_dense.shape[0]
_SCREAMING_SNAKE_CASE =numpy.arange(a ) * num_classes
_SCREAMING_SNAKE_CASE =numpy.zeros((num_labels, num_classes) )
_SCREAMING_SNAKE_CASE =1
return labels_one_hot
@deprecated(a , '''Please use tf.data to implement this functionality.''' )
def _lowerCAmelCase(a : Any , a : Any=False , a : Tuple=10 ) -> Optional[int]:
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=a ) as bytestream:
_SCREAMING_SNAKE_CASE =_readaa(a )
if magic != 2049:
raise ValueError(
'''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name) )
_SCREAMING_SNAKE_CASE =_readaa(a )
_SCREAMING_SNAKE_CASE =bytestream.read(a )
_SCREAMING_SNAKE_CASE =numpy.frombuffer(a , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(a , a )
return labels
class __UpperCAmelCase :
'''simple docstring'''
@deprecated(
_A , '''Please use alternatives such as official/mnist/_DataSet.py'''
''' from tensorflow/models.''' , )
def __init__( self , _A , _A , _A=False , _A=False , _A=dtypes.floataa , _A=True , _A=None , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =random_seed.get_seed(_A )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
_SCREAMING_SNAKE_CASE =dtypes.as_dtype(_A ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
if fake_data:
_SCREAMING_SNAKE_CASE =1_0_0_0_0
_SCREAMING_SNAKE_CASE =one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
_SCREAMING_SNAKE_CASE =images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
_SCREAMING_SNAKE_CASE =images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
_SCREAMING_SNAKE_CASE =images.astype(numpy.floataa )
_SCREAMING_SNAKE_CASE =numpy.multiply(_A , 1.0 / 255.0 )
_SCREAMING_SNAKE_CASE =images
_SCREAMING_SNAKE_CASE =labels
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self._images
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self._labels
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self._num_examples
@property
def UpperCamelCase_ ( self ):
'''simple docstring'''
return self._epochs_completed
def UpperCamelCase_ ( self , _A , _A=False , _A=True ):
'''simple docstring'''
if fake_data:
_SCREAMING_SNAKE_CASE =[1] * 7_8_4
_SCREAMING_SNAKE_CASE =[1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_A )],
[fake_label for _ in range(_A )],
)
_SCREAMING_SNAKE_CASE =self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
_SCREAMING_SNAKE_CASE =numpy.arange(self._num_examples )
numpy.random.shuffle(_A )
_SCREAMING_SNAKE_CASE =self.images[perma]
_SCREAMING_SNAKE_CASE =self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
_SCREAMING_SNAKE_CASE =self._num_examples - start
_SCREAMING_SNAKE_CASE =self._images[start : self._num_examples]
_SCREAMING_SNAKE_CASE =self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
_SCREAMING_SNAKE_CASE =numpy.arange(self._num_examples )
numpy.random.shuffle(_A )
_SCREAMING_SNAKE_CASE =self.images[perm]
_SCREAMING_SNAKE_CASE =self.labels[perm]
# Start next epoch
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =batch_size - rest_num_examples
_SCREAMING_SNAKE_CASE =self._index_in_epoch
_SCREAMING_SNAKE_CASE =self._images[start:end]
_SCREAMING_SNAKE_CASE =self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
_SCREAMING_SNAKE_CASE =self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(a , '''Please write your own downloading logic.''' )
def _lowerCAmelCase(a : Any , a : str , a : Optional[int] ) -> Optional[Any]:
if not gfile.Exists(a ):
gfile.MakeDirs(a )
_SCREAMING_SNAKE_CASE =os.path.join(a , a )
if not gfile.Exists(a ):
urllib.request.urlretrieve(a , a ) # noqa: S310
with gfile.GFile(a ) as f:
_SCREAMING_SNAKE_CASE =f.size()
print('''Successfully downloaded''' , a , a , '''bytes.''' )
return filepath
@deprecated(
a , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''' )
def _lowerCAmelCase(a : Optional[int] , a : Tuple=False , a : str=False , a : Union[str, Any]=dtypes.floataa , a : Tuple=True , a : Tuple=5000 , a : Union[str, Any]=None , a : List[str]=DEFAULT_SOURCE_URL , ) -> List[Any]:
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=a , one_hot=a , dtype=a , seed=a )
_SCREAMING_SNAKE_CASE =fake()
_SCREAMING_SNAKE_CASE =fake()
_SCREAMING_SNAKE_CASE =fake()
return _Datasets(train=a , validation=a , test=a )
if not source_url: # empty string check
_SCREAMING_SNAKE_CASE =DEFAULT_SOURCE_URL
_SCREAMING_SNAKE_CASE ='''train-images-idx3-ubyte.gz'''
_SCREAMING_SNAKE_CASE ='''train-labels-idx1-ubyte.gz'''
_SCREAMING_SNAKE_CASE ='''t10k-images-idx3-ubyte.gz'''
_SCREAMING_SNAKE_CASE ='''t10k-labels-idx1-ubyte.gz'''
_SCREAMING_SNAKE_CASE =_maybe_download(
a , a , source_url + train_images_file )
with gfile.Open(a , '''rb''' ) as f:
_SCREAMING_SNAKE_CASE =_extract_images(a )
_SCREAMING_SNAKE_CASE =_maybe_download(
a , a , source_url + train_labels_file )
with gfile.Open(a , '''rb''' ) as f:
_SCREAMING_SNAKE_CASE =_extract_labels(a , one_hot=a )
_SCREAMING_SNAKE_CASE =_maybe_download(
a , a , source_url + test_images_file )
with gfile.Open(a , '''rb''' ) as f:
_SCREAMING_SNAKE_CASE =_extract_images(a )
_SCREAMING_SNAKE_CASE =_maybe_download(
a , a , source_url + test_labels_file )
with gfile.Open(a , '''rb''' ) as f:
_SCREAMING_SNAKE_CASE =_extract_labels(a , one_hot=a )
if not 0 <= validation_size <= len(a ):
_SCREAMING_SNAKE_CASE =(
'''Validation size should be between 0 and '''
f"""{len(a )}. Received: {validation_size}."""
)
raise ValueError(a )
_SCREAMING_SNAKE_CASE =train_images[:validation_size]
_SCREAMING_SNAKE_CASE =train_labels[:validation_size]
_SCREAMING_SNAKE_CASE =train_images[validation_size:]
_SCREAMING_SNAKE_CASE =train_labels[validation_size:]
_SCREAMING_SNAKE_CASE ={'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}
_SCREAMING_SNAKE_CASE =_DataSet(a , a , **a )
_SCREAMING_SNAKE_CASE =_DataSet(a , a , **a )
_SCREAMING_SNAKE_CASE =_DataSet(a , a , **a )
return _Datasets(train=a , validation=a , test=a )
| 165 | 0 |
def _lowerCAmelCase ( lowerCAmelCase_ :Union[str, Any] )->bool:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
snake_case_ = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_UpperCamelCase )
if number < 0:
return False
snake_case_ = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 283 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class __SCREAMING_SNAKE_CASE ( A__ , A__ , unittest.TestCase ):
A : List[str] = IFInpaintingPipeline
A : List[str] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
A : Tuple = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
A : Optional[Any] = PipelineTesterMixin.required_optional_params - {'latents'}
def __lowerCamelCase ( self ):
return self._get_dummy_components()
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ):
if str(SCREAMING_SNAKE_CASE__ ).startswith('''mps''' ):
lowercase : Tuple = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
lowercase : Dict = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
lowercase : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
lowercase : Tuple = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def __lowerCamelCase ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def __lowerCamelCase ( self ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA''' )
def __lowerCamelCase ( self ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def __lowerCamelCase ( self ):
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def __lowerCamelCase ( self ):
self._test_save_load_local()
def __lowerCamelCase ( self ):
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 319 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase (unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : Any ) -> Any:
SCREAMING_SNAKE_CASE__ = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ = tf.convert_to_tensor(
[[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ = model(__UpperCAmelCase )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ = tf.TensorShape((1, 1_0, 7_6_8) )
self.assertEqual(output.shape , __UpperCAmelCase )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 719 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Tuple = "▁"
A_ : int = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
A_ : Dict = {
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
A_ : Dict = {
"facebook/m2m100_418M": 1_024,
}
# fmt: off
A_ : Any = {
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : Optional[int] = VOCAB_FILES_NAMES
lowerCamelCase__ : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ : Tuple = ['input_ids', 'attention_mask']
lowerCamelCase__ : List[int] = []
lowerCamelCase__ : List[int] = []
def __init__( self : List[str] , __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Optional[Any]=None , __UpperCAmelCase : int="<s>" , __UpperCAmelCase : Optional[int]="</s>" , __UpperCAmelCase : Any="</s>" , __UpperCAmelCase : Any="<pad>" , __UpperCAmelCase : str="<unk>" , __UpperCAmelCase : Dict="m2m100" , __UpperCAmelCase : Optional[Dict[str, Any]] = None , __UpperCAmelCase : str=8 , **__UpperCAmelCase : str , ) -> None:
SCREAMING_SNAKE_CASE__ = {} if sp_model_kwargs is None else sp_model_kwargs
SCREAMING_SNAKE_CASE__ = language_codes
SCREAMING_SNAKE_CASE__ = FAIRSEQ_LANGUAGE_CODES[language_codes]
SCREAMING_SNAKE_CASE__ = {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}
SCREAMING_SNAKE_CASE__ = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(__UpperCAmelCase )
for lang_code in fairseq_language_code
if self.get_lang_token(__UpperCAmelCase ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=__UpperCAmelCase , tgt_lang=__UpperCAmelCase , bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , language_codes=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=__UpperCAmelCase , **__UpperCAmelCase , )
SCREAMING_SNAKE_CASE__ = vocab_file
SCREAMING_SNAKE_CASE__ = load_json(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE__ = spm_file
SCREAMING_SNAKE_CASE__ = load_spm(__UpperCAmelCase , self.sp_model_kwargs )
SCREAMING_SNAKE_CASE__ = len(self.encoder )
SCREAMING_SNAKE_CASE__ = {
self.get_lang_token(__UpperCAmelCase ): self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase )
}
SCREAMING_SNAKE_CASE__ = {lang_code: self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase )}
SCREAMING_SNAKE_CASE__ = {v: k for k, v in self.lang_token_to_id.items()}
SCREAMING_SNAKE_CASE__ = src_lang if src_lang is not None else """en"""
SCREAMING_SNAKE_CASE__ = tgt_lang
SCREAMING_SNAKE_CASE__ = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
SCREAMING_SNAKE_CASE__ = num_madeup_words
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> int:
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def SCREAMING_SNAKE_CASE ( self : Any ) -> str:
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE ( self : str , __UpperCAmelCase : str ) -> None:
SCREAMING_SNAKE_CASE__ = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str , __UpperCAmelCase : Tuple ) -> Tuple:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(__UpperCAmelCase , self.encoder[self.unk_token] )
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : int ) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(__UpperCAmelCase , self.unk_token )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __UpperCAmelCase : Optional[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__UpperCAmelCase ) + token
SCREAMING_SNAKE_CASE__ = []
else:
current_sub_tokens.append(__UpperCAmelCase )
out_string += self.sp_model.decode(__UpperCAmelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None , __UpperCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__UpperCAmelCase )) + suffix_ones
return prefix_ones + ([0] * len(__UpperCAmelCase )) + ([0] * len(__UpperCAmelCase )) + suffix_ones
def SCREAMING_SNAKE_CASE ( self : List[str] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
SCREAMING_SNAKE_CASE__ = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Union[str, Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ = None
return state
def __setstate__( self : Union[str, Any] , __UpperCAmelCase : Dict ) -> None:
SCREAMING_SNAKE_CASE__ = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = load_spm(self.spm_file , self.sp_model_kwargs )
def SCREAMING_SNAKE_CASE ( self : Tuple , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) -> Tuple[str]:
SCREAMING_SNAKE_CASE__ = Path(__UpperCAmelCase )
if not save_dir.is_dir():
raise OSError(F"""{save_directory} should be a directory""" )
SCREAMING_SNAKE_CASE__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
SCREAMING_SNAKE_CASE__ = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , __UpperCAmelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , __UpperCAmelCase )
elif not os.path.isfile(self.spm_file ):
with open(__UpperCAmelCase , """wb""" ) as fi:
SCREAMING_SNAKE_CASE__ = self.sp_model.serialized_model_proto()
fi.write(__UpperCAmelCase )
return (str(__UpperCAmelCase ), str(__UpperCAmelCase ))
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : str = "en" , __UpperCAmelCase : Optional[List[str]] = None , __UpperCAmelCase : str = "ro" , **__UpperCAmelCase : str , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE__ = src_lang
SCREAMING_SNAKE_CASE__ = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(__UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : int , __UpperCAmelCase : Optional[str] , __UpperCAmelCase : Optional[str] , **__UpperCAmelCase : Tuple ) -> str:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ = src_lang
SCREAMING_SNAKE_CASE__ = self(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , **__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = self.get_lang_id(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE ( self : str ) -> str:
self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE ( self : List[Any] , __UpperCAmelCase : str ) -> None:
SCREAMING_SNAKE_CASE__ = self.get_lang_token(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = self.lang_token_to_id[lang_token]
SCREAMING_SNAKE_CASE__ = [self.cur_lang_id]
SCREAMING_SNAKE_CASE__ = [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : str ) -> None:
SCREAMING_SNAKE_CASE__ = self.get_lang_token(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = self.lang_token_to_id[lang_token]
SCREAMING_SNAKE_CASE__ = [self.cur_lang_id]
SCREAMING_SNAKE_CASE__ = [self.eos_token_id]
def SCREAMING_SNAKE_CASE ( self : Dict , __UpperCAmelCase : str ) -> str:
return self.lang_code_to_token[lang]
def SCREAMING_SNAKE_CASE ( self : int , __UpperCAmelCase : str ) -> int:
SCREAMING_SNAKE_CASE__ = self.get_lang_token(__UpperCAmelCase )
return self.lang_token_to_id[lang_token]
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = sentencepiece.SentencePieceProcessor(**snake_case__ )
spm.Load(str(snake_case__ ) )
return spm
def A ( snake_case__ ):
'''simple docstring'''
with open(snake_case__ , """r""" ) as f:
return json.load(snake_case__ )
def A ( snake_case__ , snake_case__ ):
'''simple docstring'''
with open(snake_case__ , """w""" ) as f:
json.dump(snake_case__ , snake_case__ , indent=2 )
| 616 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
"""simple docstring"""
_UpperCAmelCase :List[Any] = None
_UpperCAmelCase :Dict = BloomTokenizerFast
_UpperCAmelCase :Tuple = BloomTokenizerFast
_UpperCAmelCase :str = True
_UpperCAmelCase :int = False
_UpperCAmelCase :List[Any] = "tokenizer_file"
_UpperCAmelCase :List[Any] = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
def _snake_case ( self ):
super().setUp()
lowercase__: int = BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''' )
tokenizer.save_pretrained(self.tmpdirname )
def _snake_case ( self , **_UpperCAmelCase ):
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def _snake_case ( self ):
lowercase__: Optional[Any] = self.get_rust_tokenizer()
lowercase__: List[str] = ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
lowercase__: str = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
lowercase__: int = tokenizer.batch_encode_plus(_UpperCAmelCase )['''input_ids''']
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
lowercase__: Any = tokenizer.batch_decode(_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def _snake_case ( self , _UpperCAmelCase=6 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__: int = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase , **_UpperCAmelCase )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
lowercase__: Any = '''This is a simple input'''
lowercase__: Dict = ['''This is a simple input 1''', '''This is a simple input 2''']
lowercase__: Dict = ('''This is a simple input''', '''This is a pair''')
lowercase__: List[Any] = [
('''This is a simple input 1''', '''This is a simple input 2'''),
('''This is a simple pair 1''', '''This is a simple pair 2'''),
]
# Simple input tests
try:
tokenizer_r.encode(_UpperCAmelCase , max_length=_UpperCAmelCase )
tokenizer_r.encode_plus(_UpperCAmelCase , max_length=_UpperCAmelCase )
tokenizer_r.batch_encode_plus(_UpperCAmelCase , max_length=_UpperCAmelCase )
tokenizer_r.encode(_UpperCAmelCase , max_length=_UpperCAmelCase )
tokenizer_r.batch_encode_plus(_UpperCAmelCase , max_length=_UpperCAmelCase )
except ValueError:
self.fail('''Bloom Tokenizer should be able to deal with padding''' )
lowercase__: Tuple = None # Hotfixing padding = None
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding='''max_length''' )
# Simple input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding='''max_length''' )
# Simple input
self.assertRaises(
_UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding='''max_length''' , )
# Pair input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding='''max_length''' )
# Pair input
self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding='''max_length''' )
# Pair input
self.assertRaises(
_UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding='''max_length''' , )
def _snake_case ( self ):
lowercase__: Tuple = self.get_rust_tokenizer()
lowercase__: Union[str, Any] = load_dataset('''xnli''' , '''all_languages''' , split='''test''' , streaming=_UpperCAmelCase )
lowercase__: int = next(iter(_UpperCAmelCase ) )['''premise'''] # pick up one data
lowercase__: Optional[int] = list(sample_data.values() )
lowercase__: Union[str, Any] = list(map(tokenizer.encode , _UpperCAmelCase ) )
lowercase__: Tuple = [tokenizer.decode(_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase ) for x in output_tokens]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def _snake_case ( self ):
# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
# any sequence length constraints. This test of the parent class will fail since it relies on the
# maximum sequence length of the positoonal embeddings.
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 586 | """simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
__A = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
__A = 1_0
__A = 2_5_6
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Optional[MinHash]:
if len(__UpperCAmelCase ) < MIN_NUM_TOKENS:
return None
lowercase__: Tuple = MinHash(num_perm=__UpperCAmelCase )
for token in set(__UpperCAmelCase ):
min_hash.update(token.encode() )
return min_hash
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Set[str]:
return {t for t in NON_ALPHA.split(__UpperCAmelCase ) if len(t.strip() ) > 0}
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , *,
_UpperCAmelCase = 0.85 , ):
lowercase__: Optional[int] = duplication_jaccard_threshold
lowercase__: str = NUM_PERM
lowercase__: Tuple = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
lowercase__: Optional[int] = defaultdict(_UpperCAmelCase )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Any = self._index.query(_UpperCAmelCase )
if code_key in self._index.keys:
print(F"""Duplicate key {code_key}""" )
return
self._index.insert(_UpperCAmelCase , _UpperCAmelCase )
if len(_UpperCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(_UpperCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(_UpperCAmelCase )
def _snake_case ( self ):
lowercase__: List[Any] = []
for base, duplicates in self._duplicate_clusters.items():
lowercase__: Dict = [base] + list(_UpperCAmelCase )
# reformat the cluster to be a list of dict
lowercase__: Union[str, Any] = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(_UpperCAmelCase )
return duplicate_clusters
def _snake_case ( self , _UpperCAmelCase ):
lowercase__: int = self.get_duplicate_clusters()
with open(_UpperCAmelCase , '''w''' ) as f:
json.dump(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Dict:
lowercase__, lowercase__: Union[str, Any] = element
lowercase__: Any = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> Union[str, Any]:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(__UpperCAmelCase , max_queue_size=1_0_0_0_0 ) , chunksize=1_0_0 , ):
if data is not None:
yield data
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
lowercase__: Optional[Any] = DuplicationIndex(duplication_jaccard_threshold=__UpperCAmelCase )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(__UpperCAmelCase ) ) , max_queue_size=1_0_0 ) ):
di.add(__UpperCAmelCase , __UpperCAmelCase )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> float:
lowercase__: Optional[Any] = get_tokens(__UpperCAmelCase )
lowercase__: Optional[Any] = get_tokens(__UpperCAmelCase )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
__A = None
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Optional[Any]:
lowercase__: Any = []
for elementa in cluster:
lowercase__: List[str] = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
lowercase__: Any = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(__UpperCAmelCase , __UpperCAmelCase ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
lowercase__: int = 1
extremes.append(__UpperCAmelCase )
return extremes
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
global _shared_dataset
lowercase__: Optional[int] = dataset
lowercase__: Union[str, Any] = []
lowercase__: str = partial(_find_cluster_extremes_shared , jaccard_threshold=__UpperCAmelCase )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
__UpperCAmelCase , __UpperCAmelCase , ) , total=len(__UpperCAmelCase ) , ):
extremes_list.append(__UpperCAmelCase )
return extremes_list
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase = 0.8_5 ) -> Tuple[Type[Dataset], List[List[Dict]]]:
lowercase__: Any = make_duplicate_clusters(__UpperCAmelCase , __UpperCAmelCase )
lowercase__: Union[str, Any] = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
lowercase__: List[str] = {}
lowercase__: int = find_extremes(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for extremes in extremes_clusters:
for element in extremes:
lowercase__: str = element
lowercase__: List[str] = duplicate_indices - set(extreme_dict.keys() )
lowercase__: List[str] = dataset.filter(lambda __UpperCAmelCase , __UpperCAmelCase : idx not in remove_indices , with_indices=__UpperCAmelCase )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
lowercase__: Optional[int] = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
lowercase__: Optional[int] = extreme_dict[element['''base_index''']]['''copies''']
print(F"""Original dataset size: {len(__UpperCAmelCase )}""" )
print(F"""Number of duplicate clusters: {len(__UpperCAmelCase )}""" )
print(F"""Files in duplicate cluster: {len(__UpperCAmelCase )}""" )
print(F"""Unique files in duplicate cluster: {len(__UpperCAmelCase )}""" )
print(F"""Filtered dataset size: {len(__UpperCAmelCase )}""" )
return ds_filter, duplicate_clusters
| 586 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = list(a__ )
SCREAMING_SNAKE_CASE : int = list(a__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for i in range(len(a__ ) ):
if lista[i] != lista[i]:
count += 1
SCREAMING_SNAKE_CASE : Any = '''_'''
if count > 1:
return False
else:
return "".join(a__ )
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = []
while True:
SCREAMING_SNAKE_CASE : Tuple = ['''$'''] * len(a__ )
SCREAMING_SNAKE_CASE : int = []
for i in range(len(a__ ) ):
for j in range(i + 1 , len(a__ ) ):
SCREAMING_SNAKE_CASE : Any = compare_string(binary[i] , binary[j] )
if k is False:
SCREAMING_SNAKE_CASE : List[Any] = '''*'''
SCREAMING_SNAKE_CASE : Optional[int] = '''*'''
temp.append('''X''' )
for i in range(len(a__ ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(a__ ) == 0:
return pi
SCREAMING_SNAKE_CASE : List[Any] = list(set(a__ ) )
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = []
for minterm in minterms:
SCREAMING_SNAKE_CASE : List[str] = ''''''
for _ in range(a__ ):
SCREAMING_SNAKE_CASE : Any = str(minterm % 2 ) + string
minterm //= 2
temp.append(a__ )
return temp
def UpperCAmelCase_( a__ , a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = list(a__ )
SCREAMING_SNAKE_CASE : Optional[Any] = list(a__ )
SCREAMING_SNAKE_CASE : Optional[Any] = 0
for i in range(len(a__ ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = []
SCREAMING_SNAKE_CASE : List[Any] = [0] * len(a__ )
for i in range(len(chart[0] ) ):
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = -1
for j in range(len(a__ ) ):
if chart[j][i] == 1:
count += 1
SCREAMING_SNAKE_CASE : int = j
if count == 1:
SCREAMING_SNAKE_CASE : Optional[Any] = 1
for i in range(len(a__ ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(a__ ) ):
SCREAMING_SNAKE_CASE : Optional[int] = 0
temp.append(prime_implicants[i] )
while True:
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : str = -1
SCREAMING_SNAKE_CASE : str = 0
for i in range(len(a__ ) ):
SCREAMING_SNAKE_CASE : List[str] = chart[i].count(1 )
if count_n > max_n:
SCREAMING_SNAKE_CASE : List[Any] = count_n
SCREAMING_SNAKE_CASE : Optional[int] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(a__ ) ):
SCREAMING_SNAKE_CASE : Optional[int] = 0
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [[0 for x in range(len(a__ ) )] for x in range(len(a__ ) )]
for i in range(len(a__ ) ):
SCREAMING_SNAKE_CASE : Any = prime_implicants[i].count('''_''' )
for j in range(len(a__ ) ):
if is_for_table(prime_implicants[i] , binary[j] , a__ ):
SCREAMING_SNAKE_CASE : Optional[int] = 1
return chart
def UpperCAmelCase_( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = int(input('''Enter the no. of variables\n''' ) )
SCREAMING_SNAKE_CASE : List[str] = [
float(a__ )
for x in input(
'''Enter the decimal representation of Minterms \'Spaces Separated\'\n''' ).split()
]
SCREAMING_SNAKE_CASE : Union[str, Any] = decimal_to_binary(a__ , a__ )
SCREAMING_SNAKE_CASE : Dict = check(a__ )
print('''Prime Implicants are:''' )
print(a__ )
SCREAMING_SNAKE_CASE : Tuple = prime_implicant_chart(a__ , a__ )
SCREAMING_SNAKE_CASE : Optional[Any] = selection(a__ , a__ )
print('''Essential Prime Implicants are:''' )
print(a__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 333 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : UNetaDModel
__SCREAMING_SNAKE_CASE : KarrasVeScheduler
def __init__( self , _lowerCamelCase , _lowerCamelCase ) ->Dict:
super().__init__()
self.register_modules(unet=_lowerCamelCase , scheduler=_lowerCamelCase )
@torch.no_grad()
def __call__( self , _lowerCamelCase = 1 , _lowerCamelCase = 50 , _lowerCamelCase = None , _lowerCamelCase = "pil" , _lowerCamelCase = True , **_lowerCamelCase , ) ->Union[Tuple, ImagePipelineOutput]:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.unet.config.sample_size
SCREAMING_SNAKE_CASE : List[str] = (batch_size, 3, img_size, img_size)
SCREAMING_SNAKE_CASE : Optional[int] = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
SCREAMING_SNAKE_CASE : Optional[int] = randn_tensor(_lowerCamelCase , generator=_lowerCamelCase , device=self.device ) * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(_lowerCamelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
# here sigma_t == t_i from the paper
SCREAMING_SNAKE_CASE : List[Any] = self.scheduler.schedule[t]
SCREAMING_SNAKE_CASE : Any = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self.scheduler.add_noise_to_input(_lowerCamelCase , _lowerCamelCase , generator=_lowerCamelCase )
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
SCREAMING_SNAKE_CASE : List[str] = (sigma_hat / 2) * model((sample_hat + 1) / 2 , sigma_hat / 2 ).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
SCREAMING_SNAKE_CASE : Optional[Any] = self.scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
SCREAMING_SNAKE_CASE : Union[str, Any] = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 , sigma_prev / 2 ).sample
SCREAMING_SNAKE_CASE : Optional[Any] = self.scheduler.step_correct(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , step_output.prev_sample , step_output['''derivative'''] , )
SCREAMING_SNAKE_CASE : Optional[int] = step_output.prev_sample
SCREAMING_SNAKE_CASE : Any = (sample / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Tuple = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE : Any = self.numpy_to_pil(_lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowerCamelCase )
| 333 | 1 |
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 ( lowerCAmelCase_ ):
"""simple docstring"""
def snake_case_ ( self : Any ):
__lowercase : List[str] = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type , pa.intaa() )
def snake_case_ ( self : Union[str, Any] ):
with self.assertRaises(_snake_case ):
__lowercase : Optional[int] = pa.array(TypedSequence([1, 2, 3] ) , type=pa.intaa() )
def snake_case_ ( self : Dict ):
with self.assertRaises(_snake_case ):
__lowercase : Dict = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''bool''' ) , type=Value('''int64''' ) ) )
def snake_case_ ( self : Union[str, Any] ):
__lowercase : int = pa.array(TypedSequence([1, 2, 3] , type=Value('''int32''' ) ) )
self.assertEqual(arr.type , pa.intaa() )
def snake_case_ ( self : List[str] ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
__lowercase : Dict = pa.array(TypedSequence(['''foo''', '''bar'''] , type=Value('''int64''' ) ) )
def snake_case_ ( self : str ):
__lowercase : Optional[Any] = pa.array(TypedSequence([1, 2, 3] , try_type=Value('''int32''' ) ) )
self.assertEqual(arr.type , pa.intaa() )
def snake_case_ ( self : Tuple ):
__lowercase : Optional[Any] = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=Value('''int64''' ) ) )
self.assertEqual(arr.type , pa.string() )
def snake_case_ ( self : Tuple ):
__lowercase : Optional[Any] = pa.array(TypedSequence([[[1, 2, 3]]] , type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) )
def snake_case_ ( self : Tuple ):
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
__lowercase : Tuple = pa.array(TypedSequence(['''foo''', '''bar'''] , type=ArrayaD((1, 3) , '''int64''' ) ) )
def snake_case_ ( self : Tuple ):
__lowercase : List[str] = pa.array(TypedSequence([[[1, 2, 3]]] , try_type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , ArrayaDExtensionType((1, 3) , '''int64''' ) )
def snake_case_ ( self : List[Any] ):
__lowercase : Dict = pa.array(TypedSequence(['''foo''', '''bar'''] , try_type=ArrayaD((1, 3) , '''int64''' ) ) )
self.assertEqual(arr.type , pa.string() )
@require_pil
def snake_case_ ( self : str ):
import PIL.Image
__lowercase : str = PIL.Image.fromarray(np.arange(10 , dtype=np.uinta ).reshape(2 , 5 ) )
with patch(
'''datasets.arrow_writer.cast_to_python_objects''' , side_effect=_snake_case ) as mock_cast_to_python_objects:
__lowercase : List[str] = pa.array(TypedSequence([{'''path''': None, '''bytes''': B'''image_bytes'''}, pil_image] , type=Image() ) )
__lowercase , __lowercase : Tuple = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('''optimize_list_casting''' , _snake_case )
self.assertFalse(kwargs['''optimize_list_casting'''] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
__lowercase : str = pa.BufferReader(__lowerCAmelCase ) if isinstance(__lowerCAmelCase , pa.Buffer ) else pa.memory_map(__lowerCAmelCase )
__lowercase : int = pa.ipc.open_stream(__lowerCAmelCase )
__lowercase : 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, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
__lowercase : Union[str, Any] = pa.BufferOutputStream()
__lowercase : Optional[Any] = pa.schema(__lowerCAmelCase ) if fields else None
with ArrowWriter(stream=__lowerCAmelCase , schema=__lowerCAmelCase , writer_batch_size=__lowerCAmelCase ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
__lowercase , __lowercase : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__lowercase : List[str] = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__lowerCAmelCase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def UpperCAmelCase_ ( ) -> Tuple:
__lowercase : Any = pa.BufferOutputStream()
__lowercase : Tuple = Features({'''labels''': ClassLabel(names=['''neg''', '''pos'''] )} )
with ArrowWriter(stream=__lowerCAmelCase , features=__lowerCAmelCase ) as writer:
writer.write({'''labels''': 0} )
writer.write({'''labels''': 1} )
__lowercase , __lowercase : Union[str, 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
__lowercase : Union[str, Any] = pa.BufferReader(output.getvalue() )
__lowercase : int = pa.ipc.open_stream(__lowerCAmelCase )
__lowercase : pa.Table = f.read_all()
__lowercase : 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(__lowerCAmelCase )
@pytest.mark.parametrize('''writer_batch_size''' , [None, 1, 10] )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
__lowercase : int = pa.BufferOutputStream()
with ArrowWriter(
stream=__lowerCAmelCase , writer_batch_size=__lowerCAmelCase , hash_salt='''split_name''' , check_duplicates=__lowerCAmelCase , ) as writer:
with pytest.raises(__lowerCAmelCase ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=[1, 2] )
__lowercase , __lowercase : Optional[int] = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> List[str]:
__lowercase : Union[str, Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=__lowerCAmelCase , writer_batch_size=__lowerCAmelCase , hash_salt='''split_name''' , check_duplicates=__lowerCAmelCase , ) as writer:
with pytest.raises(__lowerCAmelCase ):
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=10 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=10 )
__lowercase , __lowercase : Tuple = writer.finalize()
@pytest.mark.parametrize('''writer_batch_size''' , [None, 2, 10] )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Optional[Any]:
__lowercase : List[str] = pa.BufferOutputStream()
with ArrowWriter(
stream=__lowerCAmelCase , writer_batch_size=__lowerCAmelCase , hash_salt='''split_name''' , check_duplicates=__lowerCAmelCase , ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} , key=1 )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} , key=2 )
__lowercase , __lowercase : Any = 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, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
__lowercase : Optional[int] = pa.BufferOutputStream()
__lowercase : Union[str, Any] = pa.schema(__lowerCAmelCase ) if fields else None
with ArrowWriter(stream=__lowerCAmelCase , schema=__lowerCAmelCase , writer_batch_size=__lowerCAmelCase ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
writer.write_batch({'''col_1''': [], '''col_2''': []} )
__lowercase , __lowercase : Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__lowercase : Dict = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__lowerCAmelCase , 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, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
__lowercase : Tuple = pa.BufferOutputStream()
__lowercase : List[Any] = pa.schema(__lowerCAmelCase ) if fields else None
with ArrowWriter(stream=__lowerCAmelCase , schema=__lowerCAmelCase , writer_batch_size=__lowerCAmelCase ) as writer:
writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} ) )
__lowercase , __lowercase : List[str] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__lowercase : Dict = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__lowerCAmelCase , 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, 10] )
@pytest.mark.parametrize(
'''fields''' , [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
__lowercase : Optional[int] = pa.BufferOutputStream()
__lowercase : Optional[Any] = pa.schema(__lowerCAmelCase ) if fields else None
with ArrowWriter(stream=__lowerCAmelCase , schema=__lowerCAmelCase , writer_batch_size=__lowerCAmelCase ) 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]} ) )
__lowercase , __lowercase : Dict = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
__lowercase : str = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
assert writer._schema == pa.schema(__lowerCAmelCase , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def UpperCAmelCase_ ( ) -> Tuple:
with tempfile.TemporaryDirectory() as tmp_dir:
__lowercase : Optional[Any] = {'''col_1''': pa.string(), '''col_2''': pa.intaa()}
__lowercase : int = os.path.join(__lowerCAmelCase , '''test.arrow''' )
with ArrowWriter(path=__lowerCAmelCase , schema=pa.schema(__lowerCAmelCase ) ) as writer:
writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]} )
__lowercase , __lowercase : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(__lowerCAmelCase , metadata=writer._schema.metadata )
_check_output(__lowerCAmelCase , 1 )
def UpperCAmelCase_ ( __lowerCAmelCase ) -> int:
if pa.types.is_list(__lowerCAmelCase ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
if isinstance(lst[0] , __lowerCAmelCase ):
change_first_primitive_element_in_list(lst[0] , __lowerCAmelCase )
else:
__lowercase : int = 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 UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
__lowercase : str = pa.array(TypedSequence(__lowerCAmelCase , optimized_int_type=__lowerCAmelCase ) )
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 UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# in range
__lowercase : Optional[Any] = pa.array(OptimizedTypedSequence(__lowerCAmelCase , col=__lowerCAmelCase ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
__lowercase : str = copy.deepcopy(__lowerCAmelCase )
__lowercase : Optional[Any] = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(__lowerCAmelCase , __lowerCAmelCase )
__lowercase : List[str] = pa.array(OptimizedTypedSequence(__lowerCAmelCase , col=__lowerCAmelCase ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('''raise_exception''' , [False, True] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
__lowercase : Union[str, Any] = str(tmp_path / '''dataset-train.arrow''' )
try:
with ArrowWriter(path=__lowerCAmelCase ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def UpperCAmelCase_ ( __lowerCAmelCase ) -> Tuple:
__lowercase : int = '''mock://dataset-train.arrow'''
with ArrowWriter(path=__lowerCAmelCase , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(__lowerCAmelCase ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
__lowercase , __lowercase : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(__lowerCAmelCase )
def UpperCAmelCase_ ( ) -> List[Any]:
__lowercase : List[Any] = pa.BufferOutputStream()
with ParquetWriter(stream=__lowerCAmelCase ) as writer:
writer.write({'''col_1''': '''foo''', '''col_2''': 1} )
writer.write({'''col_1''': '''bar''', '''col_2''': 2} )
__lowercase , __lowercase : List[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
__lowercase : List[str] = pa.BufferReader(output.getvalue() )
__lowercase : pa.Table = pq.read_table(__lowerCAmelCase )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('''embed_local_files''' , [False, True] )
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
import PIL.Image
__lowercase : Tuple = str(tmp_path / '''test_image_rgb.jpg''' )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(__lowerCAmelCase , format='''png''' )
__lowercase : str = pa.BufferOutputStream()
with ParquetWriter(
stream=__lowerCAmelCase , features=Features({'''image''': Image()} ) , embed_local_files=__lowerCAmelCase ) as writer:
writer.write({'''image''': image_path} )
writer.finalize()
__lowercase : List[Any] = pa.BufferReader(output.getvalue() )
__lowercase : pa.Table = pq.read_table(__lowerCAmelCase )
__lowercase : List[str] = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['''image'''][0]['''path'''] , __lowerCAmelCase )
with open(__lowerCAmelCase , '''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 UpperCAmelCase_ ( ) -> Any:
__lowercase : Dict = pa.schema([pa.field('''col_1''' , pa.string() , nullable=__lowerCAmelCase )] )
__lowercase : Optional[Any] = pa.BufferOutputStream()
with ArrowWriter(stream=__lowerCAmelCase ) as writer:
writer._build_writer(inferred_schema=__lowerCAmelCase )
assert writer._schema == pa.schema([pa.field('''col_1''' , pa.string() )] )
| 509 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCAmelCase : Optional[int] = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__lowerCAmelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 509 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a :
def __init__( self: Union[str, Any] , UpperCamelCase_: List[str] , UpperCamelCase_: List[Any]=13 , UpperCamelCase_: int=[30, 30] , UpperCamelCase_: int=2 , UpperCamelCase_: Dict=3 , UpperCamelCase_: Dict=True , UpperCamelCase_: Optional[Any]=True , UpperCamelCase_: Any=32 , UpperCamelCase_: Dict=5 , UpperCamelCase_: List[str]=4 , UpperCamelCase_: List[Any]=37 , UpperCamelCase_: Dict="gelu" , UpperCamelCase_: str=0.1 , UpperCamelCase_: Any=0.1 , UpperCamelCase_: Optional[int]=10 , UpperCamelCase_: List[str]=0.02 , UpperCamelCase_: List[Any]=3 , UpperCamelCase_: List[str]=None , UpperCamelCase_: Optional[int]=8 , UpperCamelCase_: Tuple=10 , ) -> int:
"""simple docstring"""
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_labels
lowercase__ = scope
lowercase__ = n_targets
lowercase__ = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowercase__ = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowercase__ = num_patches + 1 + self.num_detection_tokens
def lowerCamelCase_ ( self: int ) -> Tuple:
"""simple docstring"""
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowercase__ = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowercase__ = []
for i in range(self.batch_size ):
lowercase__ = {}
lowercase__ = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=UpperCamelCase_ )
lowercase__ = torch.rand(self.n_targets , 4 , device=UpperCamelCase_ )
labels.append(UpperCamelCase_ )
lowercase__ = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self: str ) -> Dict:
"""simple docstring"""
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=UpperCamelCase_ , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def lowerCamelCase_ ( self: List[str] , UpperCamelCase_: Optional[Any] , UpperCamelCase_: Any , UpperCamelCase_: List[str] ) -> List[str]:
"""simple docstring"""
lowercase__ = YolosModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
lowercase__ = model(UpperCamelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def lowerCamelCase_ ( self: Tuple , UpperCamelCase_: Any , UpperCamelCase_: Optional[Any] , UpperCamelCase_: List[Any] ) -> List[Any]:
"""simple docstring"""
lowercase__ = YolosForObjectDetection(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
lowercase__ = model(pixel_values=UpperCamelCase_ )
lowercase__ = model(UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowercase__ = model(pixel_values=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def lowerCamelCase_ ( self: Optional[Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class _a ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
_lowercase : List[str] = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
_lowercase : List[str] = (
{'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
)
_lowercase : str = False
_lowercase : List[Any] = False
_lowercase : int = False
_lowercase : Dict = False
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase_: List[str] , UpperCamelCase_: Dict , UpperCamelCase_: List[Any]=False ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = super()._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowercase__ = []
for i in range(self.model_tester.batch_size ):
lowercase__ = {}
lowercase__ = torch.ones(
size=(self.model_tester.n_targets,) , device=UpperCamelCase_ , dtype=torch.long )
lowercase__ = torch.ones(
self.model_tester.n_targets , 4 , device=UpperCamelCase_ , dtype=torch.float )
labels.append(UpperCamelCase_ )
lowercase__ = labels
return inputs_dict
def lowerCamelCase_ ( self: Dict ) -> Tuple:
"""simple docstring"""
lowercase__ = YolosModelTester(self )
lowercase__ = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_ , hidden_size=37 )
def lowerCamelCase_ ( self: Dict ) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self: List[Any] ) -> List[Any]:
"""simple docstring"""
pass
def lowerCamelCase_ ( self: Tuple ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(UpperCamelCase_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase_ , nn.Linear ) )
def lowerCamelCase_ ( self: Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(UpperCamelCase_ )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase_ )
def lowerCamelCase_ ( self: List[str] ) -> List[Any]:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCamelCase_ ( self: Optional[int] ) -> int:
"""simple docstring"""
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = True
# in YOLOS, the seq_len is different
lowercase__ = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowercase__ = True
lowercase__ = False
lowercase__ = True
lowercase__ = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
lowercase__ = outputs.attentions
self.assertEqual(len(UpperCamelCase_ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowercase__ = True
lowercase__ = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
lowercase__ = outputs.attentions
self.assertEqual(len(UpperCamelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowercase__ = len(UpperCamelCase_ )
# Check attention is always last and order is fine
lowercase__ = True
lowercase__ = True
lowercase__ = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
lowercase__ = 1
self.assertEqual(out_len + added_hidden_states , len(UpperCamelCase_ ) )
lowercase__ = outputs.attentions
self.assertEqual(len(UpperCamelCase_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def lowerCamelCase_ ( self: Tuple ) -> List[Any]:
"""simple docstring"""
def check_hidden_states_output(UpperCamelCase_: List[str] , UpperCamelCase_: List[Any] , UpperCamelCase_: Optional[Any] ):
lowercase__ = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
lowercase__ = outputs.hidden_states
lowercase__ = getattr(
self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(UpperCamelCase_ ) , UpperCamelCase_ )
# YOLOS has a different seq_length
lowercase__ = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def lowerCamelCase_ ( self: Optional[int] ) -> int:
"""simple docstring"""
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*UpperCamelCase_ )
@slow
def lowerCamelCase_ ( self: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = YolosModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
def _a ( ):
"""simple docstring"""
lowercase__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class _a ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self: Tuple ) -> Optional[int]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''hustvl/yolos-small''' ) if is_vision_available() else None
@slow
def lowerCamelCase_ ( self: Tuple ) -> List[Any]:
"""simple docstring"""
lowercase__ = YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''' ).to(UpperCamelCase_ )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=UpperCamelCase_ , return_tensors='''pt''' ).to(UpperCamelCase_ )
# forward pass
with torch.no_grad():
lowercase__ = model(inputs.pixel_values )
# verify outputs
lowercase__ = torch.Size((1, 100, 92) )
self.assertEqual(outputs.logits.shape , UpperCamelCase_ )
lowercase__ = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] , device=UpperCamelCase_ , )
lowercase__ = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] , device=UpperCamelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , UpperCamelCase_ , atol=1E-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , UpperCamelCase_ , atol=1E-4 ) )
# verify postprocessing
lowercase__ = image_processor.post_process_object_detection(
UpperCamelCase_ , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowercase__ = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861] ).to(UpperCamelCase_ )
lowercase__ = [75, 75, 17, 63, 17]
lowercase__ = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495] ).to(UpperCamelCase_ )
self.assertEqual(len(results['''scores'''] ) , 5 )
self.assertTrue(torch.allclose(results['''scores'''] , UpperCamelCase_ , atol=1E-4 ) )
self.assertSequenceEqual(results['''labels'''].tolist() , UpperCamelCase_ )
self.assertTrue(torch.allclose(results['''boxes'''][0, :] , UpperCamelCase_ ) )
| 429 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(SCREAMING_SNAKE_CASE , n - 1 , SCREAMING_SNAKE_CASE ) * a) % mod
else:
lowercase__ = binary_exponentiation(SCREAMING_SNAKE_CASE , n / 2 , SCREAMING_SNAKE_CASE )
return (b * b) % mod
# a prime number
lowerCAmelCase = 701
lowerCAmelCase = 10_0000_0000
lowerCAmelCase = 10
# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)
print((a / b) % p == (a * b ** (p - 2)) % p)
| 429 | 1 |
'''simple docstring'''
from __future__ import annotations
import time
UpperCamelCase__ = list[tuple[int, int]]
UpperCamelCase__ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
UpperCamelCase__ = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , _A : int , _A : int , _A : int , _A : int , _A : Node | None ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = pos_x
UpperCAmelCase__ : Optional[int] = pos_y
UpperCAmelCase__ : Optional[int] = (pos_y, pos_x)
UpperCAmelCase__ : Optional[Any] = goal_x
UpperCAmelCase__ : Tuple = goal_y
UpperCAmelCase__ : Union[str, Any] = parent
class lowerCamelCase_ :
def __init__( self : int , _A : tuple[int, int] , _A : tuple[int, int] ):
'''simple docstring'''
UpperCAmelCase__ : int = Node(start[1] , start[0] , goal[1] , goal[0] , _A )
UpperCAmelCase__ : int = Node(goal[1] , goal[0] , goal[1] , goal[0] , _A )
UpperCAmelCase__ : int = [self.start]
UpperCAmelCase__ : List[str] = False
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
while self.node_queue:
UpperCAmelCase__ : Dict = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
UpperCAmelCase__ : Tuple = True
return self.retrace_path(_A )
UpperCAmelCase__ : Dict = self.get_successors(_A )
for node in successors:
self.node_queue.append(_A )
if not self.reached:
return [self.start.pos]
return None
def lowercase_ ( self : Optional[int] , _A : Node ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = []
for action in delta:
UpperCAmelCase__ : int = parent.pos_x + action[1]
UpperCAmelCase__ : str = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_A ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(_A , _A , self.target.pos_y , self.target.pos_x , _A ) )
return successors
def lowercase_ ( self : Any , _A : Node | None ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = node
UpperCAmelCase__ : Tuple = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
UpperCAmelCase__ : Dict = current_node.parent
path.reverse()
return path
class lowerCamelCase_ :
def __init__( self : int , _A : Optional[Any] , _A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = BreadthFirstSearch(_A , _A )
UpperCAmelCase__ : Dict = BreadthFirstSearch(_A , _A )
UpperCAmelCase__ : Union[str, Any] = False
def lowercase_ ( self : List[str] ):
'''simple docstring'''
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
UpperCAmelCase__ : Tuple = self.fwd_bfs.node_queue.pop(0 )
UpperCAmelCase__ : Optional[Any] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
UpperCAmelCase__ : Optional[Any] = True
return self.retrace_bidirectional_path(
_A , _A )
UpperCAmelCase__ : List[str] = current_bwd_node
UpperCAmelCase__ : Dict = current_fwd_node
UpperCAmelCase__ : str = {
self.fwd_bfs: self.fwd_bfs.get_successors(_A ),
self.bwd_bfs: self.bwd_bfs.get_successors(_A ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(_A )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def lowercase_ ( self : Dict , _A : Node , _A : Node ):
'''simple docstring'''
UpperCAmelCase__ : int = self.fwd_bfs.retrace_path(_A )
UpperCAmelCase__ : str = self.bwd_bfs.retrace_path(_A )
bwd_path.pop()
bwd_path.reverse()
UpperCAmelCase__ : str = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
UpperCamelCase__ = (0, 0)
UpperCamelCase__ = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
UpperCamelCase__ = time.time()
UpperCamelCase__ = BreadthFirstSearch(init, goal)
UpperCamelCase__ = bfs.search()
UpperCamelCase__ = time.time() - start_bfs_time
print('''Unidirectional BFS computation time : ''', bfs_time)
UpperCamelCase__ = time.time()
UpperCamelCase__ = BidirectionalBreadthFirstSearch(init, goal)
UpperCamelCase__ = bd_bfs.search()
UpperCamelCase__ = time.time() - start_bd_bfs_time
print('''Bidirectional BFS computation time : ''', bd_bfs_time)
| 75 |
'''simple docstring'''
# Copyright 2021 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 argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
UpperCamelCase__ = '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''
def a__ ( ) -> List[str]:
UpperCAmelCase__ : Optional[int] = _ask_options(
'''In which compute environment are you running?''' , ['''This machine''', '''AWS (Amazon SageMaker)'''] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
UpperCAmelCase__ : Any = get_sagemaker_input()
else:
UpperCAmelCase__ : List[str] = get_cluster_input()
return config
def a__ ( lowerCAmelCase__=None ) -> List[Any]:
if subparsers is not None:
UpperCAmelCase__ : Union[str, Any] = subparsers.add_parser('''config''' , description=lowerCAmelCase__ )
else:
UpperCAmelCase__ : Dict = argparse.ArgumentParser('''Accelerate config command''' , description=lowerCAmelCase__ )
parser.add_argument(
'''--config_file''' , default=lowerCAmelCase__ , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , )
if subparsers is not None:
parser.set_defaults(func=lowerCAmelCase__ )
return parser
def a__ ( lowerCAmelCase__ ) -> List[Any]:
UpperCAmelCase__ : List[Any] = get_user_input()
if args.config_file is not None:
UpperCAmelCase__ : Any = args.config_file
else:
if not os.path.isdir(lowerCAmelCase__ ):
os.makedirs(lowerCAmelCase__ )
UpperCAmelCase__ : int = default_yaml_config_file
if config_file.endswith('''.json''' ):
config.to_json_file(lowerCAmelCase__ )
else:
config.to_yaml_file(lowerCAmelCase__ )
print(F"""accelerate configuration saved at {config_file}""" )
def a__ ( ) -> str:
UpperCAmelCase__ : Optional[int] = config_command_parser()
UpperCAmelCase__ : Any = parser.parse_args()
config_command(lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 75 | 1 |
def _a ( __lowercase ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_snake_case = int(input('Enter number: ').strip())
print(F'''{number} is {"" if perfect(number) else "not "}a Perfect Number.''')
| 567 |
_snake_case = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_snake_case = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_snake_case = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def _a ( __lowercase , __lowercase , __lowercase ) -> str:
"""simple docstring"""
assert len(str(__lowercase ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
__UpperCamelCase = year // 100
__UpperCamelCase = (5 * (century % 4) + 2) % 7
__UpperCamelCase = year % 100
__UpperCamelCase = centurian % 12
__UpperCamelCase = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
__UpperCamelCase = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
else DOOMSDAY_LEAP[month - 1]
)
__UpperCamelCase = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 567 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=2, __a=24, __a=16, __a=True, __a=True, __a=32, __a=5, __a=4, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=10, __a=0.02, __a=None, __a=2, __a=2, ):
'''simple docstring'''
_lowerCAmelCase : int = parent
_lowerCAmelCase : str = batch_size
_lowerCAmelCase : Dict = patch_size
_lowerCAmelCase : Optional[int] = max_length
_lowerCAmelCase : Any = num_mel_bins
_lowerCAmelCase : List[Any] = is_training
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Dict = hidden_size
_lowerCAmelCase : Any = num_hidden_layers
_lowerCAmelCase : Dict = num_attention_heads
_lowerCAmelCase : Optional[Any] = intermediate_size
_lowerCAmelCase : Tuple = hidden_act
_lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
_lowerCAmelCase : Dict = attention_probs_dropout_prob
_lowerCAmelCase : Any = type_sequence_label_size
_lowerCAmelCase : Dict = initializer_range
_lowerCAmelCase : List[Any] = scope
_lowerCAmelCase : Any = frequency_stride
_lowerCAmelCase : str = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
_lowerCAmelCase : List[Any] = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
_lowerCAmelCase : List[str] = (self.max_length - self.patch_size) // self.time_stride + 1
_lowerCAmelCase : Tuple = frequency_out_dimension * time_out_dimension
_lowerCAmelCase : List[str] = num_patches + 2
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins])
_lowerCAmelCase : str = None
if self.use_labels:
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Tuple = self.get_config()
return config, input_values, labels
def snake_case__ ( self):
'''simple docstring'''
return ASTConfig(
patch_size=self.patch_size, max_length=self.max_length, num_mel_bins=self.num_mel_bins, 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, is_decoder=__a, initializer_range=self.initializer_range, frequency_stride=self.frequency_stride, time_stride=self.time_stride, )
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : int = ASTModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : List[Any] = model(__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Any = config_and_inputs
_lowerCAmelCase : List[str] = {"input_values": input_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , unittest.TestCase):
lowerCamelCase__ = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = ASTModelTester(self)
_lowerCAmelCase : Optional[int] = ConfigTester(self, config_class=__a, has_text_modality=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="AST does not use inputs_embeds")
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase : List[str] = model_class(__a)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
_lowerCAmelCase : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__a, nn.Linear))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase : Optional[Any] = model_class(__a)
_lowerCAmelCase : Any = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase : Dict = [*signature.parameters.keys()]
_lowerCAmelCase : Dict = ["input_values"]
self.assertListEqual(arg_names[:1], __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : Any = ASTModel.from_pretrained(__a)
self.assertIsNotNone(__a)
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint" , filename="sample_audio.flac" , repo_type="dataset" )
_lowerCAmelCase , _lowerCAmelCase : List[str] = torchaudio.load(_lowerCamelCase )
return audio, sampling_rate
@require_torch
@require_torchaudio
class UpperCAmelCase_ ( unittest.TestCase):
@cached_property
def snake_case__ ( self):
'''simple docstring'''
return (
ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")
if is_torchaudio_available()
else None
)
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.default_feature_extractor
_lowerCAmelCase : Union[str, Any] = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593").to(__a)
_lowerCAmelCase : Tuple = self.default_feature_extractor
_lowerCAmelCase , _lowerCAmelCase : Dict = prepare_audio()
_lowerCAmelCase : int = audio.squeeze().numpy()
_lowerCAmelCase : Union[str, Any] = feature_extractor(__a, sampling_rate=__a, return_tensors="pt").to(__a)
# forward pass
with torch.no_grad():
_lowerCAmelCase : int = model(**__a)
# verify the logits
_lowerCAmelCase : List[str] = torch.Size((1, 527))
self.assertEqual(outputs.logits.shape, __a)
_lowerCAmelCase : Union[str, Any] = torch.tensor([-0.8_760, -7.0_042, -8.6_602]).to(__a)
self.assertTrue(torch.allclose(outputs.logits[0, :3], __a, atol=1E-4))
| 500 |
from math import ceil, sqrt
def A ( _lowerCamelCase = 1_000_000 ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_lowerCAmelCase : Optional[int] = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
_lowerCAmelCase : Tuple = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f'''{solution() = }''')
| 500 | 1 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_A : Optional[int] = ''''''
if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
class a ( tr.AbstractTransform ):
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str = " " ):
__lowerCamelCase: List[Any] = sentence_delimiter
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : str ):
return list(SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : str , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCamelCase: Any = []
for sent_idx, sentence in enumerate(SCREAMING_SNAKE_CASE_ ):
chars.extend(self.process_string(SCREAMING_SNAKE_CASE_ ) )
if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(SCREAMING_SNAKE_CASE_ ) - 1:
chars.append(self.sentence_delimiter )
return chars
_A : List[Any] = tr.Compose(
[tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
)
else:
_A : List[Any] = tr.Compose(
[
tr.RemoveMultipleSpaces(),
tr.Strip(),
tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
tr.ReduceToListOfListOfChars(),
]
)
_A : Optional[Any] = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
_A : List[str] = '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.
CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.
Character error rate can be computed as:
CER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).
CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''
_A : List[Any] = '''
Computes CER score of transcribed segments against references.
Args:
references: list of references for each speech input.
predictions: list of transcribtions to score.
concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
(float): the character error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> cer = datasets.load_metric("cer")
>>> cer_score = cer.compute(predictions=predictions, references=references)
>>> print(cer_score)
0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class a ( datasets.Metric ):
def SCREAMING_SNAKE_CASE__ ( self : str ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/jitsi/jiwer/"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
"""https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates""",
] , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : str=False ):
if concatenate_texts:
return jiwer.compute_measures(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , truth_transform=SCREAMING_SNAKE_CASE_ , hypothesis_transform=SCREAMING_SNAKE_CASE_ , )["wer"]
__lowerCamelCase: Optional[int] = 0
__lowerCamelCase: Any = 0
for prediction, reference in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase: Tuple = jiwer.compute_measures(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , truth_transform=SCREAMING_SNAKE_CASE_ , hypothesis_transform=SCREAMING_SNAKE_CASE_ , )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 189 |
from manim import *
class a ( _UpperCAmelCase ):
def SCREAMING_SNAKE_CASE__ ( self : int ):
__lowerCamelCase: int = Rectangle(height=0.5 , width=0.5 )
__lowerCamelCase: List[str] = Rectangle(height=0.25 , width=0.25 )
__lowerCamelCase: Optional[int] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
__lowerCamelCase: str = [mem.copy() for i in range(6 )]
__lowerCamelCase: Dict = [mem.copy() for i in range(6 )]
__lowerCamelCase: Union[str, Any] = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: Tuple = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: Optional[int] = VGroup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: Dict = Text("""CPU""" , font_size=24 )
__lowerCamelCase: Any = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Optional[int] = [mem.copy() for i in range(4 )]
__lowerCamelCase: Optional[int] = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: Dict = Text("""GPU""" , font_size=24 )
__lowerCamelCase: Dict = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
gpu.move_to([-1, -1, 0] )
self.add(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Any = [mem.copy() for i in range(6 )]
__lowerCamelCase: Dict = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: List[Any] = Text("""Model""" , font_size=24 )
__lowerCamelCase: Any = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
model.move_to([3, -1.0, 0] )
self.add(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Tuple = []
__lowerCamelCase: Any = []
__lowerCamelCase: int = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE_ ):
rect.set_stroke(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: int = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(SCREAMING_SNAKE_CASE_ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=SCREAMING_SNAKE_CASE_ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=SCREAMING_SNAKE_CASE_ , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=SCREAMING_SNAKE_CASE_ , buff=0.0 )
self.add(SCREAMING_SNAKE_CASE_ )
model_cpu_arr.append(SCREAMING_SNAKE_CASE_ )
self.add(*SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: List[Any] = [mem.copy() for i in range(6 )]
__lowerCamelCase: Any = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: Tuple = Text("""Loaded Checkpoint""" , font_size=24 )
__lowerCamelCase: Tuple = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
checkpoint.move_to([3, 0.5, 0] )
self.add(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: List[Any] = []
__lowerCamelCase: Optional[int] = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase: Optional[int] = fill.copy().set_fill(SCREAMING_SNAKE_CASE_ , opacity=0.7 )
target.move_to(SCREAMING_SNAKE_CASE_ )
ckpt_arr.append(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: List[Any] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(SCREAMING_SNAKE_CASE_ )
self.add(*SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Optional[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
__lowerCamelCase: Dict = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Optional[int] = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(SCREAMING_SNAKE_CASE_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase: Tuple = MarkupText(
F'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.' , font_size=24 , )
step_a.move_to([2, 2, 0] )
__lowerCamelCase: List[Any] = [meta_mem.copy() for i in range(6 )]
__lowerCamelCase: Optional[int] = [meta_mem.copy() for i in range(6 )]
__lowerCamelCase: str = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: Optional[int] = VGroup(*SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: List[str] = VGroup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0 )
__lowerCamelCase: Dict = Text("""Disk""" , font_size=24 )
__lowerCamelCase: Any = Group(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).arrange(SCREAMING_SNAKE_CASE_ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE_ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(SCREAMING_SNAKE_CASE_ , run_time=3 ) , Write(SCREAMING_SNAKE_CASE_ , run_time=1 ) , Create(SCREAMING_SNAKE_CASE_ , run_time=1 ) )
__lowerCamelCase: int = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE_ ):
__lowerCamelCase: Optional[int] = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(SCREAMING_SNAKE_CASE_ , run_time=1.5 ) )
self.play(*SCREAMING_SNAKE_CASE_ )
self.play(FadeOut(SCREAMING_SNAKE_CASE_ ) )
__lowerCamelCase: List[Any] = MarkupText(F'Then, the checkpoint is removed from memory\nthrough garbage collection.' , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(SCREAMING_SNAKE_CASE_ , run_time=3 ) )
self.play(
FadeOut(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ ) , )
self.wait()
| 189 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class a ( unittest.TestCase ):
_snake_case : Any = ViTImageProcessor if is_vision_available() else None
@property
def lowerCAmelCase_ ( self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = (3, 32, 128)
_UpperCAmelCase = tempfile.mkdtemp()
# fmt: off
_UpperCAmelCase = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """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"""]
# fmt: on
_UpperCAmelCase = dict(zip(__SCREAMING_SNAKE_CASE , range(len(__SCREAMING_SNAKE_CASE ) ) ) )
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + """\n""" )
_UpperCAmelCase = {
"""do_normalize""": False,
"""do_resize""": True,
"""image_processor_type""": """ViTImageProcessor""",
"""resample""": 3,
"""size""": {"""height""": 32, """width""": 128},
}
_UpperCAmelCase = os.path.join(self.tmpdirname , __SCREAMING_SNAKE_CASE )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( self : List[Any] , **__lowerCAmelCase : Dict ):
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( self : Optional[Any] , **__lowerCAmelCase : Any ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( self : Tuple ):
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_UpperCAmelCase = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
_UpperCAmelCase = Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE , 0 , -1 ) )
return image_input
def lowerCAmelCase_ ( self : str ):
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=__SCREAMING_SNAKE_CASE )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , __SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( self : Tuple ):
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_UpperCAmelCase = self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0 )
_UpperCAmelCase = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__SCREAMING_SNAKE_CASE , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , __SCREAMING_SNAKE_CASE )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( self : int ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = image_processor(__SCREAMING_SNAKE_CASE , return_tensors="""np""" )
_UpperCAmelCase = processor(images=__SCREAMING_SNAKE_CASE , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = """test"""
_UpperCAmelCase = processor(text=__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase_ ( self : str ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = """test"""
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] )
# test if it raises when no input is passed
with pytest.raises(__SCREAMING_SNAKE_CASE ):
processor()
def lowerCAmelCase_ ( self : Tuple ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase = processor.char_decode(__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = [seq.replace(""" """ , """""" ) for seq in decoded_tok]
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = None
_UpperCAmelCase = self.prepare_image_inputs()
_UpperCAmelCase = processor(text=__SCREAMING_SNAKE_CASE , images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = self.get_image_processor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE , image_processor=__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = torch.randn(1 , 27 , 38 )
_UpperCAmelCase = torch.randn(1 , 27 , 5_0257 )
_UpperCAmelCase = torch.randn(1 , 27 , 3_0522 )
_UpperCAmelCase = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )
| 277 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class A__:
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any]=3 , __SCREAMING_SNAKE_CASE : Union[str, Any]=32 , __SCREAMING_SNAKE_CASE : List[Any]=3 , __SCREAMING_SNAKE_CASE : int=10 , __SCREAMING_SNAKE_CASE : List[Any]=[8, 16, 32, 64] , __SCREAMING_SNAKE_CASE : str=[1, 1, 2, 1] , __SCREAMING_SNAKE_CASE : Optional[Any]=True , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : List[str]="relu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : List[Any]=None , __SCREAMING_SNAKE_CASE : Dict=["stage2", "stage3", "stage4"] , __SCREAMING_SNAKE_CASE : Optional[Any]=[2, 3, 4] , __SCREAMING_SNAKE_CASE : int=1 , ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = parent
__SCREAMING_SNAKE_CASE = batch_size
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = embeddings_size
__SCREAMING_SNAKE_CASE = hidden_sizes
__SCREAMING_SNAKE_CASE = depths
__SCREAMING_SNAKE_CASE = is_training
__SCREAMING_SNAKE_CASE = use_labels
__SCREAMING_SNAKE_CASE = hidden_act
__SCREAMING_SNAKE_CASE = num_labels
__SCREAMING_SNAKE_CASE = scope
__SCREAMING_SNAKE_CASE = len(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = out_features
__SCREAMING_SNAKE_CASE = out_indices
__SCREAMING_SNAKE_CASE = num_groups
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE = None
if self.use_labels:
__SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def _a ( self : Any ) -> str:
"""simple docstring"""
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def _a ( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[str] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BitModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.num_labels
__SCREAMING_SNAKE_CASE = BitForImageClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BitBackbone(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = BitBackbone(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : int ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = config_and_inputs
__SCREAMING_SNAKE_CASE = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A__( __magic_name__ , __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowerCAmelCase = (
{'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BitModelTester(self )
__SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
pass
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE )
def _a ( self : int ) -> str:
"""simple docstring"""
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE = model_class(config=__SCREAMING_SNAKE_CASE )
for name, module in model.named_modules():
if isinstance(__SCREAMING_SNAKE_CASE , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
def _a ( self : int ) -> Dict:
"""simple docstring"""
def check_hidden_states_output(__SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] ):
__SCREAMING_SNAKE_CASE = model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE = layer_type
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE = True
check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
pass
def _a ( self : Optional[Any] ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE = BitModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def _a ( ) -> List[Any]:
__SCREAMING_SNAKE_CASE = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class A__( unittest.TestCase ):
@cached_property
def _a ( self : Dict ) -> str:
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.default_image_processor
__SCREAMING_SNAKE_CASE = prepare_img()
__SCREAMING_SNAKE_CASE = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(__SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
__SCREAMING_SNAKE_CASE = model(**__SCREAMING_SNAKE_CASE )
# verify the logits
__SCREAMING_SNAKE_CASE = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = torch.tensor([[-0.65_26, -0.52_63, -1.43_98]] ).to(__SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 ) )
@require_torch
class A__( __magic_name__ , unittest.TestCase ):
lowerCAmelCase = (BitBackbone,) if is_torch_available() else ()
lowerCAmelCase = BitConfig
lowerCAmelCase = False
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = BitModelTester(self )
| 482 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
),
# See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class snake_case_ ( A__ ):
"""simple docstring"""
__lowerCAmelCase : Tuple ='''trocr'''
__lowerCAmelCase : Union[str, Any] =['''past_key_values''']
__lowerCAmelCase : Optional[Any] ={
'''num_attention_heads''': '''decoder_attention_heads''',
'''hidden_size''': '''d_model''',
'''num_hidden_layers''': '''decoder_layers''',
}
def __init__( self , UpperCamelCase=5_02_65 , UpperCamelCase=10_24 , UpperCamelCase=12 , UpperCamelCase=16 , UpperCamelCase=40_96 , UpperCamelCase="gelu" , UpperCamelCase=5_12 , UpperCamelCase=0.1 , UpperCamelCase=0.0 , UpperCamelCase=0.0 , UpperCamelCase=2 , UpperCamelCase=0.0_2 , UpperCamelCase=0.0 , UpperCamelCase=True , UpperCamelCase=False , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=1 , UpperCamelCase=0 , UpperCamelCase=2 , **UpperCamelCase , ):
lowerCamelCase__ = vocab_size
lowerCamelCase__ = d_model
lowerCamelCase__ = decoder_layers
lowerCamelCase__ = decoder_attention_heads
lowerCamelCase__ = decoder_ffn_dim
lowerCamelCase__ = activation_function
lowerCamelCase__ = max_position_embeddings
lowerCamelCase__ = dropout
lowerCamelCase__ = attention_dropout
lowerCamelCase__ = activation_dropout
lowerCamelCase__ = init_std
lowerCamelCase__ = decoder_layerdrop
lowerCamelCase__ = use_cache
lowerCamelCase__ = scale_embedding
lowerCamelCase__ = use_learned_position_embeddings
lowerCamelCase__ = layernorm_embedding
super().__init__(
pad_token_id=UpperCamelCase , bos_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , decoder_start_token_id=UpperCamelCase , **UpperCamelCase , )
| 426 |
'''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
lowerCAmelCase_ = False
try:
lowerCAmelCase_ = _is_package_available("google.colab")
except ModuleNotFoundError:
pass
@input.register
class snake_case_ :
"""simple docstring"""
def __init__( self , UpperCamelCase = None , UpperCamelCase = []):
lowerCamelCase__ = 0
lowerCamelCase__ = choices
lowerCamelCase__ = prompt
if sys.platform == "win32":
lowerCamelCase__ = "*"
else:
lowerCamelCase__ = "➔ "
def __UpperCAmelCase ( self , UpperCamelCase , UpperCamelCase = ""):
if sys.platform != "win32":
writeColor(self.choices[index] , 32 , UpperCamelCase)
else:
forceWrite(self.choices[index] , UpperCamelCase)
def __UpperCAmelCase ( self , UpperCamelCase):
if index == self.position:
forceWrite(f""" {self.arrow_char} """)
self.write_choice(UpperCamelCase)
else:
forceWrite(f""" {self.choices[index]}""")
reset_cursor()
def __UpperCAmelCase ( self , UpperCamelCase , UpperCamelCase = 1):
lowerCamelCase__ = 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(UpperCamelCase)
move_cursor(UpperCamelCase , direction.name)
self.print_choice(self.position)
@input.mark(KEYMAP["up"])
def __UpperCAmelCase ( self):
self.move_direction(Direction.UP)
@input.mark(KEYMAP["down"])
def __UpperCAmelCase ( self):
self.move_direction(Direction.DOWN)
@input.mark(KEYMAP["newline"])
def __UpperCAmelCase ( self):
move_cursor(len(self.choices) - self.position , "DOWN")
return self.position
@input.mark(KEYMAP["interrupt"])
def __UpperCAmelCase ( self):
move_cursor(len(self.choices) - self.position , "DOWN")
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(UpperCamelCase)] for number in range(10)])
def __UpperCAmelCase ( self):
lowerCamelCase__ = int(chr(self.current_selection))
lowerCamelCase__ = 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 , UpperCamelCase)
else:
return
else:
return
def __UpperCAmelCase ( self , UpperCamelCase = 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")
lowerCamelCase__ = default_choice
for i in range(len(self.choices)):
self.print_choice(UpperCamelCase)
forceWrite("\n")
move_cursor(len(self.choices) - self.position , "UP")
with cursor.hide():
while True:
if in_colab:
try:
lowerCamelCase__ = int(builtins.input())
except ValueError:
lowerCamelCase__ = default_choice
else:
lowerCamelCase__ = 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(UpperCamelCase , "\n")
return choice
| 426 | 1 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def A_( A : List[str]):
if is_torch_version('<' , '2.0.0') or not hasattr(A , '_dynamo'):
return False
return isinstance(A , torch._dynamo.eval_frame.OptimizedModule)
def A_( A : Union[str, Any] , A : bool = True):
UpperCamelCase = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
UpperCamelCase = is_compiled_module(A)
if is_compiled:
UpperCamelCase = model
UpperCamelCase = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(A , A):
UpperCamelCase = model.module
if not keep_fpaa_wrapper:
UpperCamelCase = getattr(A , 'forward')
UpperCamelCase = model.__dict__.pop('_original_forward' , A)
if original_forward is not None:
while hasattr(A , '__wrapped__'):
UpperCamelCase = forward.__wrapped__
if forward == original_forward:
break
UpperCamelCase = forward
if getattr(A , '_converted_to_transformer_engine' , A):
convert_model(A , to_transformer_engine=A)
if is_compiled:
UpperCamelCase = model
UpperCamelCase = compiled_model
return model
def A_( ):
PartialState().wait_for_everyone()
def A_( A : Dict , A : int):
if PartialState().distributed_type == DistributedType.TPU:
xm.save(A , A)
elif PartialState().local_process_index == 0:
torch.save(A , A)
@contextmanager
def A_( **A : Dict):
for key, value in kwargs.items():
UpperCamelCase = str(A)
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def A_( A : int):
if not hasattr(A , '__qualname__') and not hasattr(A , '__name__'):
UpperCamelCase = getattr(A , '__class__' , A)
if hasattr(A , '__qualname__'):
return obj.__qualname__
if hasattr(A , '__name__'):
return obj.__name__
return str(A)
def A_( A : Tuple , A : List[str]):
for key, value in source.items():
if isinstance(A , A):
UpperCamelCase = destination.setdefault(A , {})
merge_dicts(A , A)
else:
UpperCamelCase = value
return destination
def A_( A : int = None):
if port is None:
UpperCamelCase = 2_9500
with socket.socket(socket.AF_INET , socket.SOCK_STREAM) as s:
return s.connect_ex(('localhost', port)) == 0
| 3 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = """unispeech-sat"""
def __init__( self , A_=32 , A_=768 , A_=12 , A_=12 , A_=3072 , A_="gelu" , A_=0.1 , A_=0.1 , A_=0.1 , A_=0.0 , A_=0.0 , A_=0.1 , A_=0.1 , A_=0.02 , A_=1e-5 , A_="group" , A_="gelu" , A_=(512, 512, 512, 512, 512, 512, 512) , A_=(5, 2, 2, 2, 2, 2, 2) , A_=(10, 3, 3, 3, 3, 2, 2) , A_=False , A_=128 , A_=16 , A_=False , A_=True , A_=0.05 , A_=10 , A_=2 , A_=0.0 , A_=10 , A_=0 , A_=320 , A_=2 , A_=0.1 , A_=100 , A_=256 , A_=256 , A_=0.1 , A_="mean" , A_=False , A_=False , A_=256 , A_=(512, 512, 512, 512, 1500) , A_=(5, 3, 3, 1, 1) , A_=(1, 2, 3, 1, 1) , A_=512 , A_=0 , A_=1 , A_=2 , A_=504 , **A_ , )-> Tuple:
'''simple docstring'''
super().__init__(**A_ , pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ )
UpperCamelCase = hidden_size
UpperCamelCase = feat_extract_norm
UpperCamelCase = feat_extract_activation
UpperCamelCase = list(A_ )
UpperCamelCase = list(A_ )
UpperCamelCase = list(A_ )
UpperCamelCase = conv_bias
UpperCamelCase = num_conv_pos_embeddings
UpperCamelCase = num_conv_pos_embedding_groups
UpperCamelCase = len(self.conv_dim )
UpperCamelCase = num_hidden_layers
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = num_attention_heads
UpperCamelCase = hidden_dropout
UpperCamelCase = attention_dropout
UpperCamelCase = activation_dropout
UpperCamelCase = feat_proj_dropout
UpperCamelCase = final_dropout
UpperCamelCase = layerdrop
UpperCamelCase = layer_norm_eps
UpperCamelCase = initializer_range
UpperCamelCase = vocab_size
UpperCamelCase = num_clusters
UpperCamelCase = do_stable_layer_norm
UpperCamelCase = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCamelCase = apply_spec_augment
UpperCamelCase = mask_time_prob
UpperCamelCase = mask_time_length
UpperCamelCase = mask_time_min_masks
UpperCamelCase = mask_feature_prob
UpperCamelCase = mask_feature_length
UpperCamelCase = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
UpperCamelCase = num_codevectors_per_group
UpperCamelCase = num_codevector_groups
UpperCamelCase = contrastive_logits_temperature
UpperCamelCase = feat_quantizer_dropout
UpperCamelCase = num_negatives
UpperCamelCase = codevector_dim
UpperCamelCase = proj_codevector_dim
UpperCamelCase = diversity_loss_weight
# ctc loss
UpperCamelCase = ctc_loss_reduction
UpperCamelCase = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCamelCase = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCamelCase = list(A_ )
UpperCamelCase = list(A_ )
UpperCamelCase = list(A_ )
UpperCamelCase = xvector_output_dim
@property
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 3 | 1 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase ( lowercase_ , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[Any] = TransfoXLTokenizer
__SCREAMING_SNAKE_CASE : List[Any] = False
__SCREAMING_SNAKE_CASE : Union[str, Any] = False
def a ( self ):
super().setUp()
snake_case_ = [
'<unk>',
'[CLS]',
'[SEP]',
'want',
'unwanted',
'wa',
'un',
'running',
',',
'low',
'l',
]
snake_case_ = 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 , **snake_case ):
snake_case_ = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **snake_case )
def a ( self , snake_case ):
snake_case_ = '<unk> UNwanted , running'
snake_case_ = '<unk> unwanted, running'
return input_text, output_text
def a ( self ):
snake_case_ = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=snake_case )
snake_case_ = tokenizer.tokenize('<unk> UNwanted , running' )
self.assertListEqual(snake_case , ['<unk>', 'unwanted', ',', 'running'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , [0, 4, 8, 7] )
def a ( self ):
snake_case_ = TransfoXLTokenizer(lower_case=snake_case )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
def a ( self ):
snake_case_ = TransfoXLTokenizer(lower_case=snake_case )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def a ( self ):
snake_case_ = TransfoXLTokenizer(lower_case=snake_case )
snake_case_ = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
snake_case_ = [
'Hello',
'(',
'bracket',
')',
'and',
'side',
'@-@',
'scrolled',
'[',
'and',
']',
'Henry',
'\'s',
'$',
'5',
'@,@',
'000',
'with',
'3',
'@.@',
'34',
'm',
'.',
'What',
'\'s',
'up',
'!',
'?',
]
self.assertListEqual(tokenizer.tokenize(snake_case ) , snake_case )
self.assertEqual(tokenizer.convert_tokens_to_string(snake_case ) , snake_case )
def a ( self ):
snake_case_ = self.get_tokenizer()
snake_case_ = len(snake_case )
tokenizer.add_tokens(['new1', 'new2'] )
tokenizer.move_added_token('new1' , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(snake_case ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('new1' ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , 'new1' )
| 108 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class lowercase :
__SCREAMING_SNAKE_CASE : int
__SCREAMING_SNAKE_CASE : Node | None = None
__SCREAMING_SNAKE_CASE : Node | None = None
def __lowerCamelCase ( ):
'''simple docstring'''
snake_case_ = Node(1 )
snake_case_ = Node(2 )
snake_case_ = Node(3 )
snake_case_ = Node(4 )
snake_case_ = Node(5 )
return tree
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = []
if root is None:
return output
snake_case_ = deque([root] )
while process_queue:
snake_case_ = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = []
def populate_output(UpperCamelCase__ , UpperCamelCase__ ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(UpperCamelCase__ , UpperCamelCase__ )
return output
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = []
def populate_output(UpperCamelCase__ , UpperCamelCase__ ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(UpperCamelCase__ , UpperCamelCase__ )
return output
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if root is None:
return []
snake_case_ = []
snake_case_ = 0
snake_case_ = height(UpperCamelCase__ )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(UpperCamelCase__ , UpperCamelCase__ ) )
snake_case_ = 1
else:
output.append(get_nodes_from_right_to_left(UpperCamelCase__ , UpperCamelCase__ ) )
snake_case_ = 0
return output
def __lowerCamelCase ( ): # Main function for testing.
'''simple docstring'''
snake_case_ = make_tree()
print(F'''In-order Traversal: {inorder(UpperCamelCase__ )}''' )
print(F'''Pre-order Traversal: {preorder(UpperCamelCase__ )}''' )
print(F'''Post-order Traversal: {postorder(UpperCamelCase__ )}''' , '\n' )
print(F'''Height of Tree: {height(UpperCamelCase__ )}''' , '\n' )
print('Complete Level Order Traversal: ' )
print(level_order(UpperCamelCase__ ) , '\n' )
print('Level-wise order Traversal: ' )
for level in range(1 , height(UpperCamelCase__ ) + 1 ):
print(F'''Level {level}:''' , get_nodes_from_left_to_right(UpperCamelCase__ , level=UpperCamelCase__ ) )
print('\nZigZag order Traversal: ' )
print(zigzag(UpperCamelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 108 | 1 |
import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler
_UpperCAmelCase : List[Any] = 16
_UpperCAmelCase : Optional[Any] = 32
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ = 16 , UpperCamelCase__ = "bert-base-cased" ):
'''simple docstring'''
snake_case_ = AutoTokenizer.from_pretrained(a__ )
snake_case_ = load_dataset('glue' , 'mrpc' )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=a__ , max_length=a__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
snake_case_ = datasets.map(
a__ , batched=a__ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=a__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(UpperCamelCase__ ):
# 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(a__ , padding='max_length' , max_length=128 , return_tensors='pt' )
return tokenizer.pad(a__ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
snake_case_ = DataLoader(
tokenized_datasets['train'] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
snake_case_ = DataLoader(
tokenized_datasets['validation'] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
return train_dataloader, eval_dataloader
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ = config['lr']
snake_case_ = int(config['num_epochs'] )
snake_case_ = int(config['seed'] )
snake_case_ = int(config['batch_size'] )
snake_case_ = args.model_name_or_path
set_seed(a__ )
snake_case_ , snake_case_ = get_dataloaders(a__ , a__ , a__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ = AutoModelForSequenceClassification.from_pretrained(a__ , return_dict=a__ )
# Instantiate optimizer
snake_case_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case_ = optimizer_cls(params=model.parameters() , lr=a__ )
if accelerator.state.deepspeed_plugin is not None:
snake_case_ = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
snake_case_ = 1
snake_case_ = (len(a__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
snake_case_ = get_linear_schedule_with_warmup(
optimizer=a__ , num_warmup_steps=0 , num_training_steps=a__ , )
else:
snake_case_ = DummyScheduler(a__ , total_num_steps=a__ , 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.
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = accelerator.prepare(
a__ , a__ , a__ , a__ , a__ )
# We need to keep track of how many total steps we have iterated over
snake_case_ = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case_ = 0
# Now we train the model
snake_case_ = evaluate.load('glue' , 'mrpc' )
snake_case_ = 0
snake_case_ = {}
for epoch in range(a__ , a__ ):
model.train()
for step, batch in enumerate(a__ ):
snake_case_ = model(**a__ )
snake_case_ = outputs.loss
snake_case_ = loss / gradient_accumulation_steps
accelerator.backward(a__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
snake_case_ = 0
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ = model(**a__ )
snake_case_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case_ , snake_case_ = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(a__ ) - 1:
snake_case_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=a__ , references=a__ , )
snake_case_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , a__ )
snake_case_ = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
snake_case_ = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(a__ , a__ )
def __lowerCamelCase ( ):
'''simple docstring'''
snake_case_ = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=a__ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=a__ , )
parser.add_argument(
'--output_dir' , type=a__ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=a__ , default=a__ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=a__ , default=3 , help='Number of train epochs.' , )
snake_case_ = parser.parse_args()
snake_case_ = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(a__ , a__ )
if __name__ == "__main__":
main()
| 362 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 A__ ( UpperCamelCase__ , unittest.TestCase ):
UpperCAmelCase = KandinskyImgaImgPipeline
UpperCAmelCase = ["prompt", "image_embeds", "negative_image_embeds", "image"]
UpperCAmelCase = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
]
UpperCAmelCase = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase = False
@property
def __UpperCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return 32
@property
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
return 32
@property
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
return self.time_input_dim
@property
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def __UpperCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
return 100
@property
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __UpperCamelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE =MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
_SCREAMING_SNAKE_CASE =MultilingualCLIP(_a )
_SCREAMING_SNAKE_CASE =text_encoder.eval()
return text_encoder
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE ={
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''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''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_SCREAMING_SNAKE_CASE =UNetaDConditionModel(**_a )
return model
@property
def __UpperCamelCase ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["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",
],
"vq_embed_dim": 4,
}
@property
def __UpperCamelCase ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE =VQModel(**self.dummy_movq_kwargs )
return model
def __UpperCamelCase ( self : str ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.dummy_text_encoder
_SCREAMING_SNAKE_CASE =self.dummy_tokenizer
_SCREAMING_SNAKE_CASE =self.dummy_unet
_SCREAMING_SNAKE_CASE =self.dummy_movq
_SCREAMING_SNAKE_CASE ={
'''num_train_timesteps''': 1000,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_00_85,
'''beta_end''': 0.0_12,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
_SCREAMING_SNAKE_CASE =DDIMScheduler(**_a )
_SCREAMING_SNAKE_CASE ={
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __UpperCamelCase ( self : str , _a : int , _a : int=0 ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
_SCREAMING_SNAKE_CASE =floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
_SCREAMING_SNAKE_CASE =floats_tensor((1, 3, 64, 64) , rng=random.Random(_a ) ).to(_a )
_SCREAMING_SNAKE_CASE =image.cpu().permute(0 , 2 , 3 , 1 )[0]
_SCREAMING_SNAKE_CASE =Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((256, 256) )
if str(_a ).startswith('''mps''' ):
_SCREAMING_SNAKE_CASE =torch.manual_seed(_a )
else:
_SCREAMING_SNAKE_CASE =torch.Generator(device=_a ).manual_seed(_a )
_SCREAMING_SNAKE_CASE ={
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def __UpperCamelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='''cpu'''
_SCREAMING_SNAKE_CASE =self.get_dummy_components()
_SCREAMING_SNAKE_CASE =self.pipeline_class(**_a )
_SCREAMING_SNAKE_CASE =pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_SCREAMING_SNAKE_CASE =pipe(**self.get_dummy_inputs(_a ) )
_SCREAMING_SNAKE_CASE =output.images
_SCREAMING_SNAKE_CASE =pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
_SCREAMING_SNAKE_CASE =image[0, -3:, -3:, -1]
_SCREAMING_SNAKE_CASE =image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_SCREAMING_SNAKE_CASE =np.array(
[0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33] )
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 A__ ( unittest.TestCase ):
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Dict ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_img2img_frog.npy''' )
_SCREAMING_SNAKE_CASE =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_SCREAMING_SNAKE_CASE ='''A red cartoon frog, 4k'''
_SCREAMING_SNAKE_CASE =KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
_SCREAMING_SNAKE_CASE =KandinskyImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1''' , torch_dtype=torch.floataa )
_SCREAMING_SNAKE_CASE =pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
_SCREAMING_SNAKE_CASE =torch.Generator(device='''cpu''' ).manual_seed(0 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_SCREAMING_SNAKE_CASE =pipeline(
_a , image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='''np''' , )
_SCREAMING_SNAKE_CASE =output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(_a , _a ) | 691 | 0 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
lowerCamelCase__ : Any = get_logger(__name__)
lowerCamelCase__ : Dict = Path(__file__).parent / "model_card_template.md"
lowerCamelCase__ : Optional[int] = uuida().hex
lowerCamelCase__ : Tuple = os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase__ : Any = os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
lowerCamelCase__ : Optional[Any] = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def __A ( a_ : Union[Dict, str, None] = None )-> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = F"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F"; torch/{_torch_version}"
if is_flax_available():
ua += F"; jax/{_jax_version}"
ua += F"; flax/{_flax_version}"
if is_onnx_available():
ua += F"; onnxruntime/{_onnxruntime_version}"
# CI will set this value to True
if os.environ.get('''DIFFUSERS_IS_CI''' , '''''' ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(a_ , a_ ):
ua += "; " + "; ".join(F"{k}/{v}" for k, v in user_agent.items() )
elif isinstance(a_ , a_ ):
ua += "; " + user_agent
return ua
def __A ( a_ : str , a_ : Optional[str] = None , a_ : Optional[str] = None )-> str:
'''simple docstring'''
if token is None:
SCREAMING_SNAKE_CASE : Tuple = HfFolder.get_token()
if organization is None:
SCREAMING_SNAKE_CASE : Any = whoami(a_ )['''name''']
return F"{username}/{model_id}"
else:
return F"{organization}/{model_id}"
def __A ( a_ : Union[str, Any] , a_ : str )-> List[Any]:
'''simple docstring'''
if not is_jinja_available():
raise ValueError(
'''Modelcard rendering is based on Jinja templates.'''
''' Please make sure to have `jinja` installed before using `create_model_card`.'''
''' To install it, please run `pip install Jinja2`.''' )
if hasattr(a_ , '''local_rank''' ) and args.local_rank not in [-1, 0]:
return
SCREAMING_SNAKE_CASE : Any = args.hub_token if hasattr(a_ , '''hub_token''' ) else None
SCREAMING_SNAKE_CASE : Optional[int] = get_full_repo_name(a_ , token=a_ )
SCREAMING_SNAKE_CASE : List[str] = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language='''en''' , license='''apache-2.0''' , library_name='''diffusers''' , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=a_ , model_name=a_ , repo_name=a_ , dataset_name=args.dataset_name if hasattr(a_ , '''dataset_name''' ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(a_ , '''gradient_accumulation_steps''' ) else None
) , adam_betaa=args.adam_betaa if hasattr(a_ , '''adam_beta1''' ) else None , adam_betaa=args.adam_betaa if hasattr(a_ , '''adam_beta2''' ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(a_ , '''adam_weight_decay''' ) else None , adam_epsilon=args.adam_epsilon if hasattr(a_ , '''adam_epsilon''' ) else None , lr_scheduler=args.lr_scheduler if hasattr(a_ , '''lr_scheduler''' ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(a_ , '''lr_warmup_steps''' ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(a_ , '''ema_inv_gamma''' ) else None , ema_power=args.ema_power if hasattr(a_ , '''ema_power''' ) else None , ema_max_decay=args.ema_max_decay if hasattr(a_ , '''ema_max_decay''' ) else None , mixed_precision=args.mixed_precision , )
SCREAMING_SNAKE_CASE : List[str] = os.path.join(args.output_dir , '''README.md''' )
model_card.save(a_ )
def __A ( a_ : Optional[str] , a_ : Optional[str] = None )-> int:
'''simple docstring'''
if resolved_file is None or commit_hash is not None:
return commit_hash
SCREAMING_SNAKE_CASE : str = str(Path(a_ ).as_posix() )
SCREAMING_SNAKE_CASE : Any = re.search(r'''snapshots/([^/]+)/''' , a_ )
if search is None:
return None
SCREAMING_SNAKE_CASE : List[Any] = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(a_ ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCamelCase__ : Optional[Any] = os.path.expanduser(
os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
lowerCamelCase__ : List[Any] = os.path.join(hf_cache_home, "diffusers")
def __A ( a_ : Optional[str] = None , a_ : Optional[str] = None )-> None:
'''simple docstring'''
if new_cache_dir is None:
SCREAMING_SNAKE_CASE : Union[str, Any] = DIFFUSERS_CACHE
if old_cache_dir is None:
SCREAMING_SNAKE_CASE : Optional[Any] = old_diffusers_cache
SCREAMING_SNAKE_CASE : Optional[Any] = Path(a_ ).expanduser()
SCREAMING_SNAKE_CASE : List[Any] = Path(a_ ).expanduser()
for old_blob_path in old_cache_dir.glob('''**/blobs/*''' ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
SCREAMING_SNAKE_CASE : Dict = new_cache_dir / old_blob_path.relative_to(a_ )
new_blob_path.parent.mkdir(parents=a_ , exist_ok=a_ )
os.replace(a_ , a_ )
try:
os.symlink(a_ , a_ )
except OSError:
logger.warning(
'''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.''' )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
lowerCamelCase__ : int = os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
lowerCamelCase__ : Tuple = 0
else:
with open(cache_version_file) as f:
try:
lowerCamelCase__ : str = int(f.read())
except ValueError:
lowerCamelCase__ : Any = 0
if cache_version < 1:
lowerCamelCase__ : Union[str, Any] = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
"existing cached models. This is a one-time operation, you can interrupt it or run it "
"later by calling `diffusers.utils.hub_utils.move_cache()`."
)
try:
move_cache()
except Exception as e:
lowerCamelCase__ : List[Any] = "\n".join(traceback.format_tb(e.__traceback__))
logger.error(
f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
"file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
"message and we will do our best to help."
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, "w") as f:
f.write("1")
except Exception:
logger.warning(
f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
"the directory exists and can be written to."
)
def __A ( a_ : str , a_ : Optional[str] = None )-> str:
'''simple docstring'''
if variant is not None:
SCREAMING_SNAKE_CASE : Optional[int] = weights_name.split('''.''' )
SCREAMING_SNAKE_CASE : Optional[Any] = splits[:-1] + [variant] + splits[-1:]
SCREAMING_SNAKE_CASE : List[str] = '''.'''.join(a_ )
return weights_name
def __A ( a_ : int , *,
a_ : List[str] , a_ : List[str] , a_ : Tuple , a_ : Optional[int] , a_ : int , a_ : int , a_ : Tuple , a_ : Union[str, Any] , a_ : Dict , a_ : Dict , a_ : int=None , )-> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = str(a_ )
if os.path.isfile(a_ ):
return pretrained_model_name_or_path
elif os.path.isdir(a_ ):
if os.path.isfile(os.path.join(a_ , a_ ) ):
# Load from a PyTorch checkpoint
SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(a_ , a_ )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(a_ , a_ , a_ ) ):
SCREAMING_SNAKE_CASE : List[str] = os.path.join(a_ , a_ , a_ )
return model_file
else:
raise EnvironmentError(
F"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}." )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(a_ ).base_version ) >= version.parse('''0.20.0''' )
):
try:
SCREAMING_SNAKE_CASE : List[str] = hf_hub_download(
a_ , filename=_add_variant(a_ , a_ ) , cache_dir=a_ , force_download=a_ , proxies=a_ , resume_download=a_ , local_files_only=a_ , use_auth_token=a_ , user_agent=a_ , subfolder=a_ , revision=revision or commit_hash , )
warnings.warn(
F"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead." , a_ , )
return model_file
except: # noqa: E722
warnings.warn(
F"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a_ , a_ )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a_ , a_ )}' so that the correct variant file can be added." , a_ , )
try:
# 2. Load model file as usual
SCREAMING_SNAKE_CASE : Optional[Any] = hf_hub_download(
a_ , filename=a_ , cache_dir=a_ , force_download=a_ , proxies=a_ , resume_download=a_ , local_files_only=a_ , use_auth_token=a_ , user_agent=a_ , subfolder=a_ , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
'''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
'''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
'''login`.''' )
except RevisionNotFoundError:
raise EnvironmentError(
F"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
'''this model name. Check the model page at '''
F"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." )
except EntryNotFoundError:
raise EnvironmentError(
F"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}." )
except HTTPError as err:
raise EnvironmentError(
F"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" )
except ValueError:
raise EnvironmentError(
F"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
F" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
F" directory containing a file named {weights_name} or"
''' \nCheckout your internet connection or see how to run the library in'''
''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.''' )
except EnvironmentError:
raise EnvironmentError(
F"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
'''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
F"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
F"containing a file named {weights_name}" )
| 18 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCamelCase__ : Optional[Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__ : Optional[int] = 50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__ : Optional[Any] = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def __A ( a_ : str , a_ : str )-> tuple[str, float]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = len([g for position, g in enumerate(a_ ) if g == main_target[position]] )
return (item, float(a_ ))
def __A ( a_ : str , a_ : str )-> tuple[str, str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = random.randint(0 , len(a_ ) - 1 )
SCREAMING_SNAKE_CASE : str = parent_a[:random_slice] + parent_a[random_slice:]
SCREAMING_SNAKE_CASE : Dict = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def __A ( a_ : str , a_ : list[str] )-> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = list(a_ )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
SCREAMING_SNAKE_CASE : Any = random.choice(a_ )
return "".join(a_ )
def __A ( a_ : tuple[str, float] , a_ : list[tuple[str, float]] , a_ : list[str] , )-> list[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = []
# Generate more children proportionally to the fitness score.
SCREAMING_SNAKE_CASE : List[str] = int(parent_a[1] * 1_00 ) + 1
SCREAMING_SNAKE_CASE : Optional[Any] = 10 if child_n >= 10 else child_n
for _ in range(a_ ):
SCREAMING_SNAKE_CASE : List[str] = population_score[random.randint(0 , a_ )][0]
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : Tuple = crossover(parent_a[0] , a_ )
# Append new string to the population list.
pop.append(mutate(a_ , a_ ) )
pop.append(mutate(a_ , a_ ) )
return pop
def __A ( a_ : str , a_ : list[str] , a_ : bool = True )-> tuple[int, int, str]:
'''simple docstring'''
if N_POPULATION < N_SELECTED:
SCREAMING_SNAKE_CASE : List[Any] = F"{N_POPULATION} must be bigger than {N_SELECTED}"
raise ValueError(a_ )
# Verify that the target contains no genes besides the ones inside genes variable.
SCREAMING_SNAKE_CASE : List[str] = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
SCREAMING_SNAKE_CASE : str = F"{not_in_genes_list} is not in genes list, evolution cannot converge"
raise ValueError(a_ )
# Generate random starting population.
SCREAMING_SNAKE_CASE : Tuple = []
for _ in range(a_ ):
population.append(''''''.join([random.choice(a_ ) for i in range(len(a_ ) )] ) )
# Just some logs to know what the algorithms is doing.
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : List[str] = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(a_ )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
SCREAMING_SNAKE_CASE : int = [evaluate(a_ , a_ ) for item in population]
# Check if there is a matching evolution.
SCREAMING_SNAKE_CASE : List[Any] = sorted(a_ , key=lambda a_ : x[1] , reverse=a_ )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
F"\nGeneration: {generation}"
F"\nTotal Population:{total_population}"
F"\nBest score: {population_score[0][1]}"
F"\nBest string: {population_score[0][0]}" )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
SCREAMING_SNAKE_CASE : Optional[Any] = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(a_ )
# Normalize population score to be between 0 and 1.
SCREAMING_SNAKE_CASE : Optional[int] = [
(item, score / len(a_ )) for item, score in population_score
]
# This is selection
for i in range(a_ ):
population.extend(select(population_score[int(a_ )] , a_ , a_ ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(a_ ) > N_POPULATION:
break
if __name__ == "__main__":
lowerCamelCase__ : Dict = (
"This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
)
lowerCamelCase__ : int = list(
" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
"nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
)
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = basic(target_str, genes_list)
print(
f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 18 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__lowerCamelCase = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoForCausalLM''',
'''GPTNeoForQuestionAnswering''',
'''GPTNeoForSequenceClassification''',
'''GPTNeoForTokenClassification''',
'''GPTNeoModel''',
'''GPTNeoPreTrainedModel''',
'''load_tf_weights_in_gpt_neo''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''FlaxGPTNeoForCausalLM''',
'''FlaxGPTNeoModel''',
'''FlaxGPTNeoPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neo import (
GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoForCausalLM,
GPTNeoForQuestionAnswering,
GPTNeoForSequenceClassification,
GPTNeoForTokenClassification,
GPTNeoModel,
GPTNeoPreTrainedModel,
load_tf_weights_in_gpt_neo,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 288 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Dict:
A_ = sorted(zip(UpperCAmelCase__, UpperCAmelCase__ ), key=lambda UpperCAmelCase__ : x[0] / x[1], reverse=UpperCAmelCase__ )
A_ , A_ = [i[0] for i in r], [i[1] for i in r]
A_ = list(accumulate(UpperCAmelCase__ ) )
A_ = bisect(UpperCAmelCase__, UpperCAmelCase__ )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 288 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""tiiuae/falcon-40b""": """https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json""",
"""tiiuae/falcon-7b""": """https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json""",
}
class _snake_case ( lowercase__):
UpperCamelCase__ : Dict ="""falcon"""
UpperCamelCase__ : Any =["""past_key_values"""]
def __init__( self : Any, __lowercase : Tuple=6_5024, __lowercase : Optional[Any]=4544, __lowercase : Optional[Any]=32, __lowercase : str=71, __lowercase : Any=1e-5, __lowercase : int=0.02, __lowercase : Optional[int]=True, __lowercase : List[str]=0.0, __lowercase : Optional[int]=0.0, __lowercase : Optional[Any]=None, __lowercase : str=False, __lowercase : Tuple=False, __lowercase : List[str]=True, __lowercase : Optional[Any]=True, __lowercase : int=False, __lowercase : Optional[Any]=11, __lowercase : List[str]=11, **__lowercase : Tuple, ):
lowercase__ = vocab_size
# Backward compatibility with n_embed kwarg
lowercase__ = kwargs.pop("n_embed", __lowercase )
lowercase__ = hidden_size if n_embed is None else n_embed
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = layer_norm_epsilon
lowercase__ = initializer_range
lowercase__ = use_cache
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = bos_token_id
lowercase__ = eos_token_id
lowercase__ = num_attention_heads if num_kv_heads is None else num_kv_heads
lowercase__ = alibi
lowercase__ = new_decoder_architecture
lowercase__ = multi_query # Ignored when new_decoder_architecture is True
lowercase__ = parallel_attn
lowercase__ = bias
super().__init__(bos_token_id=__lowercase, eos_token_id=__lowercase, **__lowercase )
@property
def A__ ( self : int ):
return self.hidden_size // self.num_attention_heads
@property
def A__ ( self : str ):
return not self.alibi
| 37 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
lowercase__ = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
os.makedirs(SCREAMING_SNAKE_CASE_ )
lowercase__ = model.state_dict()
def to_tf_var_name(SCREAMING_SNAKE_CASE_ ):
for patt, repl in iter(SCREAMING_SNAKE_CASE_ ):
lowercase__ = name.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return f'''bert/{name}'''
def create_tf_var(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = tf.dtypes.as_dtype(tensor.dtype )
lowercase__ = tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ , shape=tensor.shape , name=SCREAMING_SNAKE_CASE_ , initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(SCREAMING_SNAKE_CASE_ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
lowercase__ = to_tf_var_name(SCREAMING_SNAKE_CASE_ )
lowercase__ = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
lowercase__ = torch_tensor.T
lowercase__ = create_tf_var(tensor=SCREAMING_SNAKE_CASE_ , name=SCREAMING_SNAKE_CASE_ , session=SCREAMING_SNAKE_CASE_ )
tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowercase__ = session.run(SCREAMING_SNAKE_CASE_ )
print(f'''Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )}''' )
lowercase__ = tf.train.Saver(tf.trainable_variables() )
saver.save(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , model_name.replace("-" , "_" ) + ".ckpt" ) )
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_=None ):
lowercase__ = argparse.ArgumentParser()
parser.add_argument("--model_name" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Directory in which to save tensorflow model" )
lowercase__ = parser.parse_args(SCREAMING_SNAKE_CASE_ )
lowercase__ = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name )
if __name__ == "__main__":
main()
| 37 | 1 |
'''simple docstring'''
import copy
import re
class _lowerCAmelCase :
__SCREAMING_SNAKE_CASE : Union[str, Any] = 'hp'
__SCREAMING_SNAKE_CASE : Tuple = {}
__SCREAMING_SNAKE_CASE : Optional[int] = None
@classmethod
def _a (cls , lowercase , lowercase ):
A_ : int = prefix
A_ : Tuple = defaults
cls.build_naming_info()
@staticmethod
def _a (lowercase , lowercase ):
if len(snake_case_ ) == 0:
return ""
A_ : Optional[Any] = None
if any(char.isdigit() for char in word ):
raise Exception(F'Parameters should not contain numbers: \'{word}\' contains a number' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(snake_case_ ) + 1 ):
A_ : str = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
A_ : List[str] = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(lowercase ):
A_ : Optional[Any] = """"""
while integer != 0:
A_ : Dict = chr(ord("""A""" ) + integer % 10 ) + s
integer //= 10
return s
A_ : List[str] = 0
while True:
A_ : Dict = word + """#""" + int_to_alphabetic(snake_case_ )
if sword in info["reverse_short_word"]:
continue
else:
A_ : int = sword
break
A_ : Union[str, Any] = short_word
A_ : Union[str, Any] = word
return short_word
@staticmethod
def _a (lowercase , lowercase ):
A_ : Tuple = param_name.split("""_""" )
A_ : Optional[Any] = [TrialShortNamer.shortname_for_word(snake_case_ , snake_case_ ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
A_ : Any = ["""""", """_"""]
for separator in separators:
A_ : str = separator.join(snake_case_ )
if shortname not in info["reverse_short_param"]:
A_ : int = shortname
A_ : Dict = param_name
return shortname
return param_name
@staticmethod
def _a (lowercase , lowercase ):
A_ : Dict = TrialShortNamer.shortname_for_key(snake_case_ , snake_case_ )
A_ : Tuple = short_name
A_ : Optional[int] = param_name
@classmethod
def _a (cls ):
if cls.NAMING_INFO is not None:
return
A_ : List[Any] = {
"""short_word""": {},
"""reverse_short_word""": {},
"""short_param""": {},
"""reverse_short_param""": {},
}
A_ : int = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(snake_case_ , snake_case_ )
A_ : List[str] = info
@classmethod
def _a (cls , lowercase ):
cls.build_naming_info()
assert cls.PREFIX is not None
A_ : List[Any] = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F'You should provide a default value for the param name {k} with value {v}' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
A_ : Dict = cls.NAMING_INFO["""short_param"""][k]
if isinstance(snake_case_ , snake_case_ ):
A_ : Tuple = 1 if v else 0
A_ : Any = """""" if isinstance(snake_case_ , (int, float) ) else """-"""
A_ : Optional[Any] = F'{key}{sep}{v}'
name.append(snake_case_ )
return "_".join(snake_case_ )
@classmethod
def _a (cls , lowercase ):
A_ : List[Any] = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
A_ : Optional[int] = []
else:
A_ : Any = repr.split("""_""" )
A_ : List[Any] = {}
for value in values:
if "-" in value:
A_, A_ : Optional[int] = value.split("""-""" )
else:
A_ : List[Any] = re.sub("""[0-9.]""" , """""" , snake_case_ )
A_ : Union[str, Any] = float(re.sub("""[^0-9.]""" , """""" , snake_case_ ) )
A_ : Union[str, Any] = cls.NAMING_INFO["""reverse_short_param"""][p_k]
A_ : List[str] = p_v
for k in cls.DEFAULTS:
if k not in parameters:
A_ : Any = cls.DEFAULTS[k]
return parameters | 667 |
'''simple docstring'''
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(lowercase_ , lowercase_ ):
return 0
elif n == 2:
return 1
else:
lowercase =[0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCamelCase ( lowercase_ : int ) -> int:
'''simple docstring'''
lowercase =0
lowercase =2
while digits < n:
index += 1
lowercase =len(str(fibonacci(lowercase_ ) ) )
return index
def UpperCamelCase ( lowercase_ : int = 1_0_0_0 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(lowercase_ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 72 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = ["""image_processor"""]
__A = """SamImageProcessor"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
super().__init__(__UpperCamelCase )
snake_case_ = self.image_processor
snake_case_ = -10
snake_case_ = self.image_processor.size['longest_edge']
def __call__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase = None , **__UpperCamelCase , ):
"""simple docstring"""
snake_case_ = self.image_processor(
__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase , )
# pop arguments that are not used in the foward but used nevertheless
snake_case_ = encoding_image_processor['original_sizes']
if hasattr(__UpperCamelCase , 'numpy' ): # Checks if Torch or TF tensor
snake_case_ = original_sizes.numpy()
snake_case_ , snake_case_ , snake_case_ = self._check_and_preprocess_points(
input_points=__UpperCamelCase , input_labels=__UpperCamelCase , input_boxes=__UpperCamelCase , )
snake_case_ = self._normalize_and_convert(
__UpperCamelCase , __UpperCamelCase , input_points=__UpperCamelCase , input_labels=__UpperCamelCase , input_boxes=__UpperCamelCase , return_tensors=__UpperCamelCase , )
return encoding_image_processor
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase="pt" , ):
"""simple docstring"""
if input_points is not None:
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
snake_case_ = [
self._normalize_coordinates(self.target_size , __UpperCamelCase , original_sizes[0] ) for point in input_points
]
else:
snake_case_ = [
self._normalize_coordinates(self.target_size , __UpperCamelCase , __UpperCamelCase )
for point, original_size in zip(__UpperCamelCase , __UpperCamelCase )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
snake_case_ , snake_case_ = self._pad_points_and_labels(__UpperCamelCase , __UpperCamelCase )
snake_case_ = np.array(__UpperCamelCase )
if input_labels is not None:
snake_case_ = np.array(__UpperCamelCase )
if input_boxes is not None:
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
snake_case_ = [
self._normalize_coordinates(self.target_size , __UpperCamelCase , original_sizes[0] , is_bounding_box=__UpperCamelCase )
for box in input_boxes
]
else:
snake_case_ = [
self._normalize_coordinates(self.target_size , __UpperCamelCase , __UpperCamelCase , is_bounding_box=__UpperCamelCase )
for box, original_size in zip(__UpperCamelCase , __UpperCamelCase )
]
snake_case_ = np.array(__UpperCamelCase )
if input_boxes is not None:
if return_tensors == "pt":
snake_case_ = torch.from_numpy(__UpperCamelCase )
# boxes batch size of 1 by default
snake_case_ = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
snake_case_ = tf.convert_to_tensor(__UpperCamelCase )
# boxes batch size of 1 by default
snake_case_ = tf.expand_dims(__UpperCamelCase , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'input_boxes': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
snake_case_ = torch.from_numpy(__UpperCamelCase )
# point batch size of 1 by default
snake_case_ = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
snake_case_ = tf.convert_to_tensor(__UpperCamelCase )
# point batch size of 1 by default
snake_case_ = tf.expand_dims(__UpperCamelCase , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'input_points': input_points} )
if input_labels is not None:
if return_tensors == "pt":
snake_case_ = torch.from_numpy(__UpperCamelCase )
# point batch size of 1 by default
snake_case_ = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
snake_case_ = tf.convert_to_tensor(__UpperCamelCase )
# point batch size of 1 by default
snake_case_ = tf.expand_dims(__UpperCamelCase , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'input_labels': input_labels} )
return encoding_image_processor
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = max([point.shape[0] for point in input_points] )
snake_case_ = []
for i, point in enumerate(__UpperCamelCase ):
if point.shape[0] != expected_nb_points:
snake_case_ = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
snake_case_ = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(__UpperCamelCase )
snake_case_ = processed_input_points
return input_points, input_labels
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=False ):
"""simple docstring"""
snake_case_ , snake_case_ = original_size
snake_case_ , snake_case_ = self.image_processor._get_preprocess_shape(__UpperCamelCase , longest_edge=__UpperCamelCase )
snake_case_ = deepcopy(__UpperCamelCase ).astype(__UpperCamelCase )
if is_bounding_box:
snake_case_ = coords.reshape(-1 , 2 , 2 )
snake_case_ = coords[..., 0] * (new_w / old_w)
snake_case_ = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
snake_case_ = coords.reshape(-1 , 4 )
return coords
def __lowerCAmelCase ( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , ):
"""simple docstring"""
if input_points is not None:
if hasattr(__UpperCamelCase , 'numpy' ): # Checks for TF or Torch tensor
snake_case_ = input_points.numpy().tolist()
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or not isinstance(input_points[0] , __UpperCamelCase ):
raise ValueError('Input points must be a list of list of floating points.' )
snake_case_ = [np.array(__UpperCamelCase ) for input_point in input_points]
else:
snake_case_ = None
if input_labels is not None:
if hasattr(__UpperCamelCase , 'numpy' ):
snake_case_ = input_labels.numpy().tolist()
if not isinstance(__UpperCamelCase , __UpperCamelCase ) or not isinstance(input_labels[0] , __UpperCamelCase ):
raise ValueError('Input labels must be a list of list integers.' )
snake_case_ = [np.array(__UpperCamelCase ) for label in input_labels]
else:
snake_case_ = None
if input_boxes is not None:
if hasattr(__UpperCamelCase , 'numpy' ):
snake_case_ = input_boxes.numpy().tolist()
if (
not isinstance(__UpperCamelCase , __UpperCamelCase )
or not isinstance(input_boxes[0] , __UpperCamelCase )
or not isinstance(input_boxes[0][0] , __UpperCamelCase )
):
raise ValueError('Input boxes must be a list of list of list of floating points.' )
snake_case_ = [np.array(__UpperCamelCase ).astype(np.floataa ) for box in input_boxes]
else:
snake_case_ = None
return input_points, input_labels, input_boxes
@property
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.image_processor.model_input_names
return list(dict.fromkeys(__UpperCamelCase ) )
def __lowerCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
return self.image_processor.post_process_masks(*__UpperCamelCase , **__UpperCamelCase )
| 46 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=16 , __UpperCamelCase=[1, 2, 1] , __UpperCamelCase=[2, 2, 4] , __UpperCamelCase=2 , __UpperCamelCase=2.0 , __UpperCamelCase=True , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.1 , __UpperCamelCase="gelu" , __UpperCamelCase=False , __UpperCamelCase=True , __UpperCamelCase=0.02 , __UpperCamelCase=1E-5 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=10 , __UpperCamelCase=8 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_size
snake_case_ = num_channels
snake_case_ = embed_dim
snake_case_ = depths
snake_case_ = num_heads
snake_case_ = window_size
snake_case_ = mlp_ratio
snake_case_ = qkv_bias
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = drop_path_rate
snake_case_ = hidden_act
snake_case_ = use_absolute_embeddings
snake_case_ = patch_norm
snake_case_ = layer_norm_eps
snake_case_ = initializer_range
snake_case_ = is_training
snake_case_ = scope
snake_case_ = use_labels
snake_case_ = type_sequence_label_size
snake_case_ = encoder_stride
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ):
"""simple docstring"""
return SwinvaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaModel(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
snake_case_ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case_ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = SwinvaForMaskedImageModeling(config=__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case_ = 1
snake_case_ = SwinvaForMaskedImageModeling(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case_ = model(__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.type_sequence_label_size
snake_case_ = SwinvaForImageClassification(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
snake_case_ = model(__UpperCamelCase , labels=__UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , unittest.TestCase ):
"""simple docstring"""
__A = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
__A = (
{"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
if is_torch_available()
else {}
)
__A = False
__A = False
__A = False
__A = False
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaModelTester(self )
snake_case_ = ConfigTester(self , config_class=__UpperCamelCase , embed_dim=37 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCamelCase )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def __lowerCAmelCase ( self ):
"""simple docstring"""
pass
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
snake_case_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(__UpperCamelCase )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
snake_case_ = len(self.model_tester.depths )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = config.window_size**2
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
snake_case_ = len(__UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
if hasattr(self.model_tester , 'num_hidden_states_types' ):
snake_case_ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
snake_case_ = 2
self.assertEqual(out_len + added_hidden_states , len(__UpperCamelCase ) )
snake_case_ = outputs.attentions
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = model_class(__UpperCamelCase )
model.to(__UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase ) )
snake_case_ = outputs.hidden_states
snake_case_ = getattr(
self.model_tester , 'expected_num_hidden_layers' , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
# Swinv2 has a different seq_length
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
snake_case_ = outputs.reshaped_hidden_states
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
snake_case_ , snake_case_ , snake_case_ , snake_case_ = reshaped_hidden_states[0].shape
snake_case_ = (
reshaped_hidden_states[0].view(__UpperCamelCase , __UpperCamelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = 3
snake_case_ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case_ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case_ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case_ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
self.check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , (padded_height, padded_width) )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = SwinvaModel.from_pretrained(__UpperCamelCase )
self.assertIsNotNone(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = _config_zero_init(__UpperCamelCase )
for model_class in self.all_model_classes:
snake_case_ = model_class(config=__UpperCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@require_vision
@require_torch
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowerCAmelCase ( self ):
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
__UpperCamelCase )
snake_case_ = self.default_image_processor
snake_case_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
snake_case_ = image_processor(images=__UpperCamelCase , return_tensors='pt' ).to(__UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case_ = model(**__UpperCamelCase )
# verify the logits
snake_case_ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __UpperCamelCase )
snake_case_ = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(__UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) )
| 46 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig",
],
"processing_clipseg": ["CLIPSegProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
"CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
"CLIPSegModel",
"CLIPSegPreTrainedModel",
"CLIPSegTextModel",
"CLIPSegVisionModel",
"CLIPSegForImageSegmentation",
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 290 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a=1_3 , _a=3 , _a=True , _a=True , _a=0.1 , _a=0.1 , _a=2_2_4 , _a=1_0_0_0 , _a=[3, 3, 6, 4] , _a=[4_8, 5_6, 1_1_2, 2_2_0] , ) -> Tuple:
_a : Dict = parent
_a : Optional[int] = batch_size
_a : Optional[Any] = num_channels
_a : Union[str, Any] = is_training
_a : Tuple = use_labels
_a : Dict = hidden_dropout_prob
_a : List[Any] = attention_probs_dropout_prob
_a : Dict = num_labels
_a : List[str] = image_size
_a : Dict = layer_depths
_a : str = embed_dims
def __lowercase ( self ) -> Optional[Any]:
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : int = None
if self.use_labels:
_a : Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
_a : Dict = self.get_config()
return config, pixel_values, labels
def __lowercase ( self ) -> int:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=_a , layer_scale_init_value=1e-5 , )
def __lowercase ( self , _a , _a , _a ) -> str:
_a : List[Any] = SwiftFormerModel(config=_a )
model.to(_a )
model.eval()
_a : Optional[int] = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def __lowercase ( self , _a , _a , _a ) -> Optional[Any]:
_a : List[str] = self.num_labels
_a : Optional[int] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : List[str] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
_a : Union[str, Any] = SwiftFormerForImageClassification(_a )
model.to(_a )
model.eval()
_a : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a : Optional[Any] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowercase ( self ) -> Tuple:
((_a) , (_a) , (_a)) : Optional[int] = self.prepare_config_and_inputs()
_a : List[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
UpperCAmelCase__ : Optional[int] = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : str = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : Tuple = False
UpperCAmelCase__ : str = False
def __lowercase ( self ) -> Optional[int]:
_a : Union[str, Any] = SwiftFormerModelTester(self )
_a : int = ConfigTester(
self , config_class=_a , has_text_modality=_a , hidden_size=3_7 , num_attention_heads=1_2 , num_hidden_layers=1_2 , )
def __lowercase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def __lowercase ( self ) -> Union[str, Any]:
pass
def __lowercase ( self ) -> Dict:
_a , _a : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Any = model_class(_a )
_a : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def __lowercase ( self ) -> str:
_a , _a : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : Optional[int] = model_class(_a )
_a : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a : Tuple = [*signature.parameters.keys()]
_a : List[str] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _a )
def __lowercase ( self ) -> int:
_a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def __lowercase ( self ) -> Optional[int]:
_a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def __lowercase ( self ) -> Optional[Any]:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a : Any = SwiftFormerModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def __lowercase ( self ) -> List[Any]:
pass
def __lowercase ( self ) -> int:
def check_hidden_states_output(_a , _a , _a ):
_a : Optional[int] = model_class(_a )
model.to(_a )
model.eval()
with torch.no_grad():
_a : Union[str, Any] = model(**self._prepare_for_class(_a , _a ) )
_a : Optional[Any] = outputs.hidden_states
_a : Union[str, Any] = 8
self.assertEqual(len(_a ) , _a ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(_a ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
_a , _a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a : str = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_a : List[str] = True
check_hidden_states_output(_a , _a , _a )
def __lowercase ( self ) -> str:
def _config_zero_init(_a ):
_a : List[Any] = copy.deepcopy(_a )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(_a , _a , 1e-1_0 )
if isinstance(getattr(_a , _a , _a ) , _a ):
_a : int = _config_zero_init(getattr(_a , _a ) )
setattr(_a , _a , _a )
return configs_no_init
_a , _a : Any = self.model_tester.prepare_config_and_inputs_for_common()
_a : Dict = _config_zero_init(_a )
for model_class in self.all_model_classes:
_a : Dict = model_class(config=_a )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9) / 1e9).round().item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __lowercase ( self ) -> Optional[Any]:
pass
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_a : Optional[int] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __lowercase ( self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def __lowercase ( self ) -> Dict:
_a : Any = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(_a )
_a : Any = self.default_image_processor
_a : Any = prepare_img()
_a : Any = image_processor(images=_a , return_tensors='''pt''' ).to(_a )
# forward pass
with torch.no_grad():
_a : Optional[Any] = model(**_a )
# verify the logits
_a : List[str] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _a )
_a : int = torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
| 14 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ : str = logging.get_logger(__name__)
A__ : List[Any] = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class _lowercase ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
_A = 'resnet'
_A = ['basic', 'bottleneck']
def __init__( self , __UpperCamelCase=3 , __UpperCamelCase=64 , __UpperCamelCase=[2_56, 5_12, 10_24, 20_48] , __UpperCamelCase=[3, 4, 6, 3] , __UpperCamelCase="bottleneck" , __UpperCamelCase="relu" , __UpperCamelCase=False , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , )-> Union[str, Any]:
super().__init__(**__UpperCamelCase )
if layer_type not in self.layer_types:
raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
UpperCAmelCase__ : List[str] = num_channels
UpperCAmelCase__ : str = embedding_size
UpperCAmelCase__ : Optional[int] = hidden_sizes
UpperCAmelCase__ : str = depths
UpperCAmelCase__ : Optional[int] = layer_type
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : Tuple = downsample_in_first_stage
UpperCAmelCase__ : str = ["stem"] + [F"stage{idx}" for idx in range(1 , len(__UpperCamelCase ) + 1 )]
UpperCAmelCase__ , UpperCAmelCase__ : Any = get_aligned_output_features_output_indices(
out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-3
| 660 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : Dict = logging.get_logger(__name__)
def a__ ( lowerCAmelCase : Optional[Any] ):
'''simple docstring'''
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = ['pixel_values']
def __init__( self , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = True , __UpperCamelCase = 1 / 2_55 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = None , __UpperCamelCase = None , **__UpperCamelCase , )-> None:
super().__init__(**__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = size if size is not None else {"shortest_edge": 2_56}
UpperCAmelCase__ : List[Any] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : List[str] = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
UpperCAmelCase__ : int = get_size_dict(__UpperCamelCase , param_name="crop_size" )
UpperCAmelCase__ : Dict = do_resize
UpperCAmelCase__ : Optional[int] = size
UpperCAmelCase__ : List[Any] = do_center_crop
UpperCAmelCase__ : str = crop_size
UpperCAmelCase__ : Optional[int] = resample
UpperCAmelCase__ : int = do_rescale
UpperCAmelCase__ : Union[str, Any] = rescale_factor
UpperCAmelCase__ : Union[str, Any] = offset
UpperCAmelCase__ : Dict = do_normalize
UpperCAmelCase__ : int = 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 lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = PILImageResampling.BILINEAR , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
if "shortest_edge" in size:
UpperCAmelCase__ : Union[str, Any] = get_resize_output_image_size(__UpperCamelCase , size["shortest_edge"] , default_to_square=__UpperCamelCase )
elif "height" in size and "width" in size:
UpperCAmelCase__ : Any = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
UpperCAmelCase__ : Optional[Any] = get_size_dict(__UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__UpperCamelCase , size=(size["height"], size["width"]) , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , __UpperCamelCase = None , **__UpperCamelCase , )-> Tuple:
UpperCAmelCase__ : str = image.astype(np.floataa )
if offset:
UpperCAmelCase__ : Tuple = image - (scale / 2)
return rescale(__UpperCamelCase , scale=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , **__UpperCamelCase , )-> np.ndarray:
return normalize(__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase , data_format=__UpperCamelCase , **__UpperCamelCase )
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , )-> np.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_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop 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." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
UpperCAmelCase__ : Optional[Any] = to_numpy_array(__UpperCamelCase )
if do_resize:
UpperCAmelCase__ : Union[str, Any] = self.resize(image=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase )
if do_center_crop:
UpperCAmelCase__ : int = self.center_crop(__UpperCamelCase , size=__UpperCamelCase )
if do_rescale:
UpperCAmelCase__ : List[str] = self.rescale(image=__UpperCamelCase , scale=__UpperCamelCase , offset=__UpperCamelCase )
if do_normalize:
UpperCAmelCase__ : List[Any] = self.normalize(image=__UpperCamelCase , mean=__UpperCamelCase , std=__UpperCamelCase )
UpperCAmelCase__ : Dict = to_channel_dimension_format(__UpperCamelCase , __UpperCamelCase )
return image
def lowerCAmelCase__ ( self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = None , __UpperCamelCase = ChannelDimension.FIRST , **__UpperCamelCase , )-> PIL.Image.Image:
UpperCAmelCase__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ : int = resample if resample is not None else self.resample
UpperCAmelCase__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
UpperCAmelCase__ : int = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ : Optional[int] = offset if offset is not None else self.offset
UpperCAmelCase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ : Dict = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ : Optional[int] = image_std if image_std is not None else self.image_std
UpperCAmelCase__ : List[str] = size if size is not None else self.size
UpperCAmelCase__ : Optional[int] = get_size_dict(__UpperCamelCase , default_to_square=__UpperCamelCase )
UpperCAmelCase__ : Dict = crop_size if crop_size is not None else self.crop_size
UpperCAmelCase__ : Tuple = get_size_dict(__UpperCamelCase , param_name="crop_size" )
if not valid_images(__UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
UpperCAmelCase__ : List[str] = make_batched(__UpperCamelCase )
UpperCAmelCase__ : Optional[Any] = [
[
self._preprocess_image(
image=__UpperCamelCase , do_resize=__UpperCamelCase , size=__UpperCamelCase , resample=__UpperCamelCase , do_center_crop=__UpperCamelCase , crop_size=__UpperCamelCase , do_rescale=__UpperCamelCase , rescale_factor=__UpperCamelCase , offset=__UpperCamelCase , do_normalize=__UpperCamelCase , image_mean=__UpperCamelCase , image_std=__UpperCamelCase , data_format=__UpperCamelCase , )
for img in video
]
for video in videos
]
UpperCAmelCase__ : Dict = {"pixel_values": videos}
return BatchFeature(data=__UpperCamelCase , tensor_type=__UpperCamelCase )
| 660 | 1 |
import os
def UpperCamelCase( __UpperCamelCase : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(__UpperCamelCase ) ,__UpperCamelCase ) ) as in_file:
lowerCAmelCase_ : Optional[Any] = in_file.read()
lowerCAmelCase_ : Tuple = [[int(__UpperCamelCase ) for cell in row.split(''',''' )] for row in data.strip().splitlines()]
lowerCAmelCase_ : Optional[Any] = [[0 for cell in row] for row in grid]
lowerCAmelCase_ : Dict = len(grid[0] )
lowerCAmelCase_ : Tuple = [[0 for i in range(__UpperCamelCase )] for j in range(__UpperCamelCase )]
lowerCAmelCase_ : str = grid[0][0]
for i in range(1 ,__UpperCamelCase ):
lowerCAmelCase_ : List[str] = grid[0][i] + dp[0][i - 1]
for i in range(1 ,__UpperCamelCase ):
lowerCAmelCase_ : List[str] = grid[i][0] + dp[i - 1][0]
for i in range(1 ,__UpperCamelCase ):
for j in range(1 ,__UpperCamelCase ):
lowerCAmelCase_ : Tuple = grid[i][j] + min(dp[i - 1][j] ,dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 171 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
A__ : List[str] = logging.get_logger(__name__)
@add_end_docstrings(
UpperCamelCase_ ,R'''
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
''' ,)
class __snake_case ( UpperCamelCase_ ):
def UpperCAmelCase__ ( self : Optional[Any] , A_ : GenericTensor):
if self.framework == "tf":
lowerCAmelCase_ : Dict = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
elif self.framework == "pt":
lowerCAmelCase_ : List[Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=A_)
else:
raise ValueError('''Unsupported framework''')
return masked_index
def UpperCAmelCase__ ( self : Tuple , A_ : GenericTensor):
lowerCAmelCase_ : List[str] = self.get_masked_index(A_)
lowerCAmelCase_ : Union[str, Any] = np.prod(masked_index.shape)
if numel < 1:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , F"""No mask_token ({self.tokenizer.mask_token}) found on the input""" , )
def UpperCAmelCase__ ( self : str , A_ : GenericTensor):
if isinstance(A_ , A_):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0])
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(A_)
def UpperCAmelCase__ ( self : Optional[Any] , A_ : Union[str, Any] , A_ : Optional[int]=None , **A_ : List[str]):
if return_tensors is None:
lowerCAmelCase_ : Optional[int] = self.framework
lowerCAmelCase_ : Optional[Any] = self.tokenizer(A_ , return_tensors=A_)
self.ensure_exactly_one_mask_token(A_)
return model_inputs
def UpperCAmelCase__ ( self : List[str] , A_ : str):
lowerCAmelCase_ : Union[str, Any] = self.model(**A_)
lowerCAmelCase_ : List[str] = model_inputs['''input_ids''']
return model_outputs
def UpperCAmelCase__ ( self : str , A_ : str , A_ : str=5 , A_ : int=None):
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
lowerCAmelCase_ : int = target_ids.shape[0]
lowerCAmelCase_ : List[Any] = model_outputs['''input_ids'''][0]
lowerCAmelCase_ : int = model_outputs['''logits''']
if self.framework == "tf":
lowerCAmelCase_ : Union[str, Any] = tf.where(input_ids == self.tokenizer.mask_token_id).numpy()[:, 0]
lowerCAmelCase_ : Optional[Any] = outputs.numpy()
lowerCAmelCase_ : List[str] = outputs[0, masked_index, :]
lowerCAmelCase_ : List[Any] = stable_softmax(A_ , axis=-1)
if target_ids is not None:
lowerCAmelCase_ : str = tf.gather_nd(tf.squeeze(A_ , 0) , target_ids.reshape(-1 , 1))
lowerCAmelCase_ : Any = tf.expand_dims(A_ , 0)
lowerCAmelCase_ : List[Any] = tf.math.top_k(A_ , k=A_)
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = topk.values.numpy(), topk.indices.numpy()
else:
lowerCAmelCase_ : Optional[Any] = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=A_).squeeze(-1)
# Fill mask pipeline supports only one ${mask_token} per sample
lowerCAmelCase_ : Dict = outputs[0, masked_index, :]
lowerCAmelCase_ : Dict = logits.softmax(dim=-1)
if target_ids is not None:
lowerCAmelCase_ : str = probs[..., target_ids]
lowerCAmelCase_ , lowerCAmelCase_ : int = probs.topk(A_)
lowerCAmelCase_ : Union[str, Any] = []
lowerCAmelCase_ : Optional[int] = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist())):
lowerCAmelCase_ : int = []
for v, p in zip(_values , _predictions):
# Copy is important since we're going to modify this array in place
lowerCAmelCase_ : Dict = input_ids.numpy().copy()
if target_ids is not None:
lowerCAmelCase_ : str = target_ids[p].tolist()
lowerCAmelCase_ : List[Any] = p
# Filter padding out:
lowerCAmelCase_ : Tuple = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
lowerCAmelCase_ : Any = self.tokenizer.decode(A_ , skip_special_tokens=A_)
lowerCAmelCase_ : str = {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]), '''sequence''': sequence}
row.append(A_)
result.append(A_)
if single_mask:
return result[0]
return result
def UpperCAmelCase__ ( self : int , A_ : Any , A_ : List[Any]=None):
if isinstance(A_ , A_):
lowerCAmelCase_ : List[str] = [targets]
try:
lowerCAmelCase_ : Union[str, Any] = self.tokenizer.get_vocab()
except Exception:
lowerCAmelCase_ : str = {}
lowerCAmelCase_ : Any = []
for target in targets:
lowerCAmelCase_ : List[str] = vocab.get(A_ , A_)
if id_ is None:
lowerCAmelCase_ : Optional[int] = self.tokenizer(
A_ , add_special_tokens=A_ , return_attention_mask=A_ , return_token_type_ids=A_ , max_length=1 , truncation=A_ , )['''input_ids''']
if len(A_) == 0:
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
'''We cannot replace it with anything meaningful, ignoring it''')
continue
lowerCAmelCase_ : Union[str, Any] = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"""The specified target token `{target}` does not exist in the model vocabulary. """
F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_)}`.""")
target_ids.append(id_)
lowerCAmelCase_ : List[str] = list(set(A_))
if len(A_) == 0:
raise ValueError('''At least one target must be provided when passed.''')
lowerCAmelCase_ : Tuple = np.array(A_)
return target_ids
def UpperCAmelCase__ ( self : List[Any] , A_ : Optional[int]=None , A_ : Tuple=None):
lowerCAmelCase_ : int = {}
if targets is not None:
lowerCAmelCase_ : Optional[Any] = self.get_target_ids(A_ , A_)
lowerCAmelCase_ : str = target_ids
if top_k is not None:
lowerCAmelCase_ : int = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'''fill-mask''' , self.model.base_model_prefix , '''The tokenizer does not define a `mask_token`.''')
return {}, {}, postprocess_params
def __call__( self : str , A_ : Tuple , *A_ : Dict , **A_ : Optional[Any]):
lowerCAmelCase_ : Tuple = super().__call__(A_ , **A_)
if isinstance(A_ , A_) and len(A_) == 1:
return outputs[0]
return outputs
| 171 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__A = None
try:
import msvcrt
except ImportError:
__A = None
try:
import fcntl
except ImportError:
__A = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
__A = OSError
# Data
# ------------------------------------------------
__A = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
__A = """3.0.12"""
__A = None
def UpperCamelCase ( ):
global _logger
__a = _logger or logging.getLogger(__name__ )
return _logger
class a ( A_ ):
def __init__( self : List[Any] , lowerCamelCase_ : Dict ) -> List[str]:
__a = lock_file
return None
def __str__( self : Tuple ) -> Dict:
__a = F"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class a :
def __init__( self : Tuple , lowerCamelCase_ : Dict ) -> List[Any]:
__a = lock
return None
def __enter__( self : Tuple ) -> List[str]:
return self.lock
def __exit__( self : Optional[Any] , lowerCamelCase_ : int , lowerCamelCase_ : str , lowerCamelCase_ : List[Any] ) -> str:
self.lock.release()
return None
class a :
def __init__( self : str , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Optional[Any]=-1 , lowerCamelCase_ : Optional[Any]=None ) -> Optional[Any]:
__a = max_filename_length if max_filename_length is not None else 2_55
# Hash the filename if it's too long
__a = self.hash_filename_if_too_long(lowerCamelCase_ , lowerCamelCase_ )
# The path to the lock file.
__a = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
__a = None
# The default timeout value.
__a = timeout
# We use this lock primarily for the lock counter.
__a = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
__a = 0
return None
@property
def lowerCAmelCase_ ( self : Optional[int] ) -> Dict:
return self._lock_file
@property
def lowerCAmelCase_ ( self : Tuple ) -> Optional[Any]:
return self._timeout
@timeout.setter
def lowerCAmelCase_ ( self : Optional[Any] , lowerCamelCase_ : int ) -> Optional[Any]:
__a = float(lowerCamelCase_ )
return None
def lowerCAmelCase_ ( self : int ) -> Dict:
raise NotImplementedError()
def lowerCAmelCase_ ( self : Any ) -> Any:
raise NotImplementedError()
@property
def lowerCAmelCase_ ( self : Optional[int] ) -> Any:
return self._lock_file_fd is not None
def lowerCAmelCase_ ( self : Any , lowerCamelCase_ : Union[str, Any]=None , lowerCamelCase_ : int=0.05 ) -> Tuple:
# Use the default timeout, if no timeout is provided.
if timeout is None:
__a = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
__a = id(self )
__a = self._lock_file
__a = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(F"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(F"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(F"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
F"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(lowerCamelCase_ )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
__a = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def lowerCAmelCase_ ( self : Dict , lowerCamelCase_ : int=False ) -> Tuple:
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
__a = id(self )
__a = self._lock_file
logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
__a = 0
logger().debug(F"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self : Dict ) -> int:
self.acquire()
return self
def __exit__( self : List[str] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Optional[Any] ) -> List[Any]:
self.release()
return None
def __del__( self : Union[str, Any] ) -> List[str]:
self.release(force=lowerCamelCase_ )
return None
def lowerCAmelCase_ ( self : Tuple , lowerCamelCase_ : str , lowerCamelCase_ : int ) -> str:
__a = os.path.basename(lowerCamelCase_ )
if len(lowerCamelCase_ ) > max_length and max_length > 0:
__a = os.path.dirname(lowerCamelCase_ )
__a = str(hash(lowerCamelCase_ ) )
__a = filename[: max_length - len(lowerCamelCase_ ) - 8] + """...""" + hashed_filename + """.lock"""
return os.path.join(lowerCamelCase_ , lowerCamelCase_ )
else:
return path
class a ( A_ ):
def __init__( self : Optional[Any] , lowerCamelCase_ : Dict , lowerCamelCase_ : Optional[Any]=-1 , lowerCamelCase_ : Any=None ) -> Optional[Any]:
from .file_utils import relative_to_absolute_path
super().__init__(lowerCamelCase_ , timeout=lowerCamelCase_ , max_filename_length=lowerCamelCase_ )
__a = """\\\\?\\""" + relative_to_absolute_path(self.lock_file )
def lowerCAmelCase_ ( self : List[Any] ) -> List[str]:
__a = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
__a = os.open(self._lock_file , lowerCamelCase_ )
except OSError:
pass
else:
try:
msvcrt.locking(lowerCamelCase_ , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(lowerCamelCase_ )
else:
__a = fd
return None
def lowerCAmelCase_ ( self : str ) -> Tuple:
__a = self._lock_file_fd
__a = None
msvcrt.locking(lowerCamelCase_ , msvcrt.LK_UNLCK , 1 )
os.close(lowerCamelCase_ )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class a ( A_ ):
def __init__( self : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : Optional[int]=-1 , lowerCamelCase_ : Tuple=None ) -> int:
__a = os.statvfs(os.path.dirname(lowerCamelCase_ ) ).f_namemax
super().__init__(lowerCamelCase_ , timeout=lowerCamelCase_ , max_filename_length=lowerCamelCase_ )
def lowerCAmelCase_ ( self : Optional[Any] ) -> Optional[Any]:
__a = os.O_RDWR | os.O_CREAT | os.O_TRUNC
__a = os.open(self._lock_file , lowerCamelCase_ )
try:
fcntl.flock(lowerCamelCase_ , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(lowerCamelCase_ )
else:
__a = fd
return None
def lowerCAmelCase_ ( self : Any ) -> str:
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
__a = self._lock_file_fd
__a = None
fcntl.flock(lowerCamelCase_ , fcntl.LOCK_UN )
os.close(lowerCamelCase_ )
return None
class a ( A_ ):
def lowerCAmelCase_ ( self : Optional[int] ) -> Any:
__a = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
__a = os.open(self._lock_file , lowerCamelCase_ )
except OSError:
pass
else:
__a = fd
return None
def lowerCAmelCase_ ( self : str ) -> Optional[int]:
os.close(self._lock_file_fd )
__a = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
__A = None
if msvcrt:
__A = WindowsFileLock
elif fcntl:
__A = UnixFileLock
else:
__A = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 173 | """simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
"""Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}
class a ( A_ ):
A_ : Optional[Any] = '''instructblip_vision_model'''
def __init__( self : Dict , lowerCamelCase_ : Union[str, Any]=14_08 , lowerCamelCase_ : List[str]=61_44 , lowerCamelCase_ : int=39 , lowerCamelCase_ : Optional[int]=16 , lowerCamelCase_ : Any=2_24 , lowerCamelCase_ : str=14 , lowerCamelCase_ : Union[str, Any]="gelu" , lowerCamelCase_ : str=1E-6 , lowerCamelCase_ : str=0.0 , lowerCamelCase_ : int=1E-10 , lowerCamelCase_ : Dict=True , **lowerCamelCase_ : str , ) -> Optional[Any]:
super().__init__(**lowerCamelCase_ )
__a = hidden_size
__a = intermediate_size
__a = num_hidden_layers
__a = num_attention_heads
__a = patch_size
__a = image_size
__a = initializer_range
__a = attention_dropout
__a = layer_norm_eps
__a = hidden_act
__a = qkv_bias
@classmethod
def lowerCAmelCase_ ( cls : Tuple , lowerCamelCase_ : Union[str, os.PathLike] , **lowerCamelCase_ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowerCamelCase_ )
__a , __a = cls.get_config_dict(lowerCamelCase_ , **lowerCamelCase_ )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
__a = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCamelCase_ , **lowerCamelCase_ )
class a ( A_ ):
A_ : str = '''instructblip_qformer'''
def __init__( self : Dict , lowerCamelCase_ : Union[str, Any]=3_05_22 , lowerCamelCase_ : Tuple=7_68 , lowerCamelCase_ : Any=12 , lowerCamelCase_ : Union[str, Any]=12 , lowerCamelCase_ : List[str]=30_72 , lowerCamelCase_ : Optional[Any]="gelu" , lowerCamelCase_ : Dict=0.1 , lowerCamelCase_ : Any=0.1 , lowerCamelCase_ : List[str]=5_12 , lowerCamelCase_ : Optional[Any]=0.02 , lowerCamelCase_ : int=1E-12 , lowerCamelCase_ : List[Any]=0 , lowerCamelCase_ : Union[str, Any]="absolute" , lowerCamelCase_ : Tuple=2 , lowerCamelCase_ : Union[str, Any]=14_08 , **lowerCamelCase_ : Any , ) -> Optional[int]:
super().__init__(pad_token_id=lowerCamelCase_ , **lowerCamelCase_ )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = cross_attention_frequency
__a = encoder_hidden_size
@classmethod
def lowerCAmelCase_ ( cls : str , lowerCamelCase_ : Union[str, os.PathLike] , **lowerCamelCase_ : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(lowerCamelCase_ )
__a , __a = cls.get_config_dict(lowerCamelCase_ , **lowerCamelCase_ )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("""model_type""" ) == "instructblip":
__a = config_dict["""qformer_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCamelCase_ , **lowerCamelCase_ )
class a ( A_ ):
A_ : Any = '''instructblip'''
A_ : Union[str, Any] = True
def __init__( self : List[Any] , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : List[str]=32 , **lowerCamelCase_ : Optional[int] ) -> List[Any]:
super().__init__(**lowerCamelCase_ )
if vision_config is None:
__a = {}
logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values.""" )
if qformer_config is None:
__a = {}
logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.""" )
if text_config is None:
__a = {}
logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`).""" )
__a = InstructBlipVisionConfig(**lowerCamelCase_ )
__a = InstructBlipQFormerConfig(**lowerCamelCase_ )
__a = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
__a = CONFIG_MAPPING[text_model_type](**lowerCamelCase_ )
__a = self.text_config.tie_word_embeddings
__a = self.text_config.is_encoder_decoder
__a = num_query_tokens
__a = self.vision_config.hidden_size
__a = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__a = 1.0
__a = 0.02
@classmethod
def lowerCAmelCase_ ( cls : Optional[int] , lowerCamelCase_ : InstructBlipVisionConfig , lowerCamelCase_ : InstructBlipQFormerConfig , lowerCamelCase_ : PretrainedConfig , **lowerCamelCase_ : Optional[Any] , ) -> Any:
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **lowerCamelCase_ , )
def lowerCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
__a = copy.deepcopy(self.__dict__ )
__a = self.vision_config.to_dict()
__a = self.qformer_config.to_dict()
__a = self.text_config.to_dict()
__a = self.__class__.model_type
return output
| 173 | 1 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
SCREAMING_SNAKE_CASE__ : Optional[int] = get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
SCREAMING_SNAKE_CASE__ : Any = get_tests_dir('''fixtures/vocab.json''')
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_tests_dir('''fixtures''')
class a__( unittest.TestCase ):
a_ : Union[str, Any] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
def _lowercase ( self ) -> Dict:
snake_case__ =0
def _lowercase ( self ) -> List[Any]:
snake_case__ =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =WavaVecaConfig()
snake_case__ =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
# save in new folder
model_config.save_pretrained(_UpperCAmelCase )
processor.save_pretrained(_UpperCAmelCase )
snake_case__ =AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> Tuple:
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , _UpperCAmelCase ) )
copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , 'vocab.json' ) )
snake_case__ =AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> Dict:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =WavaVecaFeatureExtractor()
snake_case__ =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
snake_case__ =WavaVecaProcessor(_UpperCAmelCase , _UpperCAmelCase )
# save in new folder
processor.save_pretrained(_UpperCAmelCase )
# drop `processor_class` in tokenizer
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , 'r' ) as f:
snake_case__ =json.load(_UpperCAmelCase )
config_dict.pop('processor_class' )
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , 'w' ) as f:
f.write(json.dumps(_UpperCAmelCase ) )
snake_case__ =AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =WavaVecaFeatureExtractor()
snake_case__ =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
snake_case__ =WavaVecaProcessor(_UpperCAmelCase , _UpperCAmelCase )
# save in new folder
processor.save_pretrained(_UpperCAmelCase )
# drop `processor_class` in feature extractor
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , 'r' ) as f:
snake_case__ =json.load(_UpperCAmelCase )
config_dict.pop('processor_class' )
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , 'w' ) as f:
f.write(json.dumps(_UpperCAmelCase ) )
snake_case__ =AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> List[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =WavaVecaConfig(processor_class='Wav2Vec2Processor' )
model_config.save_pretrained(_UpperCAmelCase )
# copy relevant files
copyfile(_UpperCAmelCase , os.path.join(_UpperCAmelCase , 'vocab.json' ) )
# create emtpy sample processor
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase ) , 'w' ) as f:
f.write('{}' )
snake_case__ =AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> int:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_UpperCAmelCase ):
snake_case__ =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_UpperCAmelCase ):
snake_case__ =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=_UpperCAmelCase )
snake_case__ =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' , trust_remote_code=_UpperCAmelCase )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
snake_case__ =processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
snake_case__ =processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
# Test we can also load the slow version
snake_case__ =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=_UpperCAmelCase , use_fast=_UpperCAmelCase )
snake_case__ =new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , 'NewTokenizer' )
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
def _lowercase ( self ) -> Optional[Any]:
try:
AutoConfig.register('custom' , _UpperCAmelCase )
AutoFeatureExtractor.register(_UpperCAmelCase , _UpperCAmelCase )
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase )
AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_UpperCAmelCase ):
AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
snake_case__ =CustomFeatureExtractor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ =os.path.join(_UpperCAmelCase , 'vocab.txt' )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
snake_case__ =CustomTokenizer(_UpperCAmelCase )
snake_case__ =CustomProcessor(_UpperCAmelCase , _UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(_UpperCAmelCase )
snake_case__ =AutoProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowercase ( self ) -> Union[str, Any]:
class a__( snake_case__ ):
a_ : Optional[int] = False
class a__( snake_case__ ):
a_ : List[Any] = False
class a__( snake_case__ ):
a_ : int = '''AutoFeatureExtractor'''
a_ : int = '''AutoTokenizer'''
a_ : List[str] = False
try:
AutoConfig.register('custom' , _UpperCAmelCase )
AutoFeatureExtractor.register(_UpperCAmelCase , _UpperCAmelCase )
AutoTokenizer.register(_UpperCAmelCase , slow_tokenizer_class=_UpperCAmelCase )
AutoProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# If remote code is not set, the default is to use local classes.
snake_case__ =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
snake_case__ =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=_UpperCAmelCase )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
snake_case__ =AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=_UpperCAmelCase )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowercase ( self ) -> Union[str, Any]:
snake_case__ =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(processor.__class__.__name__ , 'BertTokenizerFast' )
def _lowercase ( self ) -> List[Any]:
snake_case__ =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext' )
self.assertEqual(processor.__class__.__name__ , 'ConvNextImageProcessor' )
@is_staging_test
class a__( unittest.TestCase ):
a_ : Dict = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']
@classmethod
def _lowercase ( cls ) -> str:
snake_case__ =TOKEN
HfFolder.save_token(_UpperCAmelCase )
@classmethod
def _lowercase ( cls ) -> int:
try:
delete_repo(token=cls._token , repo_id='test-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-processor' )
except HTTPError:
pass
def _lowercase ( self ) -> int:
snake_case__ =WavaVecaProcessor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_UpperCAmelCase , 'test-processor' ) , push_to_hub=_UpperCAmelCase , use_auth_token=self._token )
snake_case__ =WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_UpperCAmelCase , getattr(new_processor.feature_extractor , _UpperCAmelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def _lowercase ( self ) -> List[str]:
snake_case__ =WavaVecaProcessor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(_UpperCAmelCase , 'test-processor-org' ) , push_to_hub=_UpperCAmelCase , use_auth_token=self._token , organization='valid_org' , )
snake_case__ =WavaVecaProcessor.from_pretrained('valid_org/test-processor-org' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(_UpperCAmelCase , getattr(new_processor.feature_extractor , _UpperCAmelCase ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def _lowercase ( self ) -> Any:
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
snake_case__ =CustomFeatureExtractor.from_pretrained(_UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ =os.path.join(_UpperCAmelCase , 'vocab.txt' )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
snake_case__ =CustomTokenizer(_UpperCAmelCase )
snake_case__ =CustomProcessor(_UpperCAmelCase , _UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(f"""{USER}/test-dynamic-processor""" , token=self._token )
snake_case__ =Repository(_UpperCAmelCase , clone_from=f"""{USER}/test-dynamic-processor""" , token=self._token )
processor.save_pretrained(_UpperCAmelCase )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(_UpperCAmelCase , 'tokenizer_config.json' ) ) as f:
snake_case__ =json.load(_UpperCAmelCase )
self.assertDictEqual(
tokenizer_config['auto_map'] , {
'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , 'custom_feature_extraction.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , 'custom_tokenization.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase , 'custom_processing.py' ) ) )
repo.push_to_hub()
snake_case__ =AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor""" , trust_remote_code=_UpperCAmelCase )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , 'CustomProcessor' )
| 538 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : Tuple = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[str] = [
'''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
SCREAMING_SNAKE_CASE__ : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 538 | 1 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
__lowerCamelCase : Tuple = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
__lowerCamelCase : Tuple = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
__lowerCamelCase : str = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ (datasets.Metric ):
"""simple docstring"""
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
] , )
def _A ( self :int ) -> Any:
'''simple docstring'''
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("float" ) ),
"references": datasets.Sequence(datasets.Value("float" ) ),
}
else:
return {
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
}
def _A ( self :Union[str, Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :Any , lowerCAmelCase__ :List[str]=None , lowerCAmelCase__ :str="uniform_average" , lowerCAmelCase__ :Union[str, Any]=True ) -> Tuple:
'''simple docstring'''
snake_case_ : Any = mean_squared_error(
lowerCAmelCase__ , lowerCAmelCase__ , sample_weight=lowerCAmelCase__ , multioutput=lowerCAmelCase__ , squared=lowerCAmelCase__ )
return {"mse": mse}
| 717 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__=None ,**__magic_name__ )-> Optional[Any]:
"""simple docstring"""
snake_case_ : int = [x.strip() for x in open(__magic_name__ ).readlines()]
snake_case_ : Optional[int] = [x.strip() for x in open(__magic_name__ ).readlines()][: len(__magic_name__ )]
snake_case_ : List[Any] = calculate_rouge(__magic_name__ ,__magic_name__ ,**__magic_name__ )
if save_path is not None:
save_json(__magic_name__ ,__magic_name__ ,indent=__magic_name__ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 656 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a ( __lowerCAmelCase ):
"""simple docstring"""
__lowerCAmelCase = ["""image_processor""", """tokenizer"""]
__lowerCAmelCase = """BridgeTowerImageProcessor"""
__lowerCAmelCase = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , snake_case_ , snake_case_ ):
'''simple docstring'''
super().__init__(snake_case_ , snake_case_ )
def __call__( self , snake_case_ , snake_case_ = None , snake_case_ = True , snake_case_ = False , snake_case_ = None , snake_case_ = None , snake_case_ = 0 , snake_case_ = None , snake_case_ = None , snake_case_ = None , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = False , snake_case_ = True , snake_case_ = None , **snake_case_ , ):
'''simple docstring'''
__UpperCAmelCase: Any = self.tokenizer(
text=snake_case_ , add_special_tokens=snake_case_ , padding=snake_case_ , truncation=snake_case_ , max_length=snake_case_ , stride=snake_case_ , pad_to_multiple_of=snake_case_ , return_token_type_ids=snake_case_ , return_attention_mask=snake_case_ , return_overflowing_tokens=snake_case_ , return_special_tokens_mask=snake_case_ , return_offsets_mapping=snake_case_ , return_length=snake_case_ , verbose=snake_case_ , return_tensors=snake_case_ , **snake_case_ , )
# add pixel_values + pixel_mask
__UpperCAmelCase: str = self.image_processor(
snake_case_ , return_tensors=snake_case_ , do_normalize=snake_case_ , do_center_crop=snake_case_ , **snake_case_ )
encoding.update(snake_case_ )
return encoding
def lowercase_ ( self , *snake_case_ , **snake_case_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*snake_case_ , **snake_case_ )
def lowercase_ ( self , *snake_case_ , **snake_case_ ):
'''simple docstring'''
return self.tokenizer.decode(*snake_case_ , **snake_case_ )
@property
def lowercase_ ( self ):
'''simple docstring'''
__UpperCAmelCase: str = 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 ) ) | 523 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE_ = 2_99_79_24_58
# Symbols
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = symbols('ct x y z')
def UpperCamelCase__ ( _lowercase : float ) -> float:
if velocity > c:
raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!""" )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError("""Speed must be greater than or equal to 1!""" )
return velocity / c
def UpperCamelCase__ ( _lowercase : float ) -> float:
return 1 / sqrt(1 - beta(_lowercase ) ** 2 )
def UpperCamelCase__ ( _lowercase : float ) -> np.ndarray:
return np.array(
[
[gamma(_lowercase ), -gamma(_lowercase ) * beta(_lowercase ), 0, 0],
[-gamma(_lowercase ) * beta(_lowercase ), gamma(_lowercase ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def UpperCamelCase__ ( _lowercase : float , _lowercase : np.ndarray | None = None ) -> np.ndarray:
# Ensure event is not empty
if event is None:
__UpperCAmelCase: List[str] = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(_lowercase ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
SCREAMING_SNAKE_CASE_ = transform(29_97_92_45)
print('Example of four vector: ')
print(F"""ct' = {four_vector[0]}""")
print(F"""x' = {four_vector[1]}""")
print(F"""y' = {four_vector[2]}""")
print(F"""z' = {four_vector[3]}""")
# Substitute symbols with numerical values
SCREAMING_SNAKE_CASE_ = {ct: c, x: 1, y: 1, z: 1}
SCREAMING_SNAKE_CASE_ = [four_vector[i].subs(sub_dict) for i in range(4)]
print(F"""\n{numerical_vector}""") | 523 | 1 |
'''simple docstring'''
from collections import namedtuple
import requests
from lxml import html # type: ignore
a : Dict = namedtuple('''covid_data''', '''cases deaths recovered''')
def __UpperCAmelCase ( _UpperCAmelCase : str = "https://www.worldometers.info/coronavirus/" ) -> Optional[int]:
__snake_case = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(UpperCamelCase__ ).content ).xpath(UpperCamelCase__ ) )
a : int = '''Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'''
print(fmt.format(*covid_stats()))
| 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a : str = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : int = [
'''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 : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 680 | 0 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
__snake_case = float('''nan''')
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = sys.stdout
UpperCamelCase__ :Any = open(UpperCamelCase_ , '''a''' )
def __getattr__( self , UpperCamelCase_ ):
'''simple docstring'''
return getattr(self.stdout , UpperCamelCase_ )
def lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
self.stdout.write(UpperCamelCase_ )
# strip tqdm codes
self.file.write(re.sub(r'''^.*\r''' , '''''' , UpperCamelCase_ , 0 , re.M ) )
def a ( __a=80 , __a=False ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = []
# deal with critical env vars
UpperCamelCase__ :Dict = ['''CUDA_VISIBLE_DEVICES''']
for key in env_keys:
UpperCamelCase__ :List[str] = os.environ.get(__a , __a )
if val is not None:
cmd.append(f'''{key}={val}''' )
# python executable (not always needed if the script is executable)
UpperCamelCase__ :Dict = sys.executable if full_python_path else sys.executable.split('''/''' )[-1]
cmd.append(__a )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
UpperCamelCase__ :Dict = []
UpperCamelCase__ :int = ''''''
while len(__a ) > 0:
current_line += f'''{cmd.pop(0 )} '''
if len(__a ) == 0 or len(__a ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(__a )
UpperCamelCase__ :Dict = ''''''
return "\\\n".join(__a )
def a ( __a , __a ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ :Tuple = re.sub(R'''[\\\n]+''' , ''' ''' , args.base_cmd )
# remove --output_dir if any and set our own
UpperCamelCase__ :List[Any] = re.sub('''--output_dir\s+[^\s]+''' , '''''' , args.base_cmd )
args.base_cmd += f''' --output_dir {output_dir}'''
# ensure we have --overwrite_output_dir
UpperCamelCase__ :Dict = re.sub('''--overwrite_output_dir\s+''' , '''''' , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def a ( __a , __a , __a , __a , __a , __a , __a ) -> Tuple:
'''simple docstring'''
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2] )} , )
UpperCamelCase__ :Tuple = subprocess.run(__a , capture_output=__a , text=__a )
if verbose:
print('''STDOUT''' , result.stdout )
print('''STDERR''' , result.stderr )
# save the streams
UpperCamelCase__ :List[Any] = variation.replace(''' ''' , '''-''' )
with open(Path(__a ) / f'''log.{prefix}.stdout.txt''' , '''w''' ) as f:
f.write(result.stdout )
with open(Path(__a ) / f'''log.{prefix}.stderr.txt''' , '''w''' ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print('''failed''' )
return {target_metric_key: nan}
with io.open(f'''{output_dir}/all_results.json''' , '''r''' , encoding='''utf-8''' ) as f:
UpperCamelCase__ :str = json.load(__a )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def a ( __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ :Tuple = []
UpperCamelCase__ :Dict = []
UpperCamelCase__ :Union[str, Any] = f'''{id}: {variation:<{longest_variation_len}}'''
UpperCamelCase__ :Optional[Any] = f'''{preamble}: '''
UpperCamelCase__ :Dict = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(__a ) , desc=__a , leave=__a ):
UpperCamelCase__ :Dict = process_run_single(
__a , __a , __a , __a , __a , __a , __a )
UpperCamelCase__ :str = single_run_metrics[target_metric_key]
if not math.isnan(__a ):
metrics.append(__a )
results.append(__a )
outcome += "✓"
else:
outcome += "✘"
UpperCamelCase__ :str = f'''\33[2K\r{outcome}'''
if len(__a ) > 0:
UpperCamelCase__ :Optional[Any] = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
UpperCamelCase__ :Any = round(mean_metrics[target_metric_key] , 2 )
UpperCamelCase__ :Optional[int] = f'''{outcome} {mean_target}'''
if len(__a ) > 1:
results_str += f''' {tuple(round(__a , 2 ) for x in results )}'''
print(__a )
UpperCamelCase__ :List[Any] = variation
return mean_metrics
else:
print(__a )
return {variation_key: variation, target_metric_key: nan}
def a ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = torch.cuda.get_device_properties(torch.device('''cuda''' ) )
return f'''
Datetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}
Software:
transformers: {transformers.__version__}
torch : {torch.__version__}
cuda : {torch.version.cuda}
python : {platform.python_version()}
Hardware:
{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB
'''
def a ( __a , __a , __a , __a , __a ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Dict = pd.DataFrame(__a )
UpperCamelCase__ :str = '''variation'''
UpperCamelCase__ :Tuple = '''diff_%'''
UpperCamelCase__ :Any = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
UpperCamelCase__ :int = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(__a ):
# as a fallback, use the minimal value as the sentinel
UpperCamelCase__ :Union[str, Any] = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(__a ):
UpperCamelCase__ :Dict = df.apply(
lambda __a : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis='''columns''' , )
# re-order columns
UpperCamelCase__ :List[str] = [variation_key, target_metric_key, diff_key, *report_metric_keys]
UpperCamelCase__ :List[Any] = df.reindex(__a , axis='''columns''' ) # reorder cols
# capitalize
UpperCamelCase__ :Optional[Any] = df.rename(str.capitalize , axis='''columns''' )
# make the cols as narrow as possible
UpperCamelCase__ :List[str] = df.rename(lambda __a : c.replace('''_''' , '''<br>''' ) , axis='''columns''' )
UpperCamelCase__ :Optional[int] = df.rename(lambda __a : c.replace('''_''' , '''\n''' ) , axis='''columns''' )
UpperCamelCase__ :List[str] = ['''''', '''Copy between the cut-here-lines and paste as is to github or a forum''']
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=__a , floatfmt='''.2f''' )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=__a , floatfmt='''.2f''' )]
print('''\n\n'''.join(__a ) )
def a ( ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :List[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--base-cmd''' , default=__a , type=__a , required=__a , help='''Base cmd''' , )
parser.add_argument(
'''--variations''' , default=__a , type=__a , nargs='''+''' , required=__a , help='''Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'''' , )
parser.add_argument(
'''--base-variation''' , default=__a , type=__a , help='''Baseline variation to compare to. if None the minimal target value will be used to compare against''' , )
parser.add_argument(
'''--target-metric-key''' , default=__a , type=__a , required=__a , help='''Target metric key in output_dir/all_results.json, e.g., train_samples_per_second''' , )
parser.add_argument(
'''--report-metric-keys''' , default='''''' , type=__a , help='''Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples''' , )
parser.add_argument(
'''--repeat-times''' , default=1 , type=__a , help='''How many times to re-run each variation - an average will be reported''' , )
parser.add_argument(
'''--output_dir''' , default='''output_benchmark''' , type=__a , help='''The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked''' , )
parser.add_argument(
'''--verbose''' , default=__a , action='''store_true''' , help='''Whether to show the outputs of each run or just the benchmark progress''' , )
UpperCamelCase__ :str = parser.parse_args()
UpperCamelCase__ :List[str] = args.output_dir
Path(__a ).mkdir(exist_ok=__a )
UpperCamelCase__ :Optional[int] = get_base_command(__a , __a )
# split each dimension into its --foo variations
UpperCamelCase__ :Union[str, Any] = [list(map(str.strip , re.split(R'''\|''' , __a ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
UpperCamelCase__ :int = list(map(str.strip , map(''' '''.join , itertools.product(*__a ) ) ) )
UpperCamelCase__ :Any = max(len(__a ) for x in variations )
# split wanted keys
UpperCamelCase__ :Optional[int] = args.report_metric_keys.split()
# capture prints into a log file for convenience
UpperCamelCase__ :List[str] = f'''benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt'''
print(f'''\nNote: each run\'s output is also logged under {output_dir}/log.*.std*.txt''' )
print(f'''and this script\'s output is also piped into {report_fn}''' )
UpperCamelCase__ :Optional[Any] = Tee(__a )
print(f'''\n*** Running {len(__a )} benchmarks:''' )
print(f'''Base command: {" ".join(__a )}''' )
UpperCamelCase__ :Any = '''variation'''
UpperCamelCase__ :Optional[Any] = []
for id, variation in enumerate(tqdm(__a , desc='''Total completion: ''' , leave=__a ) ):
UpperCamelCase__ :int = base_cmd + variation.split()
results.append(
process_run(
id + 1 , __a , __a , __a , __a , args.target_metric_key , __a , args.repeat_times , __a , args.verbose , ) )
process_results(__a , args.target_metric_key , __a , args.base_variation , __a )
if __name__ == "__main__":
main() | 189 |
'''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels | 189 | 1 |
def _A ( _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : Any = len(_UpperCamelCase ) + 1
_UpperCAmelCase : Union[str, Any] = len(_UpperCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
_UpperCAmelCase : Union[str, Any] = [[0 for i in range(_UpperCamelCase )] for j in range(_UpperCamelCase )]
# since string of zero length match pattern of zero length
_UpperCAmelCase : str = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , _UpperCamelCase ):
_UpperCAmelCase : List[str] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , _UpperCamelCase ):
_UpperCAmelCase : Dict = dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , _UpperCamelCase ):
for j in range(1 , _UpperCamelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
_UpperCAmelCase : Dict = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
_UpperCAmelCase : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
_UpperCAmelCase : Optional[Any] = dp[i - 1][j]
else:
_UpperCAmelCase : Tuple = 0
else:
_UpperCAmelCase : Tuple = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
UpperCAmelCase__ : Dict = 'aab'
UpperCAmelCase__ : List[str] = 'c*a*b'
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"""{input_string} matches the given pattern {pattern}""")
else:
print(F"""{input_string} does not match with the given pattern {pattern}""")
| 416 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
def _A ( _UpperCamelCase ):
if isinstance(_UpperCamelCase , np.ndarray ):
return list(tensor.shape )
_UpperCAmelCase : int = tf.shape(_UpperCamelCase )
if tensor.shape == tf.TensorShape(_UpperCamelCase ):
return dynamic
_UpperCAmelCase : Optional[int] = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(_UpperCamelCase )]
def _A ( _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = None ):
return tf.nn.softmax(logits=logits + 1e-9 , axis=_UpperCamelCase , name=_UpperCamelCase )
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=1e-5 , _UpperCamelCase=-1 ):
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.''' )
# Get mean and variance on the axis to be normalized
_UpperCAmelCase , _UpperCAmelCase : Tuple = tf.nn.moments(_UpperCamelCase , axes=[axis] , keepdims=_UpperCamelCase )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
_UpperCAmelCase : List[Any] = [1] * inputs.shape.rank
_UpperCAmelCase : Any = shape_list(_UpperCamelCase )[axis]
_UpperCAmelCase : Union[str, Any] = tf.reshape(_UpperCamelCase , _UpperCamelCase )
_UpperCAmelCase : Any = tf.reshape(_UpperCamelCase , _UpperCamelCase )
# Compute layer normalization using the batch_normalization
# function.
_UpperCAmelCase : Union[str, Any] = tf.nn.batch_normalization(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , offset=_UpperCamelCase , scale=_UpperCamelCase , variance_epsilon=_UpperCamelCase , )
return outputs
def _A ( _UpperCamelCase , _UpperCamelCase=0 , _UpperCamelCase=-1 ):
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
_UpperCAmelCase : str = tf.shape(_UpperCamelCase )
_UpperCAmelCase : Dict = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
_UpperCAmelCase : str = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(_UpperCamelCase , _UpperCamelCase )
def _A ( _UpperCamelCase ):
if not isinstance(_UpperCamelCase , tf.Tensor ):
_UpperCAmelCase : Any = tf.convert_to_tensor(_UpperCamelCase ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
_UpperCAmelCase : List[Any] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
_UpperCAmelCase : Dict = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
_UpperCAmelCase : Optional[Any] = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = "input_ids" ):
tf.debugging.assert_less(
_UpperCamelCase , tf.cast(_UpperCamelCase , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(_UpperCamelCase )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : int = 64_512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
_UpperCAmelCase : int = [x for x in data if len(_UpperCamelCase ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
'''The following attributes cannot be saved to HDF5 file because '''
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
_UpperCAmelCase : Dict = np.asarray(_UpperCamelCase )
_UpperCAmelCase : Any = 1
_UpperCAmelCase : Union[str, Any] = np.array_split(_UpperCamelCase , _UpperCamelCase )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
_UpperCAmelCase : Optional[Any] = np.array_split(_UpperCamelCase , _UpperCamelCase )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(_UpperCamelCase ):
_UpperCAmelCase : int = chunk_data
else:
_UpperCAmelCase : Optional[Any] = data
def _A ( _UpperCamelCase , _UpperCamelCase ):
if name in group.attrs:
_UpperCAmelCase : List[str] = [n.decode('''utf8''' ) if hasattr(_UpperCamelCase , '''decode''' ) else n for n in group.attrs[name]]
else:
_UpperCAmelCase : str = []
_UpperCAmelCase : int = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('''utf8''' ) if hasattr(_UpperCamelCase , '''decode''' ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]] )
chunk_id += 1
return data
def _A ( _UpperCamelCase ):
def _expand_single_ad_tensor(_UpperCamelCase ):
if isinstance(_UpperCamelCase , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(_UpperCamelCase , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , _UpperCamelCase )
| 416 | 1 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
_SCREAMING_SNAKE_CASE = "true"
def __a(SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str=82 , SCREAMING_SNAKE_CASE_ : Dict=16 ):
'''simple docstring'''
set_seed(42 )
_lowerCAmelCase = RegressionModel()
_lowerCAmelCase = deepcopy(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = RegressionDataset(length=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = DataLoader(SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
model.to(accelerator.device )
_lowerCAmelCase , _lowerCAmelCase = accelerator.prepare(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return model, ddp_model, dataloader
def __a(SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : int=False ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
_lowerCAmelCase = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(SCREAMING_SNAKE_CASE_ : List[str] ):
_lowerCAmelCase = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
with accelerator.main_process_first():
_lowerCAmelCase = dataset.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=["idx", "sentence1", "sentence2"] , )
_lowerCAmelCase = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE_ : str ):
if use_longest:
return tokenizer.pad(SCREAMING_SNAKE_CASE_ , padding="longest" , return_tensors="pt" )
return tokenizer.pad(SCREAMING_SNAKE_CASE_ , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(SCREAMING_SNAKE_CASE_ , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=16 )
def __a(SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
'''simple docstring'''
_lowerCAmelCase = Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE_ , split_batches=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = get_dataloader(SCREAMING_SNAKE_CASE_ , not dispatch_batches )
_lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase , _lowerCAmelCase = accelerator.prepare(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def __a(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] ):
'''simple docstring'''
_lowerCAmelCase = []
for batch in dataloader:
_lowerCAmelCase , _lowerCAmelCase = batch.values()
with torch.no_grad():
_lowerCAmelCase = model(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase , _lowerCAmelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
_lowerCAmelCase , _lowerCAmelCase = [], []
for logit, targ in logits_and_targets:
logits.append(SCREAMING_SNAKE_CASE_ )
targs.append(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase , _lowerCAmelCase = torch.cat(SCREAMING_SNAKE_CASE_ ), torch.cat(SCREAMING_SNAKE_CASE_ )
return logits, targs
def __a(SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : List[Any]=82 , SCREAMING_SNAKE_CASE_ : Optional[int]=False , SCREAMING_SNAKE_CASE_ : List[Any]=False , SCREAMING_SNAKE_CASE_ : Union[str, Any]=16 ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = get_basic_setup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase , _lowerCAmelCase = generate_predictions(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert (
len(SCREAMING_SNAKE_CASE_ ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(SCREAMING_SNAKE_CASE_ )}'''
def __a(SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : bool = False ):
'''simple docstring'''
_lowerCAmelCase = evaluate.load("glue" , "mrpc" )
_lowerCAmelCase , _lowerCAmelCase = get_mrpc_setup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# First do baseline
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = setup["no"]
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
for batch in dataloader:
batch.to(SCREAMING_SNAKE_CASE_ )
with torch.inference_mode():
_lowerCAmelCase = model(**SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE_ , references=batch["labels"] )
_lowerCAmelCase = metric.compute()
# Then do distributed
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
_lowerCAmelCase = model(**SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
_lowerCAmelCase = batch["labels"]
_lowerCAmelCase , _lowerCAmelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def __a():
'''simple docstring'''
_lowerCAmelCase = Accelerator(split_batches=SCREAMING_SNAKE_CASE_ , dispatch_batches=SCREAMING_SNAKE_CASE_ )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
_lowerCAmelCase = Accelerator(split_batches=SCREAMING_SNAKE_CASE_ , dispatch_batches=SCREAMING_SNAKE_CASE_ )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(SCREAMING_SNAKE_CASE_ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
_lowerCAmelCase = Accelerator()
test_torch_metrics(SCREAMING_SNAKE_CASE_ , 512 )
accelerator.state._reset_state()
def __a(SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 18 |
# 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a__ : Union[str, Any] = {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = [
"""MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MraForMaskedLM""",
"""MraForMultipleChoice""",
"""MraForQuestionAnswering""",
"""MraForSequenceClassification""",
"""MraForTokenClassification""",
"""MraLayer""",
"""MraModel""",
"""MraPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mra import (
MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraLayer,
MraModel,
MraPreTrainedModel,
)
else:
import sys
a__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 165 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_SCREAMING_SNAKE_CASE = data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE = data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE = data_utils
_SCREAMING_SNAKE_CASE = data_utils
def _lowerCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : str , lowerCamelCase_ : Dict , lowerCamelCase_ : Tuple ):
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(_lowerCamelCase , '''rb''' ) as fp:
__lowercase = pickle.load(_lowerCamelCase , encoding='''latin1''' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
__lowercase = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
print(f"Save vocabulary to {pytorch_vocab_dump_path}" )
__lowercase = corpus.vocab.__dict__
torch.save(_lowerCamelCase , _lowerCamelCase )
__lowercase = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' , _lowerCamelCase )
__lowercase = pytorch_dump_folder_path + "/" + CORPUS_NAME
print(f"Save dataset to {pytorch_dataset_dump_path}" )
torch.save(_lowerCamelCase , _lowerCamelCase )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
__lowercase = os.path.abspath(_lowerCamelCase )
__lowercase = os.path.abspath(_lowerCamelCase )
print(f"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}." )
# Initialise PyTorch model
if transfo_xl_config_file == "":
__lowercase = TransfoXLConfig()
else:
__lowercase = TransfoXLConfig.from_json_file(_lowerCamelCase )
print(f"Building PyTorch model from configuration: {config}" )
__lowercase = TransfoXLLMHeadModel(_lowerCamelCase )
__lowercase = load_tf_weights_in_transfo_xl(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Save pytorch-model
__lowercase = os.path.join(_lowerCamelCase , _lowerCamelCase )
__lowercase = os.path.join(_lowerCamelCase , _lowerCamelCase )
print(f"Save PyTorch model to {os.path.abspath(_lowerCamelCase )}" )
torch.save(model.state_dict() , _lowerCamelCase )
print(f"Save configuration file to {os.path.abspath(_lowerCamelCase )}" )
with open(_lowerCamelCase , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
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(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 705 |
'''simple docstring'''
import math
def _lowerCAmelCase ( lowerCamelCase_ : int ):
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
__lowercase = range(3 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _lowerCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any=1 , **lowerCamelCase_ : Tuple ):
__lowercase = factor * value
__lowercase = value
while not is_prime(lowerCamelCase_ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowerCamelCase_ )
return value
| 56 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : Optional[int] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
}
_lowerCAmelCase : int = {
'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}
_lowerCAmelCase : List[Any] = {
'ctrl': 2_5_6,
}
_lowerCAmelCase : List[str] = {
'Pregnancy': 1_6_8_6_2_9,
'Christianity': 7_6_7_5,
'Explain': 1_0_6_4_2_3,
'Fitness': 6_3_4_4_0,
'Saving': 6_3_1_6_3,
'Ask': 2_7_1_7_1,
'Ass': 9_5_9_8_5,
'Joke': 1_6_3_5_0_9,
'Questions': 4_5_6_2_2,
'Thoughts': 4_9_6_0_5,
'Retail': 5_2_3_4_2,
'Feminism': 1_6_4_3_3_8,
'Writing': 1_1_9_9_2,
'Atheism': 1_9_2_2_6_3,
'Netflix': 4_8_6_1_6,
'Computing': 3_9_6_3_9,
'Opinion': 4_3_2_1_3,
'Alone': 4_4_9_6_7,
'Funny': 5_8_9_1_7,
'Gaming': 4_0_3_5_8,
'Human': 4_0_8_8,
'India': 1_3_3_1,
'Joker': 7_7_1_3_8,
'Diet': 3_6_2_0_6,
'Legal': 1_1_8_5_9,
'Norman': 4_9_3_9,
'Tip': 7_2_6_8_9,
'Weight': 5_2_3_4_3,
'Movies': 4_6_2_7_3,
'Running': 2_3_4_2_5,
'Science': 2_0_9_0,
'Horror': 3_7_7_9_3,
'Confession': 6_0_5_7_2,
'Finance': 1_2_2_5_0,
'Politics': 1_6_3_6_0,
'Scary': 1_9_1_9_8_5,
'Support': 1_2_6_5_4,
'Technologies': 3_2_5_1_6,
'Teenage': 6_6_1_6_0,
'Event': 3_2_7_6_9,
'Learned': 6_7_4_6_0,
'Notion': 1_8_2_7_7_0,
'Wikipedia': 3_7_5_8_3,
'Books': 6_6_6_5,
'Extract': 7_6_0_5_0,
'Confessions': 1_0_2_7_0_1,
'Conspiracy': 7_5_9_3_2,
'Links': 6_3_6_7_4,
'Narcissus': 1_5_0_4_2_5,
'Relationship': 5_4_7_6_6,
'Relationships': 1_3_4_7_9_6,
'Reviews': 4_1_6_7_1,
'News': 4_2_5_6,
'Translation': 2_6_8_2_0,
'multilingual': 1_2_8_4_0_6,
}
def a_ ( UpperCamelCase_ : str ) -> Any:
"""simple docstring"""
lowerCamelCase = set()
lowerCamelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase = char
lowerCamelCase = set(UpperCamelCase_ )
return pairs
class lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
snake_case = VOCAB_FILES_NAMES
snake_case = PRETRAINED_VOCAB_FILES_MAP
snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case = CONTROL_CODES
def __init__( self : str , __snake_case : int , __snake_case : str , __snake_case : Any="<unk>" , **__snake_case : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(unk_token=__snake_case , **__snake_case )
with open(__snake_case , encoding='utf-8' ) as vocab_handle:
lowerCamelCase = json.load(__snake_case )
lowerCamelCase = {v: k for k, v in self.encoder.items()}
with open(__snake_case , encoding='utf-8' ) as merges_handle:
lowerCamelCase = merges_handle.read().split('\n' )[1:-1]
lowerCamelCase = [tuple(merge.split() ) for merge in merges]
lowerCamelCase = dict(zip(__snake_case , range(len(__snake_case ) ) ) )
lowerCamelCase = {}
@property
def lowerCamelCase__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
return len(self.encoder )
def lowerCamelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCamelCase__ ( self : Dict , __snake_case : Any ) -> Union[str, Any]:
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase = tuple(__snake_case )
lowerCamelCase = tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
lowerCamelCase = get_pairs(__snake_case )
if not pairs:
return token
while True:
lowerCamelCase = min(__snake_case , key=lambda __snake_case : self.bpe_ranks.get(__snake_case , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase , lowerCamelCase = bigram
lowerCamelCase = []
lowerCamelCase = 0
while i < len(__snake_case ):
try:
lowerCamelCase = word.index(__snake_case , __snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase = j
if word[i] == first and i < len(__snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase = tuple(__snake_case )
lowerCamelCase = new_word
if len(__snake_case ) == 1:
break
else:
lowerCamelCase = get_pairs(__snake_case )
lowerCamelCase = '@@ '.join(__snake_case )
lowerCamelCase = word[:-4]
lowerCamelCase = word
return word
def lowerCamelCase__ ( self : Optional[int] , __snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
lowerCamelCase = []
lowerCamelCase = re.findall(R'\S+\n?' , __snake_case )
for token in words:
split_tokens.extend(list(self.bpe(__snake_case ).split(' ' ) ) )
return split_tokens
def lowerCamelCase__ ( self : Optional[int] , __snake_case : Any ) -> List[str]:
'''simple docstring'''
return self.encoder.get(__snake_case , self.encoder.get(self.unk_token ) )
def lowerCamelCase__ ( self : Optional[Any] , __snake_case : Tuple ) -> Any:
'''simple docstring'''
return self.decoder.get(__snake_case , self.unk_token )
def lowerCamelCase__ ( self : Union[str, Any] , __snake_case : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = ' '.join(__snake_case ).replace('@@ ' , '' ).strip()
return out_string
def lowerCamelCase__ ( self : Tuple , __snake_case : str , __snake_case : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(__snake_case ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase = os.path.join(
__snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase = os.path.join(
__snake_case , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(__snake_case , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=__snake_case , ensure_ascii=__snake_case ) + '\n' )
lowerCamelCase = 0
with open(__snake_case , 'w' , encoding='utf-8' ) as writer:
writer.write('#version: 0.2\n' )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __snake_case : kv[1] ):
if index != token_index:
logger.warning(
F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
' Please check that the tokenizer is not corrupted!' )
lowerCamelCase = token_index
writer.write(' '.join(__snake_case ) + '\n' )
index += 1
return vocab_file, merge_file
# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
# return ''.join(tokens_generated_so_far)
| 246 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def a_ ( ) -> Dict:
"""simple docstring"""
lowerCamelCase = 1_0
lowerCamelCase = datasets.Features(
{
'tokens': datasets.Sequence(datasets.Value('string' ) ),
'labels': datasets.Sequence(datasets.ClassLabel(names=['negative', 'positive'] ) ),
'answers': datasets.Sequence(
{
'text': datasets.Value('string' ),
'answer_start': datasets.Value('int32' ),
} ),
'id': datasets.Value('int64' ),
} )
lowerCamelCase = datasets.Dataset.from_dict(
{
'tokens': [['foo'] * 5] * n,
'labels': [[1] * 5] * n,
'answers': [{'answer_start': [9_7], 'text': ['1976']}] * 1_0,
'id': list(range(UpperCamelCase_ ) ),
} , features=UpperCamelCase_ , )
return dataset
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Any , UpperCamelCase_ : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'file.arrow' )
dataset.map(cache_file_name=UpperCamelCase_ )
return filename
# FILE_CONTENT + files
_lowerCAmelCase : List[str] = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt'
lowerCamelCase = FILE_CONTENT
with open(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ )
return filename
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[Any] ) -> str:
"""simple docstring"""
import bza
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt.bz2'
lowerCamelCase = bytes(UpperCamelCase_ , 'utf-8' )
with bza.open(UpperCamelCase_ , 'wb' ) as f:
f.write(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[int] ) -> Dict:
"""simple docstring"""
import gzip
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'file.txt.gz' )
lowerCamelCase = bytes(UpperCamelCase_ , 'utf-8' )
with gzip.open(UpperCamelCase_ , 'wb' ) as f:
f.write(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt.lz4'
lowerCamelCase = bytes(UpperCamelCase_ , 'utf-8' )
with lza.frame.open(UpperCamelCase_ , 'wb' ) as f:
f.write(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any] ) -> Any:
"""simple docstring"""
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt.7z'
with pyazr.SevenZipFile(UpperCamelCase_ , 'w' ) as archive:
archive.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[Any] ) -> Optional[int]:
"""simple docstring"""
import tarfile
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt.tar'
with tarfile.TarFile(UpperCamelCase_ , 'w' ) as f:
f.add(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Dict ) -> Optional[Any]:
"""simple docstring"""
import lzma
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt.xz'
lowerCamelCase = bytes(UpperCamelCase_ , 'utf-8' )
with lzma.open(UpperCamelCase_ , 'wb' ) as f:
f.write(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : str , UpperCamelCase_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
import zipfile
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[str] ) -> int:
"""simple docstring"""
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.txt.zst'
lowerCamelCase = bytes(UpperCamelCase_ , 'utf-8' )
with zstd.open(UpperCamelCase_ , 'wb' ) as f:
f.write(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'file.xml'
lowerCamelCase = textwrap.dedent(
'\\n <?xml version="1.0" encoding="UTF-8" ?>\n <tmx version="1.4">\n <header segtype="sentence" srclang="ca" />\n <body>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang="en"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang="en"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang="en"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang="en"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang="ca"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang="en"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>' )
with open(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ )
return filename
_lowerCAmelCase : int = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
_lowerCAmelCase : Dict = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
_lowerCAmelCase : List[str] = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
_lowerCAmelCase : Tuple = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
_lowerCAmelCase : Union[str, Any] = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope='session' )
def a_ ( ) -> List[Any]:
"""simple docstring"""
return DATA_DICT_OF_LISTS
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Any ) -> List[str]:
"""simple docstring"""
lowerCamelCase = datasets.Dataset.from_dict(UpperCamelCase_ )
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.arrow' )
dataset.map(cache_file_name=UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[int] ) -> Any:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.sqlite' )
with contextlib.closing(sqlitea.connect(UpperCamelCase_ ) ) as con:
lowerCamelCase = con.cursor()
cur.execute('CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)' )
for item in DATA:
cur.execute('INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)' , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Dict ) -> List[Any]:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.csv' )
with open(UpperCamelCase_ , 'w' , newline='' ) as f:
lowerCamelCase = csv.DictWriter(UpperCamelCase_ , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset2.csv' )
with open(UpperCamelCase_ , 'w' , newline='' ) as f:
lowerCamelCase = csv.DictWriter(UpperCamelCase_ , fieldnames=['col_1', 'col_2', 'col_3'] )
writer.writeheader()
for item in DATA:
writer.writerow(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : str , UpperCamelCase_ : Any ) -> Optional[Any]:
"""simple docstring"""
import bza
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.csv.bz2'
with open(UpperCamelCase_ , 'rb' ) as f:
lowerCamelCase = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(UpperCamelCase_ , 'wb' ) as f:
f.write(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Tuple , UpperCamelCase_ : Dict , UpperCamelCase_ : Any ) -> Tuple:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : str ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.csv.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.basename(csv_path.replace('.csv' , '.CSV' ) ) )
f.write(UpperCamelCase_ , arcname=os.path.basename(csva_path.replace('.csv' , '.CSV' ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Optional[int] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.csv.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(UpperCamelCase_ ) ) )
f.write(UpperCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(UpperCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Dict ) -> List[str]:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.parquet' )
lowerCamelCase = pa.schema(
{
'col_1': pa.string(),
'col_2': pa.intaa(),
'col_3': pa.floataa(),
} )
with open(UpperCamelCase_ , 'wb' ) as f:
lowerCamelCase = pq.ParquetWriter(UpperCamelCase_ , schema=UpperCamelCase_ )
lowerCamelCase = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(UpperCamelCase_ ) )] for k in DATA[0]} , schema=UpperCamelCase_ )
writer.write_table(UpperCamelCase_ )
writer.close()
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
lowerCamelCase = {'data': DATA}
with open(UpperCamelCase_ , 'w' ) as f:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : str ) -> List[str]:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.json' )
lowerCamelCase = {'data': DATA_DICT_OF_LISTS}
with open(UpperCamelCase_ , 'w' ) as f:
json.dump(UpperCamelCase_ , UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl' )
with open(UpperCamelCase_ , 'w' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Tuple ) -> Tuple:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset2.jsonl' )
with open(UpperCamelCase_ , 'w' ) as f:
for item in DATA:
f.write(json.dumps(UpperCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset_312.jsonl' )
with open(UpperCamelCase_ , 'w' ) as f:
for item in DATA_312:
f.write(json.dumps(UpperCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[int] ) -> int:
"""simple docstring"""
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset-str.jsonl' )
with open(UpperCamelCase_ , 'w' ) as f:
for item in DATA_STR:
f.write(json.dumps(UpperCamelCase_ ) + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : str , UpperCamelCase_ : int ) -> Union[str, Any]:
"""simple docstring"""
import gzip
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt.gz' )
with open(UpperCamelCase_ , 'rb' ) as orig_file:
with gzip.open(UpperCamelCase_ , 'wb' ) as zipped_file:
zipped_file.writelines(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[int] ) -> Optional[int]:
"""simple docstring"""
import gzip
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.gz' )
with open(UpperCamelCase_ , 'rb' ) as orig_file:
with gzip.open(UpperCamelCase_ , 'wb' ) as zipped_file:
zipped_file.writelines(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Dict , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : int ) -> int:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.join('nested' , os.path.basename(UpperCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[str] , UpperCamelCase_ : List[str] , UpperCamelCase_ : Tuple ) -> int:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.jsonl.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(UpperCamelCase_ ) ) )
f.write(UpperCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(UpperCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : str , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : int ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.jsonl.tar'
with tarfile.TarFile(UpperCamelCase_ , 'w' ) as f:
f.add(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
f.add(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset_nested.jsonl.tar'
with tarfile.TarFile(UpperCamelCase_ , 'w' ) as f:
f.add(UpperCamelCase_ , arcname=os.path.join('nested' , os.path.basename(UpperCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase = ['0', '1', '2', '3']
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset.txt' )
with open(UpperCamelCase_ , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[Any] ) -> int:
"""simple docstring"""
lowerCamelCase = ['0', '1', '2', '3']
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset2.txt' )
with open(UpperCamelCase_ , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : int ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase = ['0', '1', '2', '3']
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.abc'
with open(UpperCamelCase_ , 'w' ) as f:
for item in data:
f.write(item + '\n' )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : Any ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.text.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset_with_dir.text.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(UpperCamelCase_ ) ) )
f.write(UpperCamelCase_ , arcname=os.path.join('main_dir' , os.path.basename(UpperCamelCase_ ) ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : str , UpperCamelCase_ : Tuple , UpperCamelCase_ : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.ext.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.basename('unsupported.ext' ) )
f.write(UpperCamelCase_ , arcname=os.path.basename('unsupported_2.ext' ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase = '\n'.join(['First', 'Second\u2029with Unicode new line', 'Third'] )
lowerCamelCase = str(tmp_path_factory.mktemp('data' ) / 'dataset_with_unicode_new_lines.txt' )
with open(UpperCamelCase_ , 'w' , encoding='utf-8' ) as f:
f.write(UpperCamelCase_ )
return path
@pytest.fixture(scope='session' )
def a_ ( ) -> List[str]:
"""simple docstring"""
return os.path.join('tests' , 'features' , 'data' , 'test_image_rgb.jpg' )
@pytest.fixture(scope='session' )
def a_ ( ) -> List[str]:
"""simple docstring"""
return os.path.join('tests' , 'features' , 'data' , 'test_audio_44100.wav' )
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Any ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data' ) / 'dataset.img.zip'
with zipfile.ZipFile(UpperCamelCase_ , 'w' ) as f:
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ) )
f.write(UpperCamelCase_ , arcname=os.path.basename(UpperCamelCase_ ).replace('.jpg' , '2.jpg' ) )
return path
@pytest.fixture(scope='session' )
def a_ ( UpperCamelCase_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase = tmp_path_factory.mktemp('data_dir' )
(data_dir / "subdir").mkdir()
with open(data_dir / 'subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 1_0 )
with open(data_dir / 'subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 1_0 )
# hidden file
with open(data_dir / 'subdir' / '.test.txt' , 'w' ) as f:
f.write('bar\n' * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / '.subdir' / 'train.txt' , 'w' ) as f:
f.write('foo\n' * 1_0 )
with open(data_dir / '.subdir' / 'test.txt' , 'w' ) as f:
f.write('bar\n' * 1_0 )
return data_dir
| 246 | 1 |
'''simple docstring'''
class __lowerCAmelCase :
def __init__(self ):
_UpperCAmelCase : Optional[int] = 0
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : int = {}
def snake_case_ (self , lowerCAmelCase__ ):
if vertex not in self.adjacency:
_UpperCAmelCase : Tuple = {}
self.num_vertices += 1
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
self.add_vertex(lowerCAmelCase__ )
self.add_vertex(lowerCAmelCase__ )
if head == tail:
return
_UpperCAmelCase : str = weight
_UpperCAmelCase : Tuple = weight
def snake_case_ (self ):
_UpperCAmelCase : Any = self.get_edges()
for edge in edges:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[Any] = edge
edges.remove((tail, head, weight) )
for i in range(len(lowerCAmelCase__ ) ):
_UpperCAmelCase : Union[str, Any] = list(edges[i] )
edges.sort(key=lambda lowerCAmelCase__ : e[2] )
for i in range(len(lowerCAmelCase__ ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
_UpperCAmelCase : Optional[Any] = edges[i][2] + 1
for edge in edges:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Tuple = edge
_UpperCAmelCase : int = weight
_UpperCAmelCase : Optional[int] = weight
def __str__(self ):
_UpperCAmelCase : Optional[int] = """"""
for tail in self.adjacency:
for head in self.adjacency[tail]:
_UpperCAmelCase : Optional[Any] = self.adjacency[head][tail]
string += F"{head} -> {tail} == {weight}\n"
return string.rstrip("""\n""" )
def snake_case_ (self ):
_UpperCAmelCase : Union[str, Any] = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def snake_case_ (self ):
return self.adjacency.keys()
@staticmethod
def snake_case_ (lowerCAmelCase__=None , lowerCAmelCase__=None ):
_UpperCAmelCase : Optional[Any] = Graph()
if vertices is None:
_UpperCAmelCase : Dict = []
if edges is None:
_UpperCAmelCase : List[Any] = []
for vertex in vertices:
g.add_vertex(lowerCAmelCase__ )
for edge in edges:
g.add_edge(*lowerCAmelCase__ )
return g
class __lowerCAmelCase :
def __init__(self ):
_UpperCAmelCase : Any = {}
_UpperCAmelCase : List[str] = {}
def __len__(self ):
return len(self.parent )
def snake_case_ (self , lowerCAmelCase__ ):
if item in self.parent:
return self.find(lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = item
_UpperCAmelCase : Tuple = 0
return item
def snake_case_ (self , lowerCAmelCase__ ):
if item not in self.parent:
return self.make_set(lowerCAmelCase__ )
if item != self.parent[item]:
_UpperCAmelCase : Dict = self.find(self.parent[item] )
return self.parent[item]
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Optional[int] = self.find(lowerCAmelCase__ )
_UpperCAmelCase : int = self.find(lowerCAmelCase__ )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
_UpperCAmelCase : Union[str, Any] = roota
return roota
if self.rank[roota] < self.rank[roota]:
_UpperCAmelCase : int = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
_UpperCAmelCase : int = roota
return roota
return None
@staticmethod
def snake_case_ (lowerCAmelCase__ ):
_UpperCAmelCase : Any = graph.num_vertices
_UpperCAmelCase : Union[str, Any] = Graph.UnionFind()
_UpperCAmelCase : str = []
while num_components > 1:
_UpperCAmelCase : Tuple = {}
for vertex in graph.get_vertices():
_UpperCAmelCase : Union[str, Any] = -1
_UpperCAmelCase : str = graph.get_edges()
for edge in edges:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : int = edge
edges.remove((tail, head, weight) )
for edge in edges:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = edge
_UpperCAmelCase : Tuple = union_find.find(lowerCAmelCase__ )
_UpperCAmelCase : Any = union_find.find(lowerCAmelCase__ )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_UpperCAmelCase : str = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
_UpperCAmelCase : Tuple = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[str] = cheap_edge[vertex]
if union_find.find(lowerCAmelCase__ ) != union_find.find(lowerCAmelCase__ ):
union_find.union(lowerCAmelCase__ , lowerCAmelCase__ )
mst_edges.append(cheap_edge[vertex] )
_UpperCAmelCase : List[Any] = num_components - 1
_UpperCAmelCase : int = Graph.build(edges=lowerCAmelCase__ )
return mst
| 156 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class __lowerCAmelCase ( __a ):
snake_case : torch.FloatTensor
snake_case : torch.FloatTensor
class __lowerCAmelCase ( __a , __a ):
snake_case : Optional[int] = 1
@register_to_config
def __init__(self , lowerCAmelCase__ = 2_0_0_0 , lowerCAmelCase__ = 0.1_5 , lowerCAmelCase__ = 0.0_1 , lowerCAmelCase__ = 1_3_4_8.0 , lowerCAmelCase__ = 1e-5 , lowerCAmelCase__ = 1 , ):
# standard deviation of the initial noise distribution
_UpperCAmelCase : int = sigma_max
# setable values
_UpperCAmelCase : Dict = None
self.set_sigmas(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ = None ):
return sample
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None ):
_UpperCAmelCase : List[Any] = sampling_eps if sampling_eps is not None else self.config.sampling_eps
_UpperCAmelCase : str = torch.linspace(1 , lowerCAmelCase__ , lowerCAmelCase__ , device=lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = None ):
_UpperCAmelCase : Optional[Any] = sigma_min if sigma_min is not None else self.config.sigma_min
_UpperCAmelCase : Any = sigma_max if sigma_max is not None else self.config.sigma_max
_UpperCAmelCase : int = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : int = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
_UpperCAmelCase : Optional[int] = torch.exp(torch.linspace(math.log(lowerCAmelCase__ ) , math.log(lowerCAmelCase__ ) , lowerCAmelCase__ ) )
_UpperCAmelCase : Optional[Any] = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = True , ):
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
_UpperCAmelCase : Optional[int] = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
_UpperCAmelCase : List[Any] = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
_UpperCAmelCase : Dict = timesteps.to(self.discrete_sigmas.device )
_UpperCAmelCase : Optional[int] = self.discrete_sigmas[timesteps].to(sample.device )
_UpperCAmelCase : Tuple = self.get_adjacent_sigma(lowerCAmelCase__ , lowerCAmelCase__ ).to(sample.device )
_UpperCAmelCase : List[Any] = torch.zeros_like(lowerCAmelCase__ )
_UpperCAmelCase : List[str] = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
_UpperCAmelCase : List[str] = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
_UpperCAmelCase : Tuple = diffusion.unsqueeze(-1 )
_UpperCAmelCase : Dict = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
_UpperCAmelCase : str = randn_tensor(
sample.shape , layout=sample.layout , generator=lowerCAmelCase__ , device=sample.device , dtype=sample.dtype )
_UpperCAmelCase : Tuple = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
_UpperCAmelCase : Union[str, Any] = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=lowerCAmelCase__ , prev_sample_mean=lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = True , ):
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
_UpperCAmelCase : str = randn_tensor(sample.shape , layout=sample.layout , generator=lowerCAmelCase__ ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
_UpperCAmelCase : Tuple = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
_UpperCAmelCase : str = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
_UpperCAmelCase : Optional[Any] = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
_UpperCAmelCase : Optional[Any] = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
_UpperCAmelCase : List[str] = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
_UpperCAmelCase : Union[str, Any] = step_size.unsqueeze(-1 )
_UpperCAmelCase : List[str] = sample + step_size * model_output
_UpperCAmelCase : Dict = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCAmelCase__ )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
_UpperCAmelCase : Union[str, Any] = timesteps.to(original_samples.device )
_UpperCAmelCase : Optional[Any] = self.discrete_sigmas.to(original_samples.device )[timesteps]
_UpperCAmelCase : int = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(lowerCAmelCase__ ) * sigmas[:, None, None, None]
)
_UpperCAmelCase : List[str] = noise + original_samples
return noisy_samples
def __len__(self ):
return self.config.num_train_timesteps
| 156 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ = {
"""configuration_layoutlmv3""": [
"""LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LayoutLMv3Config""",
"""LayoutLMv3OnnxConfig""",
],
"""processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
"""tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""LayoutLMv3TokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LayoutLMv3ForQuestionAnswering""",
"""LayoutLMv3ForSequenceClassification""",
"""LayoutLMv3ForTokenClassification""",
"""LayoutLMv3Model""",
"""LayoutLMv3PreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = [
"""TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLayoutLMv3ForQuestionAnswering""",
"""TFLayoutLMv3ForSequenceClassification""",
"""TFLayoutLMv3ForTokenClassification""",
"""TFLayoutLMv3Model""",
"""TFLayoutLMv3PreTrainedModel""",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ["""LayoutLMv3FeatureExtractor"""]
a_ = ["""LayoutLMv3ImageProcessor"""]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
a_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 221 | import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase__ :
"""simple docstring"""
@staticmethod
def _UpperCAmelCase ( *__lowerCAmelCase: Optional[int] , **__lowerCAmelCase: Dict ) -> Optional[int]:
'''simple docstring'''
pass
def __lowerCAmelCase ( A_ : Image ) -> str:
__UpperCAmelCase = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def _UpperCAmelCase ( self: List[Any] , __lowerCAmelCase: Dict , __lowerCAmelCase: List[str] , __lowerCAmelCase: List[Any] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase = DepthEstimationPipeline(model=__lowerCAmelCase , image_processor=__lowerCAmelCase )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def _UpperCAmelCase ( self: Optional[int] , __lowerCAmelCase: Optional[int] , __lowerCAmelCase: List[Any] ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )} , __lowerCAmelCase )
import datasets
__UpperCAmelCase = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
__UpperCAmelCase = depth_estimator(
[
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
] )
self.assertEqual(
[
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
{"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )},
] , __lowerCAmelCase , )
@require_tf
@unittest.skip("Depth estimation is not implemented in TF" )
def _UpperCAmelCase ( self: Union[str, Any] ) -> List[str]:
'''simple docstring'''
pass
@slow
@require_torch
def _UpperCAmelCase ( self: Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase = "Intel/dpt-large"
__UpperCAmelCase = pipeline("depth-estimation" , model=__lowerCAmelCase )
__UpperCAmelCase = depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
__UpperCAmelCase = hashimage(outputs["depth"] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item() ) , 2.662 )
@require_torch
def _UpperCAmelCase ( self: Union[str, Any] ) -> Tuple:
'''simple docstring'''
self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT" )
| 221 | 1 |
'''simple docstring'''
import argparse
_lowercase : Optional[int] = "docs/source/_static/js/custom.js"
def lowerCamelCase ( UpperCAmelCase__ : Tuple ) -> Dict:
with open(UpperCAmelCase__ , encoding="""utf-8""" , newline="""\n""" ) as f:
lowercase_ : Optional[int] = f.readlines()
lowercase_ : Tuple = 0
# First let's put the right version
while not lines[index].startswith("""const stableVersion =""" ):
index += 1
lowercase_ : Optional[Any] = F'''const stableVersion = "v{version}"\n'''
# Then update the dictionary
while not lines[index].startswith("""const versionMapping = {""" ):
index += 1
# We go until the end
while not lines[index].startswith("""}""" ):
index += 1
# We add the new version at the end
lines[index - 1] += F''' "v{version}": "v{version}",\n'''
with open(UpperCAmelCase__ , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(UpperCAmelCase__ )
if __name__ == "__main__":
_lowercase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--version", help="Release version.")
_lowercase : Dict = parser.parse_args()
update_custom_js(args.version)
| 715 | '''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __magic_name__ ( unittest.TestCase):
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : List[Any] = """ylacombe/bark-small"""
lowercase_ : List[str] = tempfile.mkdtemp()
lowercase_ : Tuple = """en_speaker_1"""
lowercase_ : Union[str, Any] = """This is a test string"""
lowercase_ : int = """speaker_embeddings_path.json"""
lowercase_ : Any = """speaker_embeddings"""
def SCREAMING_SNAKE_CASE_ ( self : Tuple , **lowercase_ : Optional[int] ):
return AutoTokenizer.from_pretrained(self.checkpoint , **lowercase_ )
def SCREAMING_SNAKE_CASE_ ( self : str ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ):
lowercase_ : Any = self.get_tokenizer()
lowercase_ : Optional[Any] = BarkProcessor(tokenizer=lowercase_ )
processor.save_pretrained(self.tmpdirname )
lowercase_ : Union[str, Any] = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
lowercase_ : Any = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
lowercase_ : List[str] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
lowercase_ : Optional[Any] = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ):
lowercase_ : Optional[int] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
lowercase_ : Optional[int] = 35
lowercase_ : int = 2
lowercase_ : Union[str, Any] = 8
lowercase_ : Union[str, Any] = {
"""semantic_prompt""": np.ones(lowercase_ ),
"""coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
"""fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
lowercase_ : str = processor(text=self.input_string , voice_preset=lowercase_ )
lowercase_ : Dict = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowercase_ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
lowercase_ : Any = os.path.join(self.tmpdirname , """file.npz""" )
np.savez(lowercase_ , **lowercase_ )
lowercase_ : Optional[Any] = processor(text=self.input_string , voice_preset=lowercase_ )
lowercase_ : List[Any] = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(lowercase_ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
lowercase_ : Union[str, Any] = processor(text=self.input_string , voice_preset=self.voice_preset )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ):
lowercase_ : List[str] = self.get_tokenizer()
lowercase_ : int = BarkProcessor(tokenizer=lowercase_ )
lowercase_ : Any = processor(text=self.input_string )
lowercase_ : List[str] = tokenizer(
self.input_string , padding="""max_length""" , max_length=256 , add_special_tokens=lowercase_ , return_attention_mask=lowercase_ , return_token_type_ids=lowercase_ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 30 | 0 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def lowerCamelCase__ ( __snake_case ) -> Dict:
"""simple docstring"""
return EnvironmentCommand()
def lowerCamelCase__ ( __snake_case ) -> Union[str, Any]:
"""simple docstring"""
return EnvironmentCommand(args.accelerate_config_file )
class _UpperCAmelCase( lowerCamelCase ):
@staticmethod
def UpperCAmelCase ( __a) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase = parser.add_parser('''env''')
download_parser.set_defaults(func=__a)
download_parser.add_argument(
'''--accelerate-config_file''' , default=__a , help='''The accelerate config file to use for the default values in the launching script.''' , )
download_parser.set_defaults(func=__a)
def __init__( self , __a , *__a) -> None:
'''simple docstring'''
_UpperCamelCase = accelerate_config_file
def UpperCAmelCase ( self) -> int:
'''simple docstring'''
_UpperCamelCase = '''not installed'''
if is_safetensors_available():
import safetensors
_UpperCamelCase = safetensors.__version__
elif importlib.util.find_spec('''safetensors''') is not None:
import safetensors
_UpperCamelCase = F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''
_UpperCamelCase = '''not installed'''
_UpperCamelCase = _UpperCamelCase = '''not found'''
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
_UpperCamelCase = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(__a):
_UpperCamelCase = load_config_from_file(self._accelerate_config_file).to_dict()
_UpperCamelCase = (
'''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()])
if isinstance(__a , __a)
else F'''\t{accelerate_config}'''
)
_UpperCamelCase = '''not installed'''
_UpperCamelCase = '''NA'''
if is_torch_available():
import torch
_UpperCamelCase = torch.__version__
_UpperCamelCase = torch.cuda.is_available()
_UpperCamelCase = '''not installed'''
_UpperCamelCase = '''NA'''
if is_tf_available():
import tensorflow as tf
_UpperCamelCase = tf.__version__
try:
# deprecated in v2.1
_UpperCamelCase = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
_UpperCamelCase = bool(tf.config.list_physical_devices('''GPU'''))
_UpperCamelCase = '''not installed'''
_UpperCamelCase = '''not installed'''
_UpperCamelCase = '''not installed'''
_UpperCamelCase = '''NA'''
if is_flax_available():
import flax
import jax
import jaxlib
_UpperCamelCase = flax.__version__
_UpperCamelCase = jax.__version__
_UpperCamelCase = jaxlib.__version__
_UpperCamelCase = jax.lib.xla_bridge.get_backend().platform
_UpperCamelCase = {
'''`transformers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Huggingface_hub version''': huggingface_hub.__version__,
'''Safetensors version''': F'''{safetensors_version}''',
'''Accelerate version''': F'''{accelerate_version}''',
'''Accelerate config''': F'''{accelerate_config_str}''',
'''PyTorch version (GPU?)''': F'''{pt_version} ({pt_cuda_available})''',
'''Tensorflow version (GPU?)''': F'''{tf_version} ({tf_cuda_available})''',
'''Flax version (CPU?/GPU?/TPU?)''': F'''{flax_version} ({jax_backend})''',
'''Jax version''': F'''{jax_version}''',
'''JaxLib version''': F'''{jaxlib_version}''',
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''')
print(self.format_dict(__a))
return info
@staticmethod
def UpperCAmelCase ( __a) -> Union[str, Any]:
'''simple docstring'''
return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]) + "\n"
| 19 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
SCREAMING_SNAKE_CASE_ = parser.parse_args()
SCREAMING_SNAKE_CASE_ = "cpu"
SCREAMING_SNAKE_CASE_ = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"
SCREAMING_SNAKE_CASE_ = "path-to-your-trained-model"
SCREAMING_SNAKE_CASE_ = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
SCREAMING_SNAKE_CASE_ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
SCREAMING_SNAKE_CASE_ = pipe.to(device)
# to channels last
SCREAMING_SNAKE_CASE_ = pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE_ = pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE_ = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
SCREAMING_SNAKE_CASE_ = pipe.safety_checker.to(memory_format=torch.channels_last)
# optimize with ipex
SCREAMING_SNAKE_CASE_ = torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE_ = torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE_ = torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE_ = (sample, timestep, encoder_hidden_status)
try:
SCREAMING_SNAKE_CASE_ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
SCREAMING_SNAKE_CASE_ = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE_ = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE_ = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
SCREAMING_SNAKE_CASE_ = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)
# compute
SCREAMING_SNAKE_CASE_ = 6_6_6
SCREAMING_SNAKE_CASE_ = torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE_ = {"generator": generator}
if args.steps is not None:
SCREAMING_SNAKE_CASE_ = args.steps
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
SCREAMING_SNAKE_CASE_ = pipe(prompt, **generate_kwargs).images[0]
# save image
image.save("generated.png")
| 597 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase = logging.get_logger(__name__)
__lowercase = {
"google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class _snake_case ( UpperCAmelCase_ , UpperCAmelCase_ ):
"""simple docstring"""
_UpperCamelCase : Any = '''bit'''
_UpperCamelCase : Union[str, Any] = ['''preactivation''', '''bottleneck''']
_UpperCamelCase : str = ['''SAME''', '''VALID''']
def __init__( self : List[str] , UpperCamelCase_ : int=3 , UpperCamelCase_ : List[str]=64 , UpperCamelCase_ : Optional[int]=[256, 512, 1024, 2048] , UpperCamelCase_ : Optional[int]=[3, 4, 6, 3] , UpperCamelCase_ : int="preactivation" , UpperCamelCase_ : Union[str, Any]="relu" , UpperCamelCase_ : List[Any]=None , UpperCamelCase_ : Tuple=32 , UpperCamelCase_ : Optional[int]=0.0 , UpperCamelCase_ : Union[str, Any]=False , UpperCamelCase_ : Dict=32 , UpperCamelCase_ : Union[str, Any]=1 , UpperCamelCase_ : List[Any]=None , UpperCamelCase_ : Any=None , **UpperCamelCase_ : str , ):
super().__init__(**_lowercase )
if layer_type not in self.layer_types:
raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types )}' )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
lowerCAmelCase_ : Any =global_padding.upper()
else:
raise ValueError(F'Padding strategy {global_padding} not supported' )
lowerCAmelCase_ : str =num_channels
lowerCAmelCase_ : Optional[Any] =embedding_size
lowerCAmelCase_ : Dict =hidden_sizes
lowerCAmelCase_ : str =depths
lowerCAmelCase_ : Optional[Any] =layer_type
lowerCAmelCase_ : int =hidden_act
lowerCAmelCase_ : Optional[int] =global_padding
lowerCAmelCase_ : Any =num_groups
lowerCAmelCase_ : Tuple =drop_path_rate
lowerCAmelCase_ : Tuple =embedding_dynamic_padding
lowerCAmelCase_ : Optional[int] =output_stride
lowerCAmelCase_ : int =width_factor
lowerCAmelCase_ : str =['''stem'''] + [F'stage{idx}' for idx in range(1 , len(_lowercase ) + 1 )]
lowerCAmelCase_ , lowerCAmelCase_ : Tuple =get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 703 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
_UpperCamelCase : Optional[Any] = '''owlvit_text_model'''
def __init__( self : Union[str, Any] , UpperCamelCase_ : str=49408 , UpperCamelCase_ : int=512 , UpperCamelCase_ : Dict=2048 , UpperCamelCase_ : Optional[int]=12 , UpperCamelCase_ : List[str]=8 , UpperCamelCase_ : List[str]=16 , UpperCamelCase_ : List[str]="quick_gelu" , UpperCamelCase_ : Any=1E-5 , UpperCamelCase_ : Any=0.0 , UpperCamelCase_ : Optional[Any]=0.0_2 , UpperCamelCase_ : Tuple=1.0 , UpperCamelCase_ : int=0 , UpperCamelCase_ : Optional[int]=49406 , UpperCamelCase_ : str=49407 , **UpperCamelCase_ : Tuple , ):
super().__init__(pad_token_id=UpperCamelCase_ , bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
lowerCAmelCase_ : Dict =vocab_size
lowerCAmelCase_ : Any =hidden_size
lowerCAmelCase_ : List[Any] =intermediate_size
lowerCAmelCase_ : Union[str, Any] =num_hidden_layers
lowerCAmelCase_ : List[str] =num_attention_heads
lowerCAmelCase_ : Optional[Any] =max_position_embeddings
lowerCAmelCase_ : str =hidden_act
lowerCAmelCase_ : Dict =layer_norm_eps
lowerCAmelCase_ : Dict =attention_dropout
lowerCAmelCase_ : Tuple =initializer_range
lowerCAmelCase_ : str =initializer_factor
@classmethod
def __A ( cls : str , UpperCamelCase_ : Union[str, os.PathLike] , **UpperCamelCase_ : Any ):
cls._set_token_in_kwargs(UpperCamelCase_ )
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] =cls.get_config_dict(UpperCamelCase_ , **UpperCamelCase_ )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
lowerCAmelCase_ : Optional[Any] =config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(UpperCamelCase_ , **UpperCamelCase_ )
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
_UpperCamelCase : Optional[int] = '''owlvit_vision_model'''
def __init__( self : int , UpperCamelCase_ : Tuple=768 , UpperCamelCase_ : Union[str, Any]=3072 , UpperCamelCase_ : Any=12 , UpperCamelCase_ : Tuple=12 , UpperCamelCase_ : Any=3 , UpperCamelCase_ : str=768 , UpperCamelCase_ : Dict=32 , UpperCamelCase_ : str="quick_gelu" , UpperCamelCase_ : int=1E-5 , UpperCamelCase_ : str=0.0 , UpperCamelCase_ : str=0.0_2 , UpperCamelCase_ : Optional[Any]=1.0 , **UpperCamelCase_ : Dict , ):
super().__init__(**UpperCamelCase_ )
lowerCAmelCase_ : Dict =hidden_size
lowerCAmelCase_ : List[str] =intermediate_size
lowerCAmelCase_ : Union[str, Any] =num_hidden_layers
lowerCAmelCase_ : str =num_attention_heads
lowerCAmelCase_ : Any =num_channels
lowerCAmelCase_ : Optional[Any] =image_size
lowerCAmelCase_ : Union[str, Any] =patch_size
lowerCAmelCase_ : int =hidden_act
lowerCAmelCase_ : Optional[int] =layer_norm_eps
lowerCAmelCase_ : Dict =attention_dropout
lowerCAmelCase_ : Tuple =initializer_range
lowerCAmelCase_ : Tuple =initializer_factor
@classmethod
def __A ( cls : Any , UpperCamelCase_ : Union[str, os.PathLike] , **UpperCamelCase_ : List[Any] ):
cls._set_token_in_kwargs(UpperCamelCase_ )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] =cls.get_config_dict(UpperCamelCase_ , **UpperCamelCase_ )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get('''model_type''' ) == "owlvit":
lowerCAmelCase_ : Tuple =config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(UpperCamelCase_ , **UpperCamelCase_ )
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
_UpperCamelCase : Dict = '''owlvit'''
_UpperCamelCase : int = True
def __init__( self : List[Any] , UpperCamelCase_ : Optional[int]=None , UpperCamelCase_ : int=None , UpperCamelCase_ : List[str]=512 , UpperCamelCase_ : Union[str, Any]=2.6_5_9_2 , UpperCamelCase_ : Optional[Any]=True , **UpperCamelCase_ : int , ):
super().__init__(**UpperCamelCase_ )
if text_config is None:
lowerCAmelCase_ : Any ={}
logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.''' )
if vision_config is None:
lowerCAmelCase_ : int ={}
logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.''' )
lowerCAmelCase_ : List[str] =OwlViTTextConfig(**UpperCamelCase_ )
lowerCAmelCase_ : Optional[int] =OwlViTVisionConfig(**UpperCamelCase_ )
lowerCAmelCase_ : List[str] =projection_dim
lowerCAmelCase_ : Optional[Any] =logit_scale_init_value
lowerCAmelCase_ : str =return_dict
lowerCAmelCase_ : Union[str, Any] =1.0
@classmethod
def __A ( cls : str , UpperCamelCase_ : Union[str, os.PathLike] , **UpperCamelCase_ : Optional[Any] ):
cls._set_token_in_kwargs(UpperCamelCase_ )
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] =cls.get_config_dict(UpperCamelCase_ , **UpperCamelCase_ )
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(UpperCamelCase_ , **UpperCamelCase_ )
@classmethod
def __A ( cls : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Dict , **UpperCamelCase_ : Union[str, Any] ):
lowerCAmelCase_ : List[str] ={}
lowerCAmelCase_ : Optional[int] =text_config
lowerCAmelCase_ : Optional[int] =vision_config
return cls.from_dict(UpperCamelCase_ , **UpperCamelCase_ )
def __A ( self : Optional[int] ):
lowerCAmelCase_ : List[Any] =copy.deepcopy(self.__dict__ )
lowerCAmelCase_ : str =self.text_config.to_dict()
lowerCAmelCase_ : Any =self.vision_config.to_dict()
lowerCAmelCase_ : str =self.__class__.model_type
return output
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
@property
def __A ( self : int ):
return OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
] )
@property
def __A ( self : int ):
return OrderedDict(
[
('''logits_per_image''', {0: '''batch'''}),
('''logits_per_text''', {0: '''batch'''}),
('''text_embeds''', {0: '''batch'''}),
('''image_embeds''', {0: '''batch'''}),
] )
@property
def __A ( self : Any ):
return 1E-4
def __A ( self : Tuple , UpperCamelCase_ : "ProcessorMixin" , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : Optional["TensorType"] = None , ):
lowerCAmelCase_ : Optional[int] =super().generate_dummy_inputs(
processor.tokenizer , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , framework=UpperCamelCase_ )
lowerCAmelCase_ : Union[str, Any] =super().generate_dummy_inputs(
processor.image_processor , batch_size=UpperCamelCase_ , framework=UpperCamelCase_ )
return {**text_input_dict, **image_input_dict}
@property
def __A ( self : List[Any] ):
return 14
| 305 | 0 |
'''simple docstring'''
def __UpperCamelCase ( lowercase_ : int ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __UpperCamelCase ( lowercase_ : int ):
"""simple docstring"""
a_ = 0
a_ = number
while duplicate > 0:
a_ = divmod(__a , 10 )
fact_sum += factorial(__a )
return fact_sum == number
if __name__ == "__main__":
print("Program to check whether a number is a Krisnamurthy Number or not.")
__lowerCAmelCase = int(input("Enter number: ").strip())
print(
f"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
)
| 536 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : Any = '''gpt_neo'''
__UpperCAmelCase : Optional[int] = ['''past_key_values''']
__UpperCAmelCase : Optional[int] = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : Optional[Any] ,_a : Optional[int]=5_0257 ,_a : Tuple=2048 ,_a : Optional[int]=2048 ,_a : Any=24 ,_a : Tuple=[[["global", "local"], 12]] ,_a : Union[str, Any]=16 ,_a : List[Any]=None ,_a : Optional[int]=256 ,_a : Optional[Any]="gelu_new" ,_a : List[Any]=0.0 ,_a : Optional[int]=0.0 ,_a : List[Any]=0.0 ,_a : Union[str, Any]=0.1 ,_a : Optional[Any]=1E-5 ,_a : Optional[Any]=0.02 ,_a : str=True ,_a : Any=5_0256 ,_a : Tuple=5_0256 ,**_a : List[str] ,):
'''simple docstring'''
_a : Dict = vocab_size
_a : Union[str, Any] = max_position_embeddings
_a : List[str] = hidden_size
_a : Optional[Any] = num_layers
_a : Optional[Any] = num_heads
_a : Dict = intermediate_size
_a : Any = window_size
_a : List[str] = activation_function
_a : int = resid_dropout
_a : Tuple = embed_dropout
_a : int = attention_dropout
_a : Dict = classifier_dropout
_a : Tuple = layer_norm_epsilon
_a : List[str] = initializer_range
_a : str = use_cache
_a : List[str] = bos_token_id
_a : Optional[Any] = eos_token_id
_a : Tuple = attention_types
_a : Union[str, Any] = self.expand_attention_types_params(_a )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.attention_layers)` == `config.num_layers` '
F"""but is `len(config.attention_layers) = {len(self.attention_layers )}`, """
F"""`config.num_layers = {self.num_layers}`. """
'`config.attention_layers` is prepared using `config.attention_types`. '
'Please verify the value of `config.attention_types` argument.' )
super().__init__(bos_token_id=_a ,eos_token_id=_a ,**_a )
@staticmethod
def __lowercase ( _a : Dict ):
'''simple docstring'''
_a : Dict = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def UpperCAmelCase_ (__a : str , __a : Optional[int] , __a : Tuple , __a : Dict ):
"""simple docstring"""
import torch
_a : Tuple = input.size()
_a : Union[str, Any] = len(__a )
_a : Union[str, Any] = shape[dimension]
_a : str = torch.arange(0 , __a , __a )
_a : Optional[Any] = torch.div(sizedim - size , __a , rounding_mode='floor' ) + 1
_a : str = torch.arange(__a ) + low_indices[:min_length][:, None]
_a : Optional[Any] = [slice(__a )] * rank
_a : Dict = indices
_a : List[str] = input[s]
_a : Optional[int] = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(__a )
def UpperCAmelCase_ (__a : str , __a : Optional[int] ):
"""simple docstring"""
import torch
_a : List[str] = torch.arange(1 , __a )
_a : int = torch.remainder(__a , __a )
_a : Tuple = remainders == 0
_a : Optional[Any] = candidates[divisor_indices]
_a : List[Any] = torch.max(__a )
return largest_divisor, torch.div(__a , __a , rounding_mode='floor' )
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
@property
def __lowercase ( self : Any ):
'''simple docstring'''
_a : Optional[Any] = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(_a ,direction='inputs' )
_a : Optional[int] = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_a : List[str] = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def __lowercase ( self : List[str] ):
'''simple docstring'''
return self._config.num_heads
def __lowercase ( self : Any ,_a : PreTrainedTokenizer ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional[TensorType] = None ,):
'''simple docstring'''
_a : Dict = super(_a ,self ).generate_dummy_inputs(
_a ,batch_size=_a ,seq_length=_a ,is_pair=_a ,framework=_a )
# We need to order the input in the way they appears in the forward()
_a : Union[str, Any] = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_a, _a : Dict = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_a : Any = seqlen + 2
_a : str = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_a : Tuple = [
(torch.zeros(_a ), torch.zeros(_a )) for _ in range(self.num_layers )
]
_a : List[str] = common_inputs['attention_mask']
if self.use_past:
_a : Optional[int] = ordered_inputs['attention_mask'].dtype
_a : Optional[Any] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(_a ,_a ,dtype=_a )] ,dim=1 )
return ordered_inputs
@property
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
return 13
| 229 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class lowerCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _lowerCamelCase ( self ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self ) -> Optional[Any]:
_A : Optional[int] = StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
_A : Any = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
sd_pipe.set_scheduler('''sample_euler''' )
_A : Optional[Any] = '''A painting of a squirrel eating a burger'''
_A : Optional[int] = torch.manual_seed(0 )
_A : Optional[int] = sd_pipe([prompt] , generator=UpperCAmelCase__ , guidance_scale=9.0 , num_inference_steps=2_0 , output_type='''np''' )
_A : List[Any] = output.images
_A : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_A : Any = np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _lowerCamelCase ( self ) -> Tuple:
_A : Optional[int] = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
_A : Optional[Any] = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
sd_pipe.set_scheduler('''sample_euler''' )
_A : Any = '''A painting of a squirrel eating a burger'''
_A : int = torch.manual_seed(0 )
_A : str = sd_pipe([prompt] , generator=UpperCAmelCase__ , guidance_scale=9.0 , num_inference_steps=2_0 , output_type='''np''' )
_A : int = output.images
_A : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_A : Tuple = np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-1
def _lowerCamelCase ( self ) -> Tuple:
_A : Tuple = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
_A : str = sd_pipe.to(UpperCAmelCase__ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
sd_pipe.set_scheduler('''sample_dpmpp_2m''' )
_A : str = '''A painting of a squirrel eating a burger'''
_A : Union[str, Any] = torch.manual_seed(0 )
_A : int = sd_pipe(
[prompt] , generator=UpperCAmelCase__ , guidance_scale=7.5 , num_inference_steps=1_5 , output_type='''np''' , use_karras_sigmas=UpperCAmelCase__ , )
_A : Any = output.images
_A : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_A : str = np.array(
[0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 718 |
'''simple docstring'''
def lowercase ( lowerCAmelCase : int = 100_0000):
"""simple docstring"""
_A : Any = 1
_A : str = 1
_A : Dict = {1: 1}
for inputa in range(2 , lowerCAmelCase):
_A : Any = 0
_A : str = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
_A : Any = (3 * number) + 1
counter += 1
if inputa not in counters:
_A : Dict = counter
if counter > pre_counter:
_A : List[Any] = inputa
_A : str = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 417 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
lowercase__: Optional[Any] = gray_code_sequence_string(__UpperCAmelCase )
#
# convert them to integers
for i in range(len(__UpperCAmelCase ) ):
lowercase__: int = int(sequence[i] , 2 )
return sequence
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
lowercase__: List[Any] = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
lowercase__: Dict = gray_code_sequence_string(bit_count - 1 )
lowercase__: Optional[Any] = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
lowercase__: str = '''0''' + smaller_sequence[i]
sequence.append(__UpperCAmelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
lowercase__: Tuple = '''1''' + smaller_sequence[i]
sequence.append(__UpperCAmelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 586 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__A = logging.get_logger(__name__)
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ):
warnings.warn(
'''The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use YolosImageProcessor instead.''' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 586 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
UpperCamelCase_ = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
UpperCamelCase_ = (
subprocess.check_output(f'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)
UpperCamelCase_ = """|""".join(sys.argv[1:])
UpperCamelCase_ = re.compile(rf'''^({joined_dirs}).*?\.py$''')
UpperCamelCase_ = [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")
| 88 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase_ = """▁"""
UpperCamelCase_ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = BigBirdTokenizer
lowerCamelCase_ = BigBirdTokenizerFast
lowerCamelCase_ = True
lowerCamelCase_ = True
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
super().setUp()
lowercase : Optional[int] =self.tokenizer_class(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowercase : Optional[int] ='''<s>'''
lowercase : int =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__ ) , UpperCAmelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__ ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<unk>''' )
self.assertEqual(vocab_keys[1] , '''<s>''' )
self.assertEqual(vocab_keys[-1] , '''[MASK]''' )
self.assertEqual(len(UpperCAmelCase__ ) , 1004 )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1000 )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowercase : Optional[int] =self.get_tokenizer()
lowercase : Any =self.get_rust_tokenizer()
lowercase : int ='''I was born in 92000, and this is falsé.'''
lowercase : List[str] =tokenizer.tokenize(UpperCAmelCase__ )
lowercase : Dict =rust_tokenizer.tokenize(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase : str =tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
lowercase : Union[str, Any] =rust_tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
lowercase : Optional[Any] =self.get_rust_tokenizer()
lowercase : Optional[Any] =tokenizer.encode(UpperCAmelCase__ )
lowercase : Union[str, Any] =rust_tokenizer.encode(UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
lowercase : Tuple =BigBirdTokenizer(UpperCAmelCase__ , keep_accents=UpperCAmelCase__ )
lowercase : Tuple =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(UpperCAmelCase__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase__ ) , [285, 46, 10, 170, 382] , )
lowercase : Tuple =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
lowercase : Any =tokenizer.convert_tokens_to_ids(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
lowercase : List[Any] =tokenizer.convert_ids_to_tokens(UpperCAmelCase__ )
self.assertListEqual(
UpperCAmelCase__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''' )
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : str ='''Hello World!'''
lowercase : Union[str, Any] =[65, 18536, 2260, 101, 66]
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@slow
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
lowercase : int =(
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
)
# fmt: off
lowercase : Tuple =[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66] # noqa: E231
# fmt: on
self.assertListEqual(UpperCAmelCase__ , self.big_tokenizer.encode(UpperCAmelCase__ ) )
@require_torch
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
lowercase : List[str] =list(self.big_tokenizer.get_vocab().keys() )[:10]
lowercase : Dict =''' '''.join(UpperCAmelCase__ )
lowercase : Union[str, Any] =self.big_tokenizer.encode_plus(UpperCAmelCase__ , return_tensors='''pt''' , return_token_type_ids=UpperCAmelCase__ )
lowercase : Dict =self.big_tokenizer.batch_encode_plus(
[sequence + ''' ''' + sequence] , return_tensors='''pt''' , return_token_type_ids=UpperCAmelCase__ )
lowercase : Optional[int] =BigBirdConfig(attention_type='''original_full''' )
lowercase : Dict =BigBirdModel(UpperCAmelCase__ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**UpperCAmelCase__ )
model(**UpperCAmelCase__ )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowercase : Union[str, Any] =BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''' )
lowercase : Dict =tokenizer.decode(tokenizer('''Paris is the [MASK].''' ).input_ids )
self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]''' )
@slow
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
# fmt: off
lowercase : str ={'''input_ids''': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCAmelCase__ , model_name='''google/bigbird-roberta-base''' , revision='''215c99f1600e06f83acce68422f2035b2b5c3510''' , )
| 88 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class a__( unittest.TestCase ):
def _lowercase ( self ) -> List[str]:
snake_case__ =0
def _lowercase ( self ) -> Tuple:
snake_case__ =AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32' )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =Path(_UpperCAmelCase ) / 'preprocessor_config.json'
snake_case__ =Path(_UpperCAmelCase ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_UpperCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_UpperCAmelCase , 'w' ) )
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> Dict:
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =Path(_UpperCAmelCase ) / 'preprocessor_config.json'
snake_case__ =Path(_UpperCAmelCase ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_UpperCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_UpperCAmelCase , 'w' ) )
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =CLIPConfig()
# Create a dummy config file with image_proceesor_type
snake_case__ =Path(_UpperCAmelCase ) / 'preprocessor_config.json'
snake_case__ =Path(_UpperCAmelCase ) / 'config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_UpperCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_UpperCAmelCase , 'w' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase ).to_dict()
config_dict.pop('image_processor_type' )
snake_case__ =CLIPImageProcessor(**_UpperCAmelCase )
# save in new folder
model_config.save_pretrained(_UpperCAmelCase )
config.save_pretrained(_UpperCAmelCase )
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase )
# make sure private variable is not incorrectly saved
snake_case__ =json.loads(config.to_json_string() )
self.assertTrue('_processor_class' not in dict_as_saved )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> str:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =Path(_UpperCAmelCase ) / 'preprocessor_config.json'
json.dump(
{'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'} , open(_UpperCAmelCase , 'w' ) , )
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self ) -> Optional[int]:
with self.assertRaisesRegex(
_UpperCAmelCase , 'clip-base is not a local folder and is not a valid model identifier' ):
snake_case__ =AutoImageProcessor.from_pretrained('clip-base' )
def _lowercase ( self ) -> List[str]:
with self.assertRaisesRegex(
_UpperCAmelCase , r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ):
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase , revision='aaaaaa' )
def _lowercase ( self ) -> List[str]:
with self.assertRaisesRegex(
_UpperCAmelCase , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ):
snake_case__ =AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model' )
def _lowercase ( self ) -> Union[str, Any]:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_UpperCAmelCase ):
snake_case__ =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_UpperCAmelCase ):
snake_case__ =AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_UpperCAmelCase )
snake_case__ =AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_UpperCAmelCase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_UpperCAmelCase )
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase , trust_remote_code=_UpperCAmelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , 'NewImageProcessor' )
def _lowercase ( self ) -> Any:
try:
AutoConfig.register('custom' , _UpperCAmelCase )
AutoImageProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_UpperCAmelCase ):
AutoImageProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ =Path(_UpperCAmelCase ) / 'preprocessor_config.json'
snake_case__ =Path(_UpperCAmelCase ) / 'config.json'
json.dump(
{'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'} , open(_UpperCAmelCase , 'w' ) , )
json.dump({'model_type': 'clip'} , open(_UpperCAmelCase , 'w' ) )
snake_case__ =CustomImageProcessor.from_pretrained(_UpperCAmelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_UpperCAmelCase )
snake_case__ =AutoImageProcessor.from_pretrained(_UpperCAmelCase )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def _lowercase ( self ) -> Optional[Any]:
class a__( snake_case__ ):
a_ : int = True
try:
AutoConfig.register('custom' , _UpperCAmelCase )
AutoImageProcessor.register(_UpperCAmelCase , _UpperCAmelCase )
# If remote code is not set, the default is to use local
snake_case__ =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor' )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
snake_case__ =AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_UpperCAmelCase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
snake_case__ =AutoImageProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_image_processor' , trust_remote_code=_UpperCAmelCase )
self.assertEqual(image_processor.__class__.__name__ , 'NewImageProcessor' )
self.assertTrue(not hasattr(_UpperCAmelCase , 'is_local' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 538 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class a__:
a_ : CommonSchedulerState
# setable values
a_ : jnp.ndarray
a_ : jnp.ndarray
a_ : Optional[int] = None
@classmethod
def _lowercase ( cls , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Dict:
return cls(common=_UpperCAmelCase , init_noise_sigma=_UpperCAmelCase , timesteps=_UpperCAmelCase )
@dataclass
class a__( snake_case__ ):
a_ : DDPMSchedulerState
class a__( snake_case__ , snake_case__ ):
a_ : Union[str, Any] = [e.name for e in FlaxKarrasDiffusionSchedulers]
a_ : jnp.dtype
@property
def _lowercase ( self ) -> Union[str, Any]:
return True
@register_to_config
def __init__( self , _UpperCAmelCase = 1000 , _UpperCAmelCase = 0.0_001 , _UpperCAmelCase = 0.02 , _UpperCAmelCase = "linear" , _UpperCAmelCase = None , _UpperCAmelCase = "fixed_small" , _UpperCAmelCase = True , _UpperCAmelCase = "epsilon" , _UpperCAmelCase = jnp.floataa , ) -> Union[str, Any]:
snake_case__ =dtype
def _lowercase ( self , _UpperCAmelCase = None ) -> DDPMSchedulerState:
if common is None:
snake_case__ =CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
snake_case__ =jnp.array(1.0 , dtype=self.dtype )
snake_case__ =jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=_UpperCAmelCase , init_noise_sigma=_UpperCAmelCase , timesteps=_UpperCAmelCase , )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None ) -> jnp.ndarray:
return sample
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = () ) -> DDPMSchedulerState:
snake_case__ =self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
snake_case__ =(jnp.arange(0 , _UpperCAmelCase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=_UpperCAmelCase , timesteps=_UpperCAmelCase , )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None ) -> Optional[Any]:
snake_case__ =state.common.alphas_cumprod[t]
snake_case__ =jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
snake_case__ =(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
snake_case__ =self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
snake_case__ =jnp.clip(_UpperCAmelCase , a_min=1E-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
snake_case__ =jnp.log(jnp.clip(_UpperCAmelCase , a_min=1E-20 ) )
elif variance_type == "fixed_large":
snake_case__ =state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
snake_case__ =jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
snake_case__ =variance
snake_case__ =state.common.betas[t]
snake_case__ =(predicted_variance + 1) / 2
snake_case__ =frac * max_log + (1 - frac) * min_log
return variance
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = True , ) -> Union[FlaxDDPMSchedulerOutput, Tuple]:
snake_case__ =timestep
if key is None:
snake_case__ =jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
snake_case__ , snake_case__ =jnp.split(_UpperCAmelCase , sample.shape[1] , axis=1 )
else:
snake_case__ =None
# 1. compute alphas, betas
snake_case__ =state.common.alphas_cumprod[t]
snake_case__ =jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
snake_case__ =1 - alpha_prod_t
snake_case__ =1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
snake_case__ =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
snake_case__ =model_output
elif self.config.prediction_type == "v_prediction":
snake_case__ =(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
' for the FlaxDDPMScheduler.' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
snake_case__ =jnp.clip(_UpperCAmelCase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case__ =(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
snake_case__ =state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
snake_case__ =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
snake_case__ =jax.random.split(_UpperCAmelCase , num=1 )
snake_case__ =jax.random.normal(_UpperCAmelCase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(_UpperCAmelCase , _UpperCAmelCase , predicted_variance=_UpperCAmelCase ) ** 0.5) * noise
snake_case__ =jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
snake_case__ =pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase , state=_UpperCAmelCase )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> jnp.ndarray:
return add_noise_common(state.common , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def _lowercase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ) -> jnp.ndarray:
return get_velocity_common(state.common , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def __len__( self ) -> Optional[int]:
return self.config.num_train_timesteps
| 538 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger(__name__)
def _lowerCAmelCase ( __lowerCAmelCase ) -> Dict:
"""simple docstring"""
snake_case__ : Tuple = DPTConfig(embedding_type='''hybrid''' )
if "large" in checkpoint_url:
snake_case__ : Union[str, Any] = 1024
snake_case__ : int = 4096
snake_case__ : List[Any] = 24
snake_case__ : int = 16
snake_case__ : Optional[int] = [5, 11, 17, 23]
snake_case__ : Dict = [256, 512, 1024, 1024]
snake_case__ : List[Any] = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
snake_case__ : Optional[int] = 768
snake_case__ : Optional[int] = [1, 1, 1, 0.5]
snake_case__ : List[str] = [256, 512, 768, 768]
snake_case__ : List[Any] = 150
snake_case__ : Union[str, Any] = 16
snake_case__ : Optional[int] = (1, 384, 384)
snake_case__ : List[str] = False
snake_case__ : Any = '''project'''
if "ade" in checkpoint_url:
snake_case__ : Union[str, Any] = True
snake_case__ : str = 768
snake_case__ : Union[str, Any] = [1, 1, 1, 0.5]
snake_case__ : Any = 150
snake_case__ : Optional[int] = 16
snake_case__ : Union[str, Any] = '''huggingface/label-files'''
snake_case__ : List[str] = '''ade20k-id2label.json'''
snake_case__ : Union[str, Any] = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) )
snake_case__ : str = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
snake_case__ : int = idalabel
snake_case__ : Optional[Any] = {v: k for k, v in idalabel.items()}
snake_case__ : Any = [1, 150, 480, 480]
return config, expected_shape
def _lowerCAmelCase ( __lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : List[Any] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case__ : Any = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case__ : str = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case__ : Tuple = name.replace('''patch_embed''' , '''''' )
if "pos_embed" in name:
snake_case__ : int = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
snake_case__ : Optional[Any] = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case__ : Optional[Any] = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
snake_case__ : Optional[int] = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
snake_case__ : Optional[Any] = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case__ : Dict = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name and "backbone" not in name:
snake_case__ : int = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name and "backbone" not in name:
snake_case__ : Optional[int] = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case__ : Any = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
snake_case__ : Any = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
snake_case__ : Union[str, Any] = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
snake_case__ : str = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
snake_case__ : Optional[int] = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
snake_case__ : int = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
snake_case__ : List[Any] = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case__ : Tuple = name.replace(f"""refinenet{layer_idx}""" , f"""fusion_stage.layers.{abs(layer_idx-4 )}""" )
if "out_conv" in name:
snake_case__ : List[Any] = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
snake_case__ : Optional[int] = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case__ : int = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
snake_case__ : Optional[int] = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
snake_case__ : Tuple = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case__ : Optional[int] = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case__ : Tuple = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case__ : Optional[Any] = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case__ : Optional[int] = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case__ : Optional[int] = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case__ : int = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case__ : List[str] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case__ : Any = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case__ : Optional[Any] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case__ : Tuple = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case__ : Optional[int] = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case__ : int = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
snake_case__ : str = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
snake_case__ : Any = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
snake_case__ : Optional[int] = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
snake_case__ : List[str] = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
if "backbone" in name:
snake_case__ : List[Any] = name.replace('''backbone''' , '''backbone.bit.encoder''' )
if ".." in name:
snake_case__ : List[Any] = name.replace('''..''' , '''.''' )
if "stem.conv" in name:
snake_case__ : Tuple = name.replace('''stem.conv''' , '''bit.embedder.convolution''' )
if "blocks" in name:
snake_case__ : Optional[int] = name.replace('''blocks''' , '''layers''' )
if "convolution" in name and "backbone" in name:
snake_case__ : Dict = name.replace('''convolution''' , '''conv''' )
if "layer" in name and "backbone" in name:
snake_case__ : str = name.replace('''layer''' , '''layers''' )
if "backbone.bit.encoder.bit" in name:
snake_case__ : Union[str, Any] = name.replace('''backbone.bit.encoder.bit''' , '''backbone.bit''' )
if "embedder.conv" in name:
snake_case__ : str = name.replace('''embedder.conv''' , '''embedder.convolution''' )
if "backbone.bit.encoder.stem.norm" in name:
snake_case__ : List[Any] = name.replace('''backbone.bit.encoder.stem.norm''' , '''backbone.bit.embedder.norm''' )
return name
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ : str = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight""" )
snake_case__ : Tuple = state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Union[str, Any] = in_proj_weight[: config.hidden_size, :]
snake_case__ : Dict = in_proj_bias[: config.hidden_size]
snake_case__ : Any = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ : str = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ : str = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ : List[str] = in_proj_bias[-config.hidden_size :]
def _lowerCAmelCase ( ) -> int:
"""simple docstring"""
snake_case__ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case__ : Union[str, Any] = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
"""simple docstring"""
snake_case__ , snake_case__ : int = get_dpt_config(__lowerCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
snake_case__ : Dict = torch.load(__lowerCAmelCase , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(__lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
snake_case__ : str = state_dict.pop(__lowerCAmelCase )
snake_case__ : Optional[int] = val
# read in qkv matrices
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# load HuggingFace model
snake_case__ : Dict = DPTForSemanticSegmentation(__lowerCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
# Check outputs on an image
snake_case__ : Optional[Any] = 480 if '''ade''' in checkpoint_url else 384
snake_case__ : List[str] = DPTImageProcessor(size=__lowerCAmelCase )
snake_case__ : Optional[int] = prepare_img()
snake_case__ : str = image_processor(__lowerCAmelCase , return_tensors='''pt''' )
# forward pass
snake_case__ : Dict = model(**__lowerCAmelCase ).logits if '''ade''' in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth
if show_prediction:
snake_case__ : Union[str, Any] = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode='''bicubic''' , align_corners=__lowerCAmelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__lowerCAmelCase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__lowerCAmelCase )
if push_to_hub:
model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
if __name__ == "__main__":
A__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
type=str,
help='''URL of the original DPT checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=False,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
)
parser.add_argument(
'''--model_name''',
default='''dpt-large''',
type=str,
help='''Name of the model, in case you\'re pushing to the hub.''',
)
parser.add_argument(
'''--show_prediction''',
action='''store_true''',
)
A__ = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 219 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
A__ = logging.get_logger(__name__)
class a ( __lowerCamelCase ):
__lowerCAmelCase : List[str] = ["""pixel_values"""]
def __init__( self :List[str] ,__lowercase :bool = True ,__lowercase :Dict[str, int] = None ,__lowercase :PILImageResampling = PILImageResampling.BICUBIC ,__lowercase :bool = True ,__lowercase :Dict[str, int] = None ,__lowercase :bool = True ,__lowercase :Union[int, float] = 1 / 2_5_5 ,__lowercase :bool = True ,__lowercase :Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN ,__lowercase :Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD ,**__lowercase :Tuple ,):
super().__init__(**__lowercase )
snake_case__ : Optional[int] = size if size is not None else {'''shortest_edge''': 2_2_4}
snake_case__ : str = get_size_dict(__lowercase ,default_to_square=__lowercase )
snake_case__ : int = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
snake_case__ : Any = get_size_dict(__lowercase ,param_name='''crop_size''' )
snake_case__ : Optional[Any] = do_resize
snake_case__ : Any = size
snake_case__ : Optional[int] = resample
snake_case__ : Any = do_center_crop
snake_case__ : Dict = crop_size
snake_case__ : List[str] = do_rescale
snake_case__ : str = rescale_factor
snake_case__ : int = do_normalize
snake_case__ : Optional[Any] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
snake_case__ : Tuple = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __lowerCamelCase ( self :str ,__lowercase :np.ndarray ,__lowercase :Dict[str, int] ,__lowercase :PILImageResampling = PILImageResampling.BICUBIC ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :List[str] ,):
snake_case__ : List[Any] = get_size_dict(__lowercase ,default_to_square=__lowercase )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
snake_case__ : Any = int((2_5_6 / 2_2_4) * size['''shortest_edge'''] )
snake_case__ : Union[str, Any] = get_resize_output_image_size(__lowercase ,size=__lowercase ,default_to_square=__lowercase )
snake_case__ : Optional[Any] = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
__lowercase ,size=(size_dict['''height'''], size_dict['''width''']) ,resample=__lowercase ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :List[Any] ,__lowercase :np.ndarray ,__lowercase :Dict[str, int] ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :str ,):
snake_case__ : List[Any] = get_size_dict(__lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(__lowercase ,size=(size['''height'''], size['''width''']) ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :List[Any] ,__lowercase :np.ndarray ,__lowercase :Union[int, float] ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :Union[str, Any] ,):
return rescale(__lowercase ,scale=__lowercase ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :int ,__lowercase :np.ndarray ,__lowercase :Union[float, List[float]] ,__lowercase :Union[float, List[float]] ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :Tuple ,):
return normalize(__lowercase ,mean=__lowercase ,std=__lowercase ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :Union[str, Any] ,__lowercase :ImageInput ,__lowercase :Optional[bool] = None ,__lowercase :Optional[Dict[str, int]] = None ,__lowercase :PILImageResampling = None ,__lowercase :Optional[bool] = None ,__lowercase :Optional[Dict[str, int]] = None ,__lowercase :Optional[bool] = None ,__lowercase :Optional[float] = None ,__lowercase :Optional[bool] = None ,__lowercase :Optional[Union[float, Iterable[float]]] = None ,__lowercase :Optional[Union[float, Iterable[float]]] = None ,__lowercase :Optional[TensorType] = None ,__lowercase :ChannelDimension = ChannelDimension.FIRST ,**__lowercase :List[str] ,):
snake_case__ : Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case__ : Optional[Any] = resample if resample is not None else self.resample
snake_case__ : Any = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case__ : Dict = do_rescale if do_rescale is not None else self.do_rescale
snake_case__ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case__ : int = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ : Tuple = image_mean if image_mean is not None else self.image_mean
snake_case__ : Optional[Any] = image_std if image_std is not None else self.image_std
snake_case__ : Optional[Any] = size if size is not None else self.size
snake_case__ : Union[str, Any] = get_size_dict(__lowercase ,default_to_square=__lowercase )
snake_case__ : Any = crop_size if crop_size is not None else self.crop_size
snake_case__ : Dict = get_size_dict(__lowercase ,param_name='''crop_size''' )
snake_case__ : Union[str, Any] = make_list_of_images(__lowercase )
if not valid_images(__lowercase ):
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:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop 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.
snake_case__ : Any = [to_numpy_array(__lowercase ) for image in images]
if do_resize:
snake_case__ : List[Any] = [self.resize(__lowercase ,__lowercase ,__lowercase ) for image in images]
if do_center_crop:
snake_case__ : List[Any] = [self.center_crop(__lowercase ,__lowercase ) for image in images]
if do_rescale:
snake_case__ : Optional[int] = [self.rescale(__lowercase ,__lowercase ) for image in images]
if do_normalize:
snake_case__ : List[str] = [self.normalize(__lowercase ,__lowercase ,__lowercase ) for image in images]
snake_case__ : List[Any] = [to_channel_dimension_format(__lowercase ,__lowercase ) for image in images]
snake_case__ : Tuple = {'''pixel_values''': images}
return BatchFeature(data=__lowercase ,tensor_type=__lowercase )
| 219 | 1 |
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