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"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[Any] = {
'''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class a ( __snake_case ):
SCREAMING_SNAKE_CASE : Any = 'gptj'
SCREAMING_SNAKE_CASE : List[str] = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Any=50400 , __SCREAMING_SNAKE_CASE : List[str]=2048 , __SCREAMING_SNAKE_CASE : Dict=4096 , __SCREAMING_SNAKE_CASE : Union[str, Any]=28 , __SCREAMING_SNAKE_CASE : Dict=16 , __SCREAMING_SNAKE_CASE : int=64 , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : Union[str, Any]="gelu_new" , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : Any=0.0 , __SCREAMING_SNAKE_CASE : Tuple=0.0 , __SCREAMING_SNAKE_CASE : Dict=1e-5 , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : List[Any]=50256 , __SCREAMING_SNAKE_CASE : int=50256 , __SCREAMING_SNAKE_CASE : List[Any]=False , **__SCREAMING_SNAKE_CASE : List[Any] , ) -> Tuple:
lowerCamelCase_ = vocab_size
lowerCamelCase_ = n_positions
lowerCamelCase_ = n_embd
lowerCamelCase_ = n_layer
lowerCamelCase_ = n_head
lowerCamelCase_ = n_inner
lowerCamelCase_ = rotary_dim
lowerCamelCase_ = activation_function
lowerCamelCase_ = resid_pdrop
lowerCamelCase_ = embd_pdrop
lowerCamelCase_ = attn_pdrop
lowerCamelCase_ = layer_norm_epsilon
lowerCamelCase_ = initializer_range
lowerCamelCase_ = use_cache
lowerCamelCase_ = bos_token_id
lowerCamelCase_ = eos_token_id
super().__init__(
bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , tie_word_embeddings=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class a ( __snake_case ):
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Tuple = "default" , __SCREAMING_SNAKE_CASE : Optional[Any] = None , __SCREAMING_SNAKE_CASE : Tuple = False , ) -> List[str]:
super().__init__(__SCREAMING_SNAKE_CASE , task=__SCREAMING_SNAKE_CASE , patching_specs=__SCREAMING_SNAKE_CASE , use_past=__SCREAMING_SNAKE_CASE )
if not getattr(self._config , 'pad_token_id' , __SCREAMING_SNAKE_CASE ):
# TODO: how to do that better?
lowerCamelCase_ = 0
@property
def UpperCamelCase ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
lowerCamelCase_ = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE , direction='inputs' )
lowerCamelCase_ = {0: 'batch', 1: 'past_sequence + sequence'}
else:
lowerCamelCase_ = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def UpperCamelCase ( self : Dict ) -> int:
return self._config.n_layer
@property
def UpperCamelCase ( self : Union[str, Any] ) -> int:
return self._config.n_head
def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any] = -1 , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : Optional[int] = False , __SCREAMING_SNAKE_CASE : int = None , ) -> Mapping[str, Any]:
lowerCamelCase_ = super(__SCREAMING_SNAKE_CASE , self ).generate_dummy_inputs(
__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , seq_length=__SCREAMING_SNAKE_CASE , is_pair=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
lowerCamelCase_ = 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
lowerCamelCase_ , lowerCamelCase_ = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
lowerCamelCase_ = seqlen + 2
lowerCamelCase_ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCamelCase_ = [
(torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
lowerCamelCase_ = common_inputs['attention_mask']
if self.use_past:
lowerCamelCase_ = ordered_inputs['attention_mask'].dtype
lowerCamelCase_ = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , dtype=__SCREAMING_SNAKE_CASE )] , dim=1 )
return ordered_inputs
@property
def UpperCamelCase ( self : str ) -> int:
return 13
| 183
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "ctc_proj",
"mask_emb": "masked_spec_embed",
}
lowercase_ = [
"ctc_proj",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowerCAmelCase (__A , __A , __A , __A , __A , __A):
"""simple docstring"""
for attribute in key.split('''.'''):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
_a = '''lm_head'''
_a = getattr(__A , __A)
if weight_type is not None:
_a = getattr(__A , __A).shape
else:
_a = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
_a = value
elif weight_type == "weight_g":
_a = value
elif weight_type == "weight_v":
_a = value
elif weight_type == "bias":
_a = value
else:
_a = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''')
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = []
_a = fairseq_model.state_dict()
_a = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
_a = False
if "conv_layers" in name:
load_conv_layer(
__A , __A , __A , __A , hf_model.config.feat_extract_norm == '''group''' , )
_a = True
else:
for key, mapped_key in MAPPING.items():
_a = '''unispeech.''' + 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]:
_a = True
if "*" in mapped_key:
_a = name.split(__A)[0].split('''.''')[-2]
_a = mapped_key.replace('''*''' , __A)
if "weight_g" in name:
_a = '''weight_g'''
elif "weight_v" in name:
_a = '''weight_v'''
elif "bias" in name:
_a = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_a = '''weight'''
else:
_a = None
set_recursively(__A , __A , __A , __A , __A , __A)
continue
if not is_used:
unused_weights.append(__A)
logger.warning(F'''Unused weights: {unused_weights}''')
def lowerCAmelCase (__A , __A , __A , __A , __A):
"""simple docstring"""
_a = full_name.split('''conv_layers.''')[-1]
_a = name.split('''.''')
_a = int(items[0])
_a = int(items[1])
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
_a = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
_a = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
_a = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
_a = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
else:
unused_weights.append(__A)
@torch.no_grad()
def lowerCAmelCase (__A , __A , __A=None , __A=None , __A=True):
"""simple docstring"""
if config_path is not None:
_a = UniSpeechConfig.from_pretrained(__A)
else:
_a = UniSpeechConfig()
if is_finetuned:
if dict_path:
_a = Dictionary.load_from_json(__A)
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_a = target_dict.pad_index
_a = target_dict.bos_index
_a = target_dict.eos_index
_a = len(target_dict.symbols)
_a = os.path.join(__A , '''vocab.json''')
if not os.path.isdir(__A):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__A))
return
os.makedirs(__A , exist_ok=__A)
_a = target_dict.indices
# fairseq has the <pad> and <s> switched
_a = 42
_a = 43
with open(__A , '''w''' , encoding='''utf-8''') as vocab_handle:
json.dump(__A , __A)
_a = WavaVecaPhonemeCTCTokenizer(
__A , 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=__A , )
_a = True if config.feat_extract_norm == '''layer''' else False
_a = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=__A , return_attention_mask=__A , )
_a = WavaVecaProcessor(feature_extractor=__A , tokenizer=__A)
processor.save_pretrained(__A)
_a = UniSpeechForCTC(__A)
else:
_a = UniSpeechForPreTraining(__A)
if is_finetuned:
_a , _a , _a = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
else:
_a , _a , _a = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])
_a = model[0].eval()
recursively_load_weights(__A , __A , __A)
hf_unispeech.save_pretrained(__A)
if __name__ == "__main__":
lowercase_ = 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"
)
lowercase_ = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 211
| 0
|
'''simple docstring'''
def __UpperCamelCase ( _UpperCAmelCase = 4000000 ):
__UpperCAmelCase : Dict = []
__UpperCAmelCase , __UpperCAmelCase : List[str] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCAmelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[Any] = b, a + b
return sum(_UpperCAmelCase )
if __name__ == "__main__":
print(f"{solution() = }")
| 37
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''fnet'''
def __init__( self : Tuple , UpperCAmelCase_ : str=32_000 , UpperCAmelCase_ : List[str]=768 , UpperCAmelCase_ : List[str]=12 , UpperCAmelCase_ : str=3_072 , UpperCAmelCase_ : List[str]="gelu_new" , UpperCAmelCase_ : str=0.1 , UpperCAmelCase_ : Optional[Any]=512 , UpperCAmelCase_ : Any=4 , UpperCAmelCase_ : str=0.02 , UpperCAmelCase_ : List[Any]=1e-12 , UpperCAmelCase_ : int=False , UpperCAmelCase_ : Tuple=512 , UpperCAmelCase_ : int=3 , UpperCAmelCase_ : Dict=1 , UpperCAmelCase_ : List[Any]=2 , **UpperCAmelCase_ : Tuple , ):
"""simple docstring"""
super().__init__(pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , **UpperCAmelCase_ )
__UpperCAmelCase : Optional[Any] = vocab_size
__UpperCAmelCase : List[str] = max_position_embeddings
__UpperCAmelCase : List[Any] = hidden_size
__UpperCAmelCase : Optional[int] = num_hidden_layers
__UpperCAmelCase : Optional[int] = intermediate_size
__UpperCAmelCase : List[str] = hidden_act
__UpperCAmelCase : List[str] = hidden_dropout_prob
__UpperCAmelCase : Tuple = initializer_range
__UpperCAmelCase : Union[str, Any] = type_vocab_size
__UpperCAmelCase : List[Any] = layer_norm_eps
__UpperCAmelCase : Optional[Any] = use_tpu_fourier_optimizations
__UpperCAmelCase : List[Any] = tpu_short_seq_length
| 37
| 1
|
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: int , _lowerCamelCase: int ):
return base * power(_lowerCamelCase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
UpperCamelCase__ : Optional[int] = int(input('''Enter the base: ''').strip())
UpperCamelCase__ : Tuple = int(input('''Enter the exponent: ''').strip())
UpperCamelCase__ : str = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
UpperCamelCase__ : Tuple = 1 / result
print(f"{base} to the power of {exponent} is {result}")
| 112
|
from __future__ import annotations
import math
lowercase : Any = '2020.9.26'
lowercase : Union[str, Any] = 'xcodz-dot, cclaus, dhruvmanila'
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float) -> tuple[float, float]:
'''simple docstring'''
if not all(isinstance(_lowerCamelCase , (float, int)) for val in locals().values()):
__UpperCamelCase : str = F'Input values must either be float or int: {list(locals().values())}'
raise TypeError(_lowerCamelCase)
__UpperCamelCase : List[str] = ((x * distance) / (z + distance)) * scale
__UpperCamelCase : List[Any] = ((y * distance) / (z + distance)) * scale
return projected_x, projected_y
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : str , _lowerCamelCase : float) -> tuple[float, float, float]:
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase):
raise TypeError("Axis must be a str")
__UpperCamelCase : str = locals()
del input_variables["axis"]
if not all(isinstance(_lowerCamelCase , (float, int)) for val in input_variables.values()):
__UpperCamelCase : Dict = (
"Input values except axis must either be float or int: "
F'{list(input_variables.values())}'
)
raise TypeError(_lowerCamelCase)
__UpperCamelCase : Optional[Any] = (angle % 360) / 450 * 180 / math.pi
if axis == "z":
__UpperCamelCase : Tuple = x * math.cos(_lowerCamelCase) - y * math.sin(_lowerCamelCase)
__UpperCamelCase : Union[str, Any] = y * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z
elif axis == "x":
__UpperCamelCase : Dict = y * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z * math.cos(_lowerCamelCase) + y * math.sin(_lowerCamelCase)
__UpperCamelCase : List[str] = x
elif axis == "y":
__UpperCamelCase : Any = x * math.cos(_lowerCamelCase) - z * math.sin(_lowerCamelCase)
__UpperCamelCase : Any = z * math.cos(_lowerCamelCase) + x * math.sin(_lowerCamelCase)
__UpperCamelCase : Dict = y
else:
raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'")
return new_x, new_y, new_z
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }")
print(f"{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }")
| 232
| 0
|
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCamelCase = {
"""facebook/maskformer-swin-base-ade""": (
"""https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCamelCase = logging.get_logger(__name__)
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''maskformer'''
UpperCamelCase = {'''hidden_size''': '''mask_feature_size'''}
UpperCamelCase = ['''resnet''', '''swin''']
UpperCamelCase = ['''detr''']
def __init__( self : Optional[int] , _UpperCAmelCase : int = 256 , _UpperCAmelCase : int = 256 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Optional[Dict] = None , _UpperCAmelCase : Optional[Dict] = None , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : float = 20.0 , _UpperCAmelCase : Optional[bool] = None , **_UpperCAmelCase : Dict , ) -> Union[str, Any]:
'''simple docstring'''
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
UpperCAmelCase_ = SwinConfig(
image_size=384 , in_channels=3 , patch_size=4 , embed_dim=128 , depths=[2, 2, 18, 2] , num_heads=[4, 8, 16, 32] , window_size=12 , drop_path_rate=0.3 , out_features=["stage1", "stage2", "stage3", "stage4"] , )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
UpperCAmelCase_ = backbone_config.pop("model_type" )
UpperCAmelCase_ = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase_ = config_class.from_dict(_UpperCAmelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
F"""Supported model types: {','.join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
UpperCAmelCase_ = DetrConfig()
else:
# verify that the decoder is supported
UpperCAmelCase_ = (
decoder_config.pop("model_type" ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
F"""Transformer Decoder {decoder_type} not supported, please use one of"""
F""" {','.join(self.decoders_supported )}""" )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
UpperCAmelCase_ = CONFIG_MAPPING[decoder_type]
UpperCAmelCase_ = config_class.from_dict(_UpperCAmelCase )
UpperCAmelCase_ = backbone_config
UpperCAmelCase_ = decoder_config
# main feature dimension for the model
UpperCAmelCase_ = fpn_feature_size
UpperCAmelCase_ = mask_feature_size
# initializer
UpperCAmelCase_ = init_std
UpperCAmelCase_ = init_xavier_std
# Hungarian matcher && loss
UpperCAmelCase_ = cross_entropy_weight
UpperCAmelCase_ = dice_weight
UpperCAmelCase_ = mask_weight
UpperCAmelCase_ = use_auxiliary_loss
UpperCAmelCase_ = no_object_weight
UpperCAmelCase_ = output_auxiliary_logits
UpperCAmelCase_ = self.decoder_config.encoder_attention_heads
UpperCAmelCase_ = self.decoder_config.num_hidden_layers
super().__init__(**_UpperCAmelCase )
@classmethod
def lowercase__ ( cls : Any , _UpperCAmelCase : PretrainedConfig , _UpperCAmelCase : PretrainedConfig , **_UpperCAmelCase : List[Any] ) -> Optional[int]:
'''simple docstring'''
return cls(
backbone_config=_UpperCAmelCase , decoder_config=_UpperCAmelCase , **_UpperCAmelCase , )
def lowercase__ ( self : List[str] ) -> Dict[str, any]:
'''simple docstring'''
UpperCAmelCase_ = copy.deepcopy(self.__dict__ )
UpperCAmelCase_ = self.backbone_config.to_dict()
UpperCAmelCase_ = self.decoder_config.to_dict()
UpperCAmelCase_ = self.__class__.model_type
return output
| 370
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCamelCase = '''rwkv'''
UpperCamelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : str , _UpperCAmelCase : int=50277 , _UpperCAmelCase : Optional[Any]=1024 , _UpperCAmelCase : str=4096 , _UpperCAmelCase : Any=32 , _UpperCAmelCase : Any=None , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Tuple=1e-5 , _UpperCAmelCase : str=0 , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Union[str, Any]=6 , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Any=True , **_UpperCAmelCase : Optional[Any] , ) -> str:
'''simple docstring'''
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = context_length
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = attention_hidden_size if attention_hidden_size is not None else hidden_size
UpperCAmelCase_ = intermediate_size if intermediate_size is not None else 4 * hidden_size
UpperCAmelCase_ = layer_norm_epsilon
UpperCAmelCase_ = rescale_every
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = bos_token_id
UpperCAmelCase_ = eos_token_id
super().__init__(
tie_word_embeddings=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , **_UpperCAmelCase )
| 241
| 0
|
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a_ ( ) -> tuple[list[int], int]:
"""simple docstring"""
lowerCamelCase_ =[randint(-1000 , 1000 ) for i in range(10 )]
lowerCamelCase_ =randint(-5000 , 5000 )
return (arr, r)
a_ : Dict = make_dataset()
def a_ ( __snake_case : list[int] , __snake_case : int ) -> tuple[int, ...]:
"""simple docstring"""
for triplet in permutations(__snake_case , 3 ):
if sum(__snake_case ) == target:
return tuple(sorted(__snake_case ) )
return (0, 0, 0)
def a_ ( __snake_case : list[int] , __snake_case : int ) -> tuple[int, int, int]:
"""simple docstring"""
arr.sort()
lowerCamelCase_ =len(__snake_case )
for i in range(n - 1 ):
lowerCamelCase_, lowerCamelCase_ =i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def a_ ( ) -> tuple[float, float]:
"""simple docstring"""
lowerCamelCase_ ='''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
lowerCamelCase_ ='''
triplet_sum1(*dataset)
'''
lowerCamelCase_ ='''
triplet_sum2(*dataset)
'''
lowerCamelCase_ =repeat(setup=__snake_case , stmt=__snake_case , repeat=5 , number=1_0000 )
lowerCamelCase_ =repeat(setup=__snake_case , stmt=__snake_case , repeat=5 , number=1_0000 )
return (min(__snake_case ), min(__snake_case ))
if __name__ == "__main__":
from doctest import testmod
testmod()
a_ : List[str] = solution_times()
print(F"""The time for naive implementation is {times[0]}.""")
print(F"""The time for optimized implementation is {times[1]}.""")
| 75
|
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
snake_case_ : Union[str, Any] = 50_00_00
snake_case_ ,snake_case_ : Optional[int] = os.path.split(__file__)
snake_case_ : Optional[Any] = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : datasets.Dataset, **SCREAMING_SNAKE_CASE__ : Dict ) -> str:
UpperCAmelCase_ : List[str] = dataset.map(**SCREAMING_SNAKE_CASE__ )
@get_duration
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : datasets.Dataset, **SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Any:
UpperCAmelCase_ : Optional[int] = dataset.filter(**SCREAMING_SNAKE_CASE__ )
def lowerCamelCase_ ( ) -> Any:
UpperCAmelCase_ : List[str] = {'''num examples''': SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCAmelCase_ : Optional[int] = datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
UpperCAmelCase_ : Dict = generate_example_dataset(
os.path.join(SCREAMING_SNAKE_CASE__, '''dataset.arrow''' ), SCREAMING_SNAKE_CASE__, num_examples=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Any = transformers.AutoTokenizer.from_pretrained('''bert-base-cased''', use_fast=SCREAMING_SNAKE_CASE__ )
def tokenize(SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
return tokenizer(examples['''text'''] )
UpperCAmelCase_ : List[str] = map(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Union[str, Any] = map(SCREAMING_SNAKE_CASE__, batched=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : str = map(SCREAMING_SNAKE_CASE__, function=lambda SCREAMING_SNAKE_CASE__ : None, batched=SCREAMING_SNAKE_CASE__ )
with dataset.formatted_as(type='''numpy''' ):
UpperCAmelCase_ : Dict = map(SCREAMING_SNAKE_CASE__, function=lambda SCREAMING_SNAKE_CASE__ : None, batched=SCREAMING_SNAKE_CASE__ )
with dataset.formatted_as(type='''pandas''' ):
UpperCAmelCase_ : Union[str, Any] = map(SCREAMING_SNAKE_CASE__, function=lambda SCREAMING_SNAKE_CASE__ : None, batched=SCREAMING_SNAKE_CASE__ )
with dataset.formatted_as(type='''torch''', columns='''numbers''' ):
UpperCAmelCase_ : Optional[int] = map(SCREAMING_SNAKE_CASE__, function=lambda SCREAMING_SNAKE_CASE__ : None, batched=SCREAMING_SNAKE_CASE__ )
with dataset.formatted_as(type='''tensorflow''', columns='''numbers''' ):
UpperCAmelCase_ : Optional[Any] = map(SCREAMING_SNAKE_CASE__, function=lambda SCREAMING_SNAKE_CASE__ : None, batched=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Any = map(SCREAMING_SNAKE_CASE__, function=SCREAMING_SNAKE_CASE__, batched=SCREAMING_SNAKE_CASE__ )
UpperCAmelCase_ : Tuple = filter(SCREAMING_SNAKE_CASE__ )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(SCREAMING_SNAKE_CASE__, '''wb''' ) as f:
f.write(json.dumps(SCREAMING_SNAKE_CASE__ ).encode('''utf-8''' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 125
| 0
|
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def _snake_case ( A , A=False ) -> Optional[Any]:
try:
lowerCAmelCase__ = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
lowerCAmelCase__ = default
else:
# KEY is set, convert it to True or False.
try:
lowerCAmelCase__ = strtobool(snake_case__ )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"""If set, {key} must be yes or no.""" )
return _value
__UpperCAmelCase = parse_flag_from_env('''RUN_SLOW''', default=False)
def _snake_case ( A ) -> str:
return unittest.skip('''Test was skipped''' )(snake_case__ )
def _snake_case ( A ) -> Optional[Any]:
return unittest.skipUnless(_run_slow_tests , '''test is slow''' )(snake_case__ )
def _snake_case ( A ) -> Dict:
return unittest.skipUnless(not torch.cuda.is_available() , '''test requires only a CPU''' )(snake_case__ )
def _snake_case ( A ) -> Dict:
return unittest.skipUnless(torch.cuda.is_available() , '''test requires a GPU''' )(snake_case__ )
def _snake_case ( A ) -> List[Any]:
return unittest.skipUnless(is_xpu_available() , '''test requires a XPU''' )(snake_case__ )
def _snake_case ( A ) -> List[Any]:
return unittest.skipUnless(is_mps_available() , '''test requires a `mps` backend support in `torch`''' )(snake_case__ )
def _snake_case ( A ) -> Tuple:
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , '''test requires the Hugging Face suite''' )(snake_case__ )
def _snake_case ( A ) -> List[Any]:
return unittest.skipUnless(is_bnb_available() , '''test requires the bitsandbytes library''' )(snake_case__ )
def _snake_case ( A ) -> List[str]:
return unittest.skipUnless(is_tpu_available() , '''test requires TPU''' )(snake_case__ )
def _snake_case ( A ) -> Optional[int]:
return unittest.skipUnless(torch.cuda.device_count() == 1 , '''test requires a GPU''' )(snake_case__ )
def _snake_case ( A ) -> Dict:
return unittest.skipUnless(torch.xpu.device_count() == 1 , '''test requires a XPU''' )(snake_case__ )
def _snake_case ( A ) -> Any:
return unittest.skipUnless(torch.cuda.device_count() > 1 , '''test requires multiple GPUs''' )(snake_case__ )
def _snake_case ( A ) -> int:
return unittest.skipUnless(torch.xpu.device_count() > 1 , '''test requires multiple XPUs''' )(snake_case__ )
def _snake_case ( A ) -> Any:
return unittest.skipUnless(is_safetensors_available() , '''test requires safetensors''' )(snake_case__ )
def _snake_case ( A ) -> Dict:
return unittest.skipUnless(is_deepspeed_available() , '''test requires DeepSpeed''' )(snake_case__ )
def _snake_case ( A ) -> Optional[Any]:
return unittest.skipUnless(is_torch_version('''>=''' , '''1.12.0''' ) , '''test requires torch version >= 1.12.0''' )(snake_case__ )
def _snake_case ( A=None , A=None ) -> Optional[Any]:
if test_case is None:
return partial(snake_case__ , version=snake_case__ )
return unittest.skipUnless(is_torch_version('''>=''' , snake_case__ ) , F"""test requires torch version >= {version}""" )(snake_case__ )
def _snake_case ( A ) -> Optional[Any]:
return unittest.skipUnless(is_tensorboard_available() , '''test requires Tensorboard''' )(snake_case__ )
def _snake_case ( A ) -> Tuple:
return unittest.skipUnless(is_wandb_available() , '''test requires wandb''' )(snake_case__ )
def _snake_case ( A ) -> List[Any]:
return unittest.skipUnless(is_comet_ml_available() , '''test requires comet_ml''' )(snake_case__ )
__UpperCAmelCase = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def _snake_case ( A ) -> Optional[Any]:
return unittest.skipUnless(
_atleast_one_tracker_available , '''test requires at least one tracker to be available and for `comet_ml` to not be installed''' , )(snake_case__ )
class a__ ( unittest.TestCase ):
'''simple docstring'''
lowercase__ : Optional[Any] = True
@classmethod
def __SCREAMING_SNAKE_CASE ( cls ) -> Any:
lowerCAmelCase__ = tempfile.mkdtemp()
@classmethod
def __SCREAMING_SNAKE_CASE ( cls ) -> Tuple:
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def __SCREAMING_SNAKE_CASE ( self ) -> str:
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob('''**/*''' ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
class a__ ( unittest.TestCase ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class a__ ( unittest.TestCase ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ ) -> Tuple:
lowerCAmelCase__ = mocks if isinstance(SCREAMING_SNAKE_CASE_ , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def _snake_case ( A ) -> Optional[int]:
lowerCAmelCase__ = AcceleratorState()
lowerCAmelCase__ = tensor[None].clone().to(state.device )
lowerCAmelCase__ = gather(snake_case__ ).cpu()
lowerCAmelCase__ = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , snake_case__ ):
return False
return True
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
lowerCAmelCase__ = returncode
lowerCAmelCase__ = stdout
lowerCAmelCase__ = stderr
async def _snake_case ( A , A ) -> Union[str, Any]:
while True:
lowerCAmelCase__ = await stream.readline()
if line:
callback(snake_case__ )
else:
break
async def _snake_case ( A , A=None , A=None , A=None , A=False , A=False ) -> _RunOutput:
if echo:
print('''\nRunning: ''' , ''' '''.join(snake_case__ ) )
lowerCAmelCase__ = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=snake_case__ , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=snake_case__ , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
lowerCAmelCase__ = []
lowerCAmelCase__ = []
def tee(A , A , A , A="" ):
lowerCAmelCase__ = line.decode('''utf-8''' ).rstrip()
sink.append(snake_case__ )
if not quiet:
print(snake_case__ , snake_case__ , file=snake_case__ )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda A : tee(snake_case__ , snake_case__ , sys.stdout , label='''stdout:''' ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda A : tee(snake_case__ , snake_case__ , sys.stderr , label='''stderr:''' ) ) ),
] , timeout=snake_case__ , )
return _RunOutput(await p.wait() , snake_case__ , snake_case__ )
def _snake_case ( A , A=None , A=None , A=180 , A=False , A=True ) -> _RunOutput:
lowerCAmelCase__ = asyncio.get_event_loop()
lowerCAmelCase__ = loop.run_until_complete(
_stream_subprocess(snake_case__ , env=snake_case__ , stdin=snake_case__ , timeout=snake_case__ , quiet=snake_case__ , echo=snake_case__ ) )
lowerCAmelCase__ = ' '.join(snake_case__ )
if result.returncode > 0:
lowerCAmelCase__ = '\n'.join(result.stderr )
raise RuntimeError(
F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
F"""The combined stderr from workers follows:\n{stderr}""" )
return result
class a__ ( a__ ):
'''simple docstring'''
pass
def _snake_case ( A , A=False ) -> int:
try:
lowerCAmelCase__ = subprocess.check_output(snake_case__ , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(snake_case__ , '''decode''' ):
lowerCAmelCase__ = output.decode('''utf-8''' )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"""Command `{" ".join(snake_case__ )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 365
|
'''simple docstring'''
import argparse
import json
import subprocess
def _snake_case ( A , A ) -> Tuple:
lowerCAmelCase__ = []
lowerCAmelCase__ = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
''' https://api.github.com/repos/huggingface/transformers/actions/runners'''
)
lowerCAmelCase__ = subprocess.run(A , shell=A , stdout=subprocess.PIPE )
lowerCAmelCase__ = output.stdout.decode('''utf-8''' )
lowerCAmelCase__ = json.loads(A )
lowerCAmelCase__ = status['''runners''']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(A )
# save the result so we can report them on Slack
with open('''offline_runners.txt''' , '''w''' ) as fp:
fp.write(json.dumps(A ) )
if len(A ) > 0:
lowerCAmelCase__ = '''\n'''.join([x['''name'''] for x in offline_runners] )
raise ValueError(F"""The following runners are offline:\n{failed}""" )
if __name__ == "__main__":
def _snake_case ( A ) -> Optional[Any]:
return values.split(''',''' )
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--target_runners''',
default=None,
type=list_str,
required=True,
help='''Comma-separated list of runners to check status.''',
)
parser.add_argument(
'''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
)
__UpperCAmelCase = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 228
| 0
|
import os
import jsonlines
import numpy as np
from tqdm import tqdm
A__ = 2048
A__ = 4096
A__ = 42
A__ = os.environ.pop('''PROCESS_TRAIN''', '''false''')
A__ = {'''null''': 0, '''short''': 1, '''long''': 2, '''yes''': 3, '''no''': 4}
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
def choose_first(__lowerCAmelCase , __lowerCAmelCase=False ):
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
if len(__lowerCAmelCase ) == 1:
snake_case__ : Tuple = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
snake_case__ : Optional[Any] = {k: [a[k]] for k in a}
if len(a['''start_token'''] ) > 0:
break
return a
snake_case__ : List[str] = {'''id''': example['''id''']}
snake_case__ : str = example['''annotations''']
snake_case__ : List[str] = annotation['''yes_no_answer''']
if 0 in yes_no_answer or 1 in yes_no_answer:
snake_case__ : Union[str, Any] = ['''yes'''] if 1 in yes_no_answer else ['''no''']
snake_case__ : Tuple = []
snake_case__ : Optional[Any] = []
snake_case__ : Dict = ['''<cls>''']
else:
snake_case__ : List[Any] = ['''short''']
snake_case__ : int = choose_first(annotation['''short_answers'''] )
if len(out['''start_token'''] ) == 0:
# answer will be long if short is not available
snake_case__ : Any = ['''long''']
snake_case__ : Optional[int] = choose_first(annotation['''long_answer'''] , is_long_answer=__lowerCAmelCase )
snake_case__ : List[Any] = []
answer.update(__lowerCAmelCase )
# disregard some samples
if len(answer['''start_token'''] ) > 1 or answer["start_token"] == answer["end_token"]:
snake_case__ : List[Any] = True
else:
snake_case__ : List[Any] = False
snake_case__ : List[str] = ['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
if not all(isinstance(answer[k] , __lowerCAmelCase ) for k in cols ):
raise ValueError('''Issue in ID''' , example['''id'''] )
return answer
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase=False ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Optional[Any] = _get_single_answer(__lowerCAmelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
snake_case__ : List[Any] = example['''document''']['''tokens''']
snake_case__ : Union[str, Any] = []
for i in range(len(doc['''token'''] ) ):
if not doc["is_html"][i]:
context.append(doc['''token'''][i] )
return {
"context": " ".join(__lowerCAmelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
snake_case__ : int = ['''start_token''', '''end_token''']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
snake_case__ : Optional[int] = example['''document''']['''tokens''']
snake_case__ : str = answer['''start_token''']
snake_case__ : int = answer['''end_token''']
snake_case__ : List[str] = []
for i in range(len(doc['''token'''] ) ):
if not doc["is_html"][i]:
context.append(doc['''token'''][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
snake_case__ : Optional[Any] = ''' '''.join(context[start_token:end_token] )
# checking above code
if assertion:
snake_case__ : str = doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
snake_case__ : str = doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
snake_case__ : Optional[Any] = ''' '''.join([old[i] for i in range(len(__lowerCAmelCase ) ) if not is_html[i]] )
if new != old:
print('''ID:''' , example['''id'''] )
print('''New:''' , __lowerCAmelCase , end='''\n''' )
print('''Old:''' , __lowerCAmelCase , end='''\n\n''' )
return {
"context": " ".join(__lowerCAmelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=2048 , __lowerCAmelCase=4096 , __lowerCAmelCase=True ) -> int:
"""simple docstring"""
snake_case__ : int = get_context_and_ans(__lowerCAmelCase , assertion=__lowerCAmelCase )
snake_case__ : List[Any] = out['''answer''']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
snake_case__ : Optional[Any] = tokenizer(example['''question''']['''text'''] , out['''context'''] ).input_ids
snake_case__ : Union[str, Any] = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
snake_case__ : Optional[Any] = []
snake_case__ : Optional[Any] = []
snake_case__ : int = input_ids[:q_len]
snake_case__ : Union[str, Any] = range(__lowerCAmelCase , len(__lowerCAmelCase ) , max_length - doc_stride )
for i in doc_start_indices:
snake_case__ : Tuple = i + max_length - q_len
snake_case__ : Any = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['''category'''][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(__lowerCAmelCase ),
"end_token": [-100] * len(__lowerCAmelCase ),
"category": category,
},
}
snake_case__ : Optional[int] = out['''context'''].split()
snake_case__ : Union[str, Any] = splitted_context[answer['''end_token''']]
snake_case__ : str = len(
tokenizer(
''' '''.join(splitted_context[: answer['''start_token''']] ) , add_special_tokens=__lowerCAmelCase , ).input_ids )
snake_case__ : Tuple = len(
tokenizer(''' '''.join(splitted_context[: answer['''end_token''']] ) , add_special_tokens=__lowerCAmelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
snake_case__ : Dict = len(tokenizer(__lowerCAmelCase , add_special_tokens=__lowerCAmelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
snake_case__ : Optional[Any] = input_ids[answer['''start_token'''] : answer['''end_token'''] + 1] # right & left are inclusive
snake_case__ : Optional[Any] = answer['''start_token''']
snake_case__ : str = answer['''end_token''']
if assertion:
snake_case__ : int = tokenizer.decode(__lowerCAmelCase )
if answer["span"] != new:
print('''ISSUE IN TOKENIZATION''' )
print('''OLD:''' , answer['''span'''] )
print('''NEW:''' , __lowerCAmelCase , end='''\n\n''' )
if len(__lowerCAmelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
snake_case__ : List[Any] = input_ids[:q_len]
snake_case__ : str = range(__lowerCAmelCase , len(__lowerCAmelCase ) , max_length - doc_stride )
snake_case__ : List[str] = []
snake_case__ : str = []
snake_case__ : int = []
snake_case__ : str = [] # null, yes, no, long, short
for i in doc_start_indices:
snake_case__ : List[Any] = i + max_length - q_len
snake_case__ : Tuple = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
snake_case__ : Dict = start_token - i + q_len
snake_case__ : Union[str, Any] = end_token - i + q_len
answers_category.append(answer['''category'''][0] ) # ["short"] -> "short"
else:
snake_case__ : Optional[Any] = -100
snake_case__ : Optional[int] = -100
answers_category.append('''null''' )
snake_case__ : str = inputs[-1][start_token : end_token + 1]
answers_start_token.append(__lowerCAmelCase )
answers_end_token.append(__lowerCAmelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('''ISSUE in strided for ID:''' , example['''id'''] )
print('''New:''' , tokenizer.decode(__lowerCAmelCase ) )
print('''Old:''' , tokenizer.decode(__lowerCAmelCase ) , end='''\n\n''' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=2048 , __lowerCAmelCase=4096 , __lowerCAmelCase=False ) -> int:
"""simple docstring"""
snake_case__ : List[Any] = get_strided_contexts_and_ans(
__lowerCAmelCase , __lowerCAmelCase , doc_stride=__lowerCAmelCase , max_length=__lowerCAmelCase , assertion=__lowerCAmelCase , )
return example
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
with jsonlines.open(__lowerCAmelCase , '''a''' ) as writer:
for example in tqdm(__lowerCAmelCase , total=len(__lowerCAmelCase ) , desc='''Saving samples ... ''' ):
snake_case__ : Any = example['''labels''']
for ids, start, end, cat in zip(
example['''input_ids'''] , labels['''start_token'''] , labels['''end_token'''] , labels['''category'''] , ):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'''input_ids''': ids,
'''start_token''': start,
'''end_token''': end,
'''category''': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
A__ = load_dataset('''natural_questions''')
A__ = BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
A__ = data['''train''' if PROCESS_TRAIN == '''true''' else '''validation''']
A__ = {
'''tokenizer''': tokenizer,
'''doc_stride''': DOC_STRIDE,
'''max_length''': MAX_LENGTH,
'''assertion''': False,
}
A__ = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
A__ = data.remove_columns(['''annotations''', '''document''', '''id''', '''question'''])
print(data)
np.random.seed(SEED)
A__ = '''nq-training.jsonl''' if PROCESS_TRAIN == '''true''' else '''nq-validation.jsonl'''
save_to_disk(data, file_name=cache_file_name)
| 230
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
A__ = logging.get_logger(__name__)
A__ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
A__ = {
'''vocab_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
),
'''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
'''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
},
'''tokenizer_file''': {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
),
'''google/realm-cc-news-pretrained-encoder''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-scorer''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
),
'''google/realm-cc-news-pretrained-openqa''': (
'''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-openqa''': (
'''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-nq-reader''': (
'''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-openqa''': (
'''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
),
'''google/realm-orqa-wq-reader''': (
'''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
),
},
}
A__ = {
'''google/realm-cc-news-pretrained-embedder''': 512,
'''google/realm-cc-news-pretrained-encoder''': 512,
'''google/realm-cc-news-pretrained-scorer''': 512,
'''google/realm-cc-news-pretrained-openqa''': 512,
'''google/realm-orqa-nq-openqa''': 512,
'''google/realm-orqa-nq-reader''': 512,
'''google/realm-orqa-wq-openqa''': 512,
'''google/realm-orqa-wq-reader''': 512,
}
A__ = {
'''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
'''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
'''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}
class a ( __lowerCamelCase ):
__lowerCAmelCase : List[Any] = VOCAB_FILES_NAMES
__lowerCAmelCase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : Optional[Any] = PRETRAINED_INIT_CONFIGURATION
__lowerCAmelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Optional[Any] = RealmTokenizer
def __init__( self :Optional[Any] ,__lowercase :Dict=None ,__lowercase :Optional[int]=None ,__lowercase :Optional[Any]=True ,__lowercase :Optional[int]="[UNK]" ,__lowercase :List[str]="[SEP]" ,__lowercase :List[str]="[PAD]" ,__lowercase :int="[CLS]" ,__lowercase :str="[MASK]" ,__lowercase :Dict=True ,__lowercase :List[str]=None ,**__lowercase :Any ,):
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 ,)
snake_case__ : List[str] = 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
):
snake_case__ : Optional[int] = getattr(__lowercase ,normalizer_state.pop('''type''' ) )
snake_case__ : List[Any] = do_lower_case
snake_case__ : Optional[Any] = strip_accents
snake_case__ : List[str] = tokenize_chinese_chars
snake_case__ : Dict = normalizer_class(**__lowercase )
snake_case__ : Tuple = do_lower_case
def __lowerCamelCase ( self :Union[str, Any] ,__lowercase :Union[str, Any] ,**__lowercase :Any ):
snake_case__ : Dict = PaddingStrategy.MAX_LENGTH
snake_case__ : List[str] = text
snake_case__ : int = kwargs.pop('''text_pair''' ,__lowercase )
snake_case__ : Optional[int] = kwargs.pop('''return_tensors''' ,__lowercase )
snake_case__ : str = {
'''input_ids''': [],
'''attention_mask''': [],
'''token_type_ids''': [],
}
for idx, candidate_text in enumerate(__lowercase ):
if batch_text_pair is not None:
snake_case__ : Optional[int] = batch_text_pair[idx]
else:
snake_case__ : Tuple = None
snake_case__ : List[str] = super().__call__(__lowercase ,__lowercase ,return_tensors=__lowercase ,**__lowercase )
snake_case__ : Optional[Any] = encoded_candidates.get('''input_ids''' )
snake_case__ : Optional[Any] = encoded_candidates.get('''attention_mask''' )
snake_case__ : List[Any] = encoded_candidates.get('''token_type_ids''' )
if encoded_input_ids is not None:
output_data["input_ids"].append(__lowercase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(__lowercase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(__lowercase )
snake_case__ : Any = {key: item for key, item in output_data.items() if len(__lowercase ) != 0}
return BatchEncoding(__lowercase ,tensor_type=__lowercase )
def __lowerCamelCase ( self :List[Any] ,__lowercase :Tuple ,__lowercase :Tuple=None ):
snake_case__ : 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 __lowerCamelCase ( self :List[str] ,__lowercase :List[int] ,__lowercase :Optional[List[int]] = None ):
snake_case__ : Tuple = [self.sep_token_id]
snake_case__ : Union[str, 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 __lowerCamelCase ( self :Optional[Any] ,__lowercase :str ,__lowercase :Optional[str] = None ):
snake_case__ : Tuple = self._tokenizer.model.save(__lowercase ,name=__lowercase )
return tuple(__lowercase )
| 230
| 1
|
'''simple docstring'''
from collections.abc import Sequence
def lowercase (_A = None ):
"""simple docstring"""
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
_lowerCAmelCase : Optional[int] = nums[0]
for i in range(1 , len(_UpperCAmelCase ) ):
_lowerCAmelCase : Dict = nums[i]
_lowerCAmelCase : Optional[Any] = max(_UpperCAmelCase , ans + num , _UpperCAmelCase )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
lowerCAmelCase = int(input("""Enter number of elements : """).strip())
lowerCAmelCase = list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
print(max_subsequence_sum(array))
| 350
|
'''simple docstring'''
def lowercase (_A ):
"""simple docstring"""
_lowerCAmelCase : Union[str, Any] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_lowerCAmelCase : List[str] = ''
_lowerCAmelCase : Any = ''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(_A ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = 0, 0
# length[i] shows the length of palindromic substring with center i
_lowerCAmelCase : List[str] = [1 for i in range(len(_A ) )]
# for each character in new_string find corresponding palindromic string
_lowerCAmelCase : Any = 0
for j in range(len(_A ) ):
_lowerCAmelCase : Optional[Any] = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(_A )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_lowerCAmelCase : List[str] = 2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
_lowerCAmelCase : Optional[Any] = j - k + 1 # noqa: E741
_lowerCAmelCase : int = j + k - 1
# update max_length and start position
if max_length < length[j]:
_lowerCAmelCase : Dict = length[j]
_lowerCAmelCase : Optional[int] = j
# create that string
_lowerCAmelCase : List[str] = new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 25
| 0
|
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__A = """\
"""
__A = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
__A = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'input_texts': datasets.Value('string' ),
} ) , reference_urls=['https://huggingface.co/docs/transformers/perplexity'] , )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = 1_6 , __UpperCAmelCase = True , __UpperCAmelCase=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
lowerCAmelCase__ :Tuple = 'cuda'
else:
lowerCAmelCase__ :List[Any] = 'cuda' if torch.cuda.is_available() else 'cpu'
lowerCAmelCase__ :List[str] = AutoModelForCausalLM.from_pretrained(__UpperCAmelCase )
lowerCAmelCase__ :List[Any] = model.to(__UpperCAmelCase )
lowerCAmelCase__ :Optional[int] = AutoTokenizer.from_pretrained(__UpperCAmelCase )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
lowerCAmelCase__ :Optional[Any] = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__UpperCAmelCase ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
lowerCAmelCase__ :List[str] = model.config.max_length - 1
else:
lowerCAmelCase__ :Dict = model.config.max_length
lowerCAmelCase__ :List[Any] = tokenizer(
__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , return_tensors='pt' , return_attention_mask=__UpperCAmelCase , ).to(__UpperCAmelCase )
lowerCAmelCase__ :Any = encodings['input_ids']
lowerCAmelCase__ :Optional[int] = encodings['attention_mask']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
lowerCAmelCase__ :Tuple = []
lowerCAmelCase__ :List[Any] = CrossEntropyLoss(reduction='none' )
for start_index in logging.tqdm(range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ) ):
lowerCAmelCase__ :Optional[int] = min(start_index + batch_size , len(__UpperCAmelCase ) )
lowerCAmelCase__ :Union[str, Any] = encoded_texts[start_index:end_index]
lowerCAmelCase__ :Tuple = attn_masks[start_index:end_index]
if add_start_token:
lowerCAmelCase__ :int = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__UpperCAmelCase )
lowerCAmelCase__ :Union[str, Any] = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
lowerCAmelCase__ :Dict = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__UpperCAmelCase ), attn_mask] , dim=1 )
lowerCAmelCase__ :Optional[int] = encoded_batch
with torch.no_grad():
lowerCAmelCase__ :Union[str, Any] = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase ).logits
lowerCAmelCase__ :str = out_logits[..., :-1, :].contiguous()
lowerCAmelCase__ :Dict = labels[..., 1:].contiguous()
lowerCAmelCase__ :Tuple = attn_mask[..., 1:].contiguous()
lowerCAmelCase__ :Dict = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __UpperCAmelCase ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__UpperCAmelCase )}
| 293
|
"""simple docstring"""
from __future__ import annotations
__A = 1.6_021e-19 # units = C
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) ->tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif conductivity < 0:
raise ValueError('Conductivity cannot be negative' )
elif electron_conc < 0:
raise ValueError('Electron concentration cannot be negative' )
elif mobility < 0:
raise ValueError('mobility cannot be negative' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 293
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
),
}
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : int = 'lilt'
def __init__(self : List[Any] , __UpperCAmelCase : Dict=3_0_5_2_2 , __UpperCAmelCase : Dict=7_6_8 , __UpperCAmelCase : Union[str, Any]=1_2 , __UpperCAmelCase : List[Any]=1_2 , __UpperCAmelCase : str=3_0_7_2 , __UpperCAmelCase : str="gelu" , __UpperCAmelCase : List[Any]=0.1 , __UpperCAmelCase : Tuple=0.1 , __UpperCAmelCase : Optional[int]=5_1_2 , __UpperCAmelCase : str=2 , __UpperCAmelCase : Union[str, Any]=0.02 , __UpperCAmelCase : int=1E-12 , __UpperCAmelCase : Union[str, Any]=0 , __UpperCAmelCase : Tuple="absolute" , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : int=4 , __UpperCAmelCase : Optional[int]=1_0_2_4 , **__UpperCAmelCase : List[Any] , ) -> str:
"""simple docstring"""
super().__init__(pad_token_id=__UpperCAmelCase , **__UpperCAmelCase )
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__ = classifier_dropout
UpperCAmelCase__ = channel_shrink_ratio
UpperCAmelCase__ = max_ad_position_embeddings
| 143
|
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
"compression_format, is_archive", [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
], )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, ) -> Any:
'''simple docstring'''
UpperCAmelCase__ = {
"7z": (seven_zip_file, SevenZipExtractor),
"bz2": (bza_file, BzipaExtractor),
"gzip": (gz_file, GzipExtractor),
"lz4": (lza_file, LzaExtractor),
"tar": (tar_file, TarExtractor),
"xz": (xz_file, XzExtractor),
"zip": (zip_file, ZipExtractor),
"zstd": (zstd_file, ZstdExtractor),
}
UpperCAmelCase__ , UpperCAmelCase__ = input_paths_and_base_extractors[compression_format]
if input_path is None:
UpperCAmelCase__ = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
assert base_extractor.is_extractable(__A )
UpperCAmelCase__ = tmp_path / ("extracted" if is_archive else "extracted.txt")
base_extractor.extract(__A, __A )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
UpperCAmelCase__ = file_path.read_text(encoding="utf-8" )
else:
UpperCAmelCase__ = output_path.read_text(encoding="utf-8" )
UpperCAmelCase__ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
"compression_format, is_archive", [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
], )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, __A, ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase__ = {
"7z": seven_zip_file,
"bz2": bza_file,
"gzip": gz_file,
"lz4": lza_file,
"tar": tar_file,
"xz": xz_file,
"zip": zip_file,
"zstd": zstd_file,
}
UpperCAmelCase__ = input_paths[compression_format]
if input_path is None:
UpperCAmelCase__ = f"""for '{compression_format}' compression_format, """
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(__A )
UpperCAmelCase__ = Extractor.infer_extractor_format(__A )
assert extractor_format is not None
UpperCAmelCase__ = tmp_path / ("extracted" if is_archive else "extracted.txt")
Extractor.extract(__A, __A, __A )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
UpperCAmelCase__ = file_path.read_text(encoding="utf-8" )
else:
UpperCAmelCase__ = output_path.read_text(encoding="utf-8" )
UpperCAmelCase__ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def lowerCAmelCase_ ( __A, __A ) -> List[str]:
'''simple docstring'''
import tarfile
UpperCAmelCase__ = tmp_path / "data_dot_dot"
directory.mkdir()
UpperCAmelCase__ = directory / "tar_file_with_dot_dot.tar"
with tarfile.TarFile(__A, "w" ) as f:
f.add(__A, arcname=os.path.join("..", text_file.name ) )
return path
@pytest.fixture
def lowerCAmelCase_ ( __A ) -> Dict:
'''simple docstring'''
import tarfile
UpperCAmelCase__ = tmp_path / "data_sym_link"
directory.mkdir()
UpperCAmelCase__ = directory / "tar_file_with_sym_link.tar"
os.symlink("..", directory / "subdir", target_is_directory=__A )
with tarfile.TarFile(__A, "w" ) as f:
f.add(str(directory / "subdir" ), arcname="subdir" ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
"insecure_tar_file, error_log", [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")], )
def lowerCAmelCase_ ( __A, __A, __A, __A, __A, __A ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ = {
"tar_file_with_dot_dot": tar_file_with_dot_dot,
"tar_file_with_sym_link": tar_file_with_sym_link,
}
UpperCAmelCase__ = insecure_tar_files[insecure_tar_file]
UpperCAmelCase__ = tmp_path / "extracted"
TarExtractor.extract(__A, __A )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def lowerCAmelCase_ ( __A ) -> Any:
'''simple docstring'''
UpperCAmelCase__ = tmpdir / "not_a_zip_file"
# From: https://github.com/python/cpython/pull/5053
UpperCAmelCase__ = (
B"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
B"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
B"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
B"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
)
with not_a_zip_file.open("wb" ) as f:
f.write(__A )
assert zipfile.is_zipfile(str(__A ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(__A ) # but we're right
| 143
| 1
|
from __future__ import annotations
from collections import namedtuple
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) != 1:
raise ValueError('''Only one argument must be 0''' )
elif power < 0:
raise ValueError(
'''Power cannot be negative in any electrical/electronics system''' )
elif voltage == 0:
return result('''voltage''' , power / current )
elif current == 0:
return result('''current''' , power / voltage )
elif power == 0:
return result('''power''' , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 49
|
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__a = np.full((len(_UpperCAmelCase ), sequence_length, 2) , _UpperCAmelCase )
else:
__a = np.full((len(_UpperCAmelCase ), sequence_length) , _UpperCAmelCase )
for i, tensor in enumerate(_UpperCAmelCase ):
if padding_side == "right":
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__a = tensor[:sequence_length]
else:
__a = tensor[:sequence_length]
else:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
__a = tensor[:sequence_length]
else:
__a = tensor[:sequence_length]
return out_tensor.tolist()
def __snake_case ( _UpperCAmelCase ):
__a = ord(_UpperCAmelCase )
if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
return True
__a = unicodedata.category(_UpperCAmelCase )
if cat.startswith('''P''' ):
return True
return False
@dataclass
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : PreTrainedTokenizerBase
UpperCamelCase__ : Union[bool, str, PaddingStrategy] = True
UpperCamelCase__ : Optional[int] = None
UpperCamelCase__ : Optional[int] = None
UpperCamelCase__ : int = -100
UpperCamelCase__ : str = "pt"
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any]):
'''simple docstring'''
import torch
__a = '''label''' if '''label''' in features[0].keys() else '''labels'''
__a = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
__a = self.tokenizer.pad(
__SCREAMING_SNAKE_CASE , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' if labels is None else None , )
if labels is None:
return batch
__a = torch.tensor(batch['''entity_ids''']).shape[1]
__a = self.tokenizer.padding_side
if padding_side == "right":
__a = [
list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
]
else:
__a = [
[self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
]
__a = [feature['''ner_tags'''] for feature in features]
__a = padding_tensor(__SCREAMING_SNAKE_CASE , -1 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = [feature['''original_entity_spans'''] for feature in features]
__a = padding_tensor(__SCREAMING_SNAKE_CASE , (-1, -1) , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = {k: torch.tensor(__SCREAMING_SNAKE_CASE , dtype=torch.intaa) for k, v in batch.items()}
return batch
| 49
| 1
|
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = "src/transformers"
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
UpperCAmelCase_ = re.compile(r'\s+\"\S*\":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
UpperCAmelCase_ = re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
UpperCAmelCase_ = re.compile(r'^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
UpperCAmelCase_ = re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
UpperCAmelCase_ = re.compile(r'^\s+\"([^\"]+)\",')
# Catches a line with objects between brackets only: ["foo", "bar"],
UpperCAmelCase_ = re.compile(r'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
UpperCAmelCase_ = re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
UpperCAmelCase_ = re.compile(r'^\s*try:')
# Catches a line with else:
UpperCAmelCase_ = re.compile(r'^\s*else:')
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
if _re_test_backend.search(__lowerCAmelCase ) is None:
return None
UpperCAmelCase__ = [b[0] for b in _re_backend.findall(__lowerCAmelCase )]
backends.sort()
return "_and_".join(__lowerCAmelCase )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
with open(__lowerCAmelCase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
UpperCAmelCase__ = f.readlines()
UpperCAmelCase__ = 0
while line_index < len(__lowerCAmelCase ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__lowerCAmelCase ):
return None
# First grab the objects without a specific backend in _import_structure
UpperCAmelCase__ = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
UpperCAmelCase__ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__lowerCAmelCase ):
UpperCAmelCase__ = _re_one_line_import_struct.search(__lowerCAmelCase ).groups()[0]
UpperCAmelCase__ = re.findall(r"""\[([^\]]+)\]""" , __lowerCAmelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
UpperCAmelCase__ = _re_import_struct_key_value.search(__lowerCAmelCase )
if single_line_import_search is not None:
UpperCAmelCase__ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(__lowerCAmelCase ) > 0]
objects.extend(__lowerCAmelCase )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
UpperCAmelCase__ = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
UpperCAmelCase__ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCAmelCase__ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCAmelCase__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
UpperCAmelCase__ = lines[line_index]
if _re_import_struct_add_one.search(__lowerCAmelCase ) is not None:
objects.append(_re_import_struct_add_one.search(__lowerCAmelCase ).groups()[0] )
elif _re_import_struct_add_many.search(__lowerCAmelCase ) is not None:
UpperCAmelCase__ = _re_import_struct_add_many.search(__lowerCAmelCase ).groups()[0].split(""", """ )
UpperCAmelCase__ = [obj[1:-1] for obj in imports if len(__lowerCAmelCase ) > 0]
objects.extend(__lowerCAmelCase )
elif _re_between_brackets.search(__lowerCAmelCase ) is not None:
UpperCAmelCase__ = _re_between_brackets.search(__lowerCAmelCase ).groups()[0].split(""", """ )
UpperCAmelCase__ = [obj[1:-1] for obj in imports if len(__lowerCAmelCase ) > 0]
objects.extend(__lowerCAmelCase )
elif _re_quote_object.search(__lowerCAmelCase ) is not None:
objects.append(_re_quote_object.search(__lowerCAmelCase ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
UpperCAmelCase__ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
UpperCAmelCase__ = []
while (
line_index < len(__lowerCAmelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
UpperCAmelCase__ = lines[line_index]
UpperCAmelCase__ = _re_import.search(__lowerCAmelCase )
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
UpperCAmelCase__ = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(__lowerCAmelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
UpperCAmelCase__ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCAmelCase__ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCAmelCase__ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
UpperCAmelCase__ = lines[line_index]
UpperCAmelCase__ = _re_import.search(__lowerCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
UpperCAmelCase__ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Any ):
'''simple docstring'''
def find_duplicates(SCREAMING_SNAKE_CASE__ : int ):
return [k for k, v in collections.Counter(__lowerCAmelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
UpperCAmelCase__ = []
for key in import_dict_objects.keys():
UpperCAmelCase__ = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
UpperCAmelCase__ = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
UpperCAmelCase__ = """base imports""" if key == """none""" else F'''{key} backend'''
errors.append(F'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = []
for root, _, files in os.walk(__lowerCAmelCase ):
if "__init__.py" in files:
UpperCAmelCase__ = os.path.join(__lowerCAmelCase , """__init__.py""" )
UpperCAmelCase__ = parse_init(__lowerCAmelCase )
if objects is not None:
UpperCAmelCase__ = analyze_results(*__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
UpperCAmelCase__ = F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append("""\n""".join(__lowerCAmelCase ) )
if len(__lowerCAmelCase ) > 0:
raise ValueError("""\n\n""".join(__lowerCAmelCase ) )
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = []
for path, directories, files in os.walk(__lowerCAmelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(__lowerCAmelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__lowerCAmelCase ) / folder).glob("""*.py""" ) ) ) == 0:
continue
UpperCAmelCase__ = str((Path(__lowerCAmelCase ) / folder).relative_to(__lowerCAmelCase ) )
UpperCAmelCase__ = short_path.replace(os.path.sep , """.""" )
submodules.append(__lowerCAmelCase )
for fname in files:
if fname == "__init__.py":
continue
UpperCAmelCase__ = str((Path(__lowerCAmelCase ) / fname).relative_to(__lowerCAmelCase ) )
UpperCAmelCase__ = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(__lowerCAmelCase )
return submodules
UpperCAmelCase_ = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
"models.esm.openfold_utils",
]
def _UpperCamelCase ( ):
'''simple docstring'''
from transformers.utils import direct_transformers_import
UpperCAmelCase__ = direct_transformers_import(__lowerCAmelCase )
UpperCAmelCase__ = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__lowerCAmelCase , """__init__.py""" ) , """r""" ) as f:
UpperCAmelCase__ = f.read()
import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]""" , __lowerCAmelCase ) ) )
UpperCAmelCase__ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__lowerCAmelCase ) > 0:
UpperCAmelCase__ = """\n""".join(F'''- {module}''' for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registed in the main init of Transformers:\n"""
F'''{list_of_modules}\n'''
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 362
|
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCAmelCase_ = logging.get_logger(__name__)
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
UpperCAmelCase__ = json.loads(SCREAMING_SNAKE_CASE__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
UpperCAmelCase__ = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
UpperCAmelCase__ = json.loads(SCREAMING_SNAKE_CASE__ )
if not mpi_options.get("""sagemaker_mpi_enabled""" , SCREAMING_SNAKE_CASE__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : str = field(
default="""""" , metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , _UpperCAmelCase , )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
UpperCAmelCase__ = torch.device("""cpu""" )
UpperCAmelCase__ = 0
elif is_sagemaker_model_parallel_available():
UpperCAmelCase__ = smp.local_rank()
UpperCAmelCase__ = torch.device("""cuda""" , _UpperCAmelCase )
UpperCAmelCase__ = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
UpperCAmelCase__ = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
UpperCAmelCase__ = torch.device("""cuda""" , self.local_rank )
UpperCAmelCase__ = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
UpperCAmelCase__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
UpperCAmelCase__ = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
UpperCAmelCase__ = torch.device("""cuda""" , self.local_rank )
UpperCAmelCase__ = 1
if device.type == "cuda":
torch.cuda.set_device(_UpperCAmelCase )
return device
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
return not is_sagemaker_model_parallel_available()
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
return False
| 61
| 0
|
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__A : Union[str, Any] = "scheduler_config.json"
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 1
UpperCamelCase__ = 2
UpperCamelCase__ = 3
UpperCamelCase__ = 4
UpperCamelCase__ = 5
@dataclass
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = 42
class _a :
"""simple docstring"""
UpperCamelCase__ = SCHEDULER_CONFIG_NAME
UpperCamelCase__ = ["""dtype"""]
UpperCamelCase__ = []
UpperCamelCase__ = True
@classmethod
def lowercase__ ( cls : Any , __UpperCamelCase : Dict[str, Any] = None , __UpperCamelCase : Optional[str] = None , __UpperCamelCase : Tuple=False , **__UpperCamelCase : List[Any] , )->List[str]:
_UpperCAmelCase , _UpperCAmelCase = cls.load_config(
pretrained_model_name_or_path=__UpperCamelCase , subfolder=__UpperCamelCase , return_unused_kwargs=__UpperCamelCase , **__UpperCamelCase , )
_UpperCAmelCase , _UpperCAmelCase = cls.from_config(__UpperCamelCase , return_unused_kwargs=__UpperCamelCase , **__UpperCamelCase )
if hasattr(__UpperCamelCase , '''create_state''' ) and getattr(__UpperCamelCase , '''has_state''' , __UpperCamelCase ):
_UpperCAmelCase = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def lowercase__ ( self : Dict , __UpperCamelCase : Union[str, os.PathLike] , __UpperCamelCase : bool = False , **__UpperCamelCase : int )->Tuple:
self.save_config(save_directory=__UpperCamelCase , push_to_hub=__UpperCamelCase , **__UpperCamelCase )
@property
def lowercase__ ( self : Any )->int:
return self._get_compatibles()
@classmethod
def lowercase__ ( cls : Optional[int] )->Optional[int]:
_UpperCAmelCase = list(set([cls.__name__] + cls._compatibles ) )
_UpperCAmelCase = importlib.import_module(__name__.split('''.''' )[0] )
_UpperCAmelCase = [
getattr(__UpperCamelCase , __UpperCamelCase ) for c in compatible_classes_str if hasattr(__UpperCamelCase , __UpperCamelCase )
]
return compatible_classes
def lowercase ( _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : Tuple[int] ):
'''simple docstring'''
assert len(_SCREAMING_SNAKE_CASE ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_SCREAMING_SNAKE_CASE ) - x.ndim) ) , _SCREAMING_SNAKE_CASE )
def lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Any=0.999 , _SCREAMING_SNAKE_CASE : Union[str, Any]=jnp.floataa ):
'''simple docstring'''
def alpha_bar(_SCREAMING_SNAKE_CASE : List[Any] ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
_UpperCAmelCase = []
for i in range(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = i / num_diffusion_timesteps
_UpperCAmelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(_SCREAMING_SNAKE_CASE ) / alpha_bar(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) )
return jnp.array(_SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )
@flax.struct.dataclass
class _a :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = 42
UpperCamelCase__ = 42
@classmethod
def lowercase__ ( cls : Tuple , __UpperCamelCase : Optional[Any] )->List[str]:
_UpperCAmelCase = scheduler.config
if config.trained_betas is not None:
_UpperCAmelCase = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
_UpperCAmelCase = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_UpperCAmelCase = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCAmelCase = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}' )
_UpperCAmelCase = 1.0 - betas
_UpperCAmelCase = jnp.cumprod(__UpperCamelCase , axis=0 )
return cls(
alphas=__UpperCamelCase , betas=__UpperCamelCase , alphas_cumprod=__UpperCamelCase , )
def lowercase ( _SCREAMING_SNAKE_CASE : CommonSchedulerState , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray ):
'''simple docstring'''
_UpperCAmelCase = state.alphas_cumprod
_UpperCAmelCase = alphas_cumprod[timesteps] ** 0.5
_UpperCAmelCase = sqrt_alpha_prod.flatten()
_UpperCAmelCase = broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE , original_samples.shape )
_UpperCAmelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
_UpperCAmelCase = sqrt_one_minus_alpha_prod.flatten()
_UpperCAmelCase = broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def lowercase ( _SCREAMING_SNAKE_CASE : CommonSchedulerState , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def lowercase ( _SCREAMING_SNAKE_CASE : CommonSchedulerState , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray , _SCREAMING_SNAKE_CASE : jnp.ndarray ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 260
|
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (DPMSolverSinglestepScheduler,)
UpperCamelCase__ = (("""num_inference_steps""", 25),)
def lowercase__ ( self : Tuple , **__UpperCamelCase : Tuple )->Any:
_UpperCAmelCase = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0_0_0_1,
'''beta_end''': 0.0_2,
'''beta_schedule''': '''linear''',
'''solver_order''': 2,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
'''sample_max_value''': 1.0,
'''algorithm_type''': '''dpmsolver++''',
'''solver_type''': '''midpoint''',
'''lambda_min_clipped''': -float('''inf''' ),
'''variance_type''': None,
}
config.update(**__UpperCamelCase )
return config
def lowercase__ ( self : Dict , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : Optional[int] )->Tuple:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase , _UpperCAmelCase = sample, sample
for t in range(__UpperCamelCase , time_step + scheduler.config.solver_order + 1 ):
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : Any )->Union[str, Any]:
pass
def lowercase__ ( self : str , __UpperCamelCase : Tuple=0 , **__UpperCamelCase : List[Any] )->Dict:
_UpperCAmelCase = dict(self.forward_default_kwargs )
_UpperCAmelCase = kwargs.pop('''num_inference_steps''' , __UpperCamelCase )
_UpperCAmelCase = self.dummy_sample
_UpperCAmelCase = 0.1 * sample
_UpperCAmelCase = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
_UpperCAmelCase = self.get_scheduler_config()
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__UpperCamelCase )
_UpperCAmelCase = scheduler_class.from_pretrained(__UpperCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(__UpperCamelCase )
# copy over dummy past residual (must be after setting timesteps)
_UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order]
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
_UpperCAmelCase = new_scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowercase__ ( self : int , __UpperCamelCase : List[str]=None , **__UpperCamelCase : Optional[int] )->List[Any]:
if scheduler is None:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(**__UpperCamelCase )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
return sample
def lowercase__ ( self : List[Any] )->Dict:
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = 5_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter
scheduler.set_timesteps(__UpperCamelCase )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_5_7_4 ) < 1e-3
def lowercase__ ( self : Dict )->Dict:
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__UpperCamelCase )
def lowercase__ ( self : str )->Optional[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
_UpperCAmelCase = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
_UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config )
_UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_UpperCAmelCase = self.full_loop(scheduler=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->int:
self.check_over_configs(thresholding=__UpperCamelCase )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__UpperCamelCase , prediction_type=__UpperCamelCase , sample_max_value=__UpperCamelCase , algorithm_type='''dpmsolver++''' , solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , )
def lowercase__ ( self : str )->str:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__UpperCamelCase )
def lowercase__ ( self : List[Any] )->Tuple:
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
_UpperCAmelCase = self.full_loop(
solver_order=__UpperCamelCase , solver_type=__UpperCamelCase , prediction_type=__UpperCamelCase , algorithm_type=__UpperCamelCase , )
assert not torch.isnan(__UpperCamelCase ).any(), "Samples have nan numbers"
def lowercase__ ( self : Dict )->List[str]:
self.check_over_configs(lower_order_final=__UpperCamelCase )
self.check_over_configs(lower_order_final=__UpperCamelCase )
def lowercase__ ( self : Dict )->str:
self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def lowercase__ ( self : List[str] )->int:
self.check_over_configs(variance_type=__UpperCamelCase )
self.check_over_configs(variance_type='''learned_range''' )
def lowercase__ ( self : List[str] )->Union[str, Any]:
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=__UpperCamelCase , time_step=0 )
def lowercase__ ( self : List[Any] )->int:
_UpperCAmelCase = self.full_loop()
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_7_9_1 ) < 1e-3
def lowercase__ ( self : List[str] )->List[str]:
_UpperCAmelCase = self.full_loop(use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.2_2_4_8 ) < 1e-3
def lowercase__ ( self : int )->List[Any]:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.1_4_5_3 ) < 1e-3
def lowercase__ ( self : Optional[Any] )->Dict:
_UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=__UpperCamelCase )
_UpperCAmelCase = torch.mean(torch.abs(__UpperCamelCase ) )
assert abs(result_mean.item() - 0.0_6_4_9 ) < 1e-3
def lowercase__ ( self : Union[str, Any] )->List[str]:
_UpperCAmelCase = self.scheduler_classes[0]
_UpperCAmelCase = self.get_scheduler_config(thresholding=__UpperCamelCase , dynamic_thresholding_ratio=0 )
_UpperCAmelCase = scheduler_class(**__UpperCamelCase )
_UpperCAmelCase = 1_0
_UpperCAmelCase = self.dummy_model()
_UpperCAmelCase = self.dummy_sample_deter.half()
scheduler.set_timesteps(__UpperCamelCase )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase = model(__UpperCamelCase , __UpperCamelCase )
_UpperCAmelCase = scheduler.step(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ).prev_sample
assert sample.dtype == torch.floataa
| 260
| 1
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def __lowerCamelCase ( __UpperCamelCase = "AAPL" ) -> str:
"""simple docstring"""
lowerCAmelCase_ : Union[str, Any] = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowerCAmelCase_ : Tuple = BeautifulSoup(requests.get(__UpperCamelCase ).text , "html.parser" )
lowerCAmelCase_ : str = "My(6px) Pos(r) smartphone_Mt(6px)"
return soup.find("div" , class_=class_ ).find("span" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
| 161
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[str] , a_ : List[str] , a_ : Tuple=7 , a_ : Any=3 , a_ : Union[str, Any]=18 , a_ : List[str]=30 , a_ : List[str]=4_00 , a_ : str=True , a_ : Tuple=None , a_ : str=True , a_ : Optional[int]=None , ):
lowerCAmelCase_ : Any = size if size is not None else {"shortest_edge": 20}
lowerCAmelCase_ : Any = crop_size if crop_size is not None else {"height": 18, "width": 18}
lowerCAmelCase_ : int = parent
lowerCAmelCase_ : Dict = batch_size
lowerCAmelCase_ : Any = num_channels
lowerCAmelCase_ : str = image_size
lowerCAmelCase_ : int = min_resolution
lowerCAmelCase_ : Tuple = max_resolution
lowerCAmelCase_ : str = do_resize
lowerCAmelCase_ : List[Any] = size
lowerCAmelCase_ : Any = do_center_crop
lowerCAmelCase_ : Tuple = crop_size
def lowerCamelCase ( self : List[str] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowerCamelCase ( A__ , unittest.TestCase ):
'''simple docstring'''
a_ : Optional[Any] = MobileNetVaImageProcessor if is_vision_available() else None
def lowerCamelCase ( self : Optional[int] ):
lowerCAmelCase_ : int = MobileNetVaImageProcessingTester(self )
@property
def lowerCamelCase ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , "do_resize" ) )
self.assertTrue(hasattr(a_ , "size" ) )
self.assertTrue(hasattr(a_ , "do_center_crop" ) )
self.assertTrue(hasattr(a_ , "crop_size" ) )
def lowerCamelCase ( self : Tuple ):
lowerCAmelCase_ : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 20} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
lowerCAmelCase_ : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def lowerCamelCase ( self : Tuple ):
pass
def lowerCamelCase ( self : Any ):
# Initialize image_processing
lowerCAmelCase_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase_ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
lowerCAmelCase_ : str = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowerCAmelCase_ : List[str] = image_processing(a_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase ( self : str ):
# Initialize image_processing
lowerCAmelCase_ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
# Test not batched input
lowerCAmelCase_ : Tuple = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowerCAmelCase_ : Dict = image_processing(a_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase ( self : Union[str, Any] ):
# Initialize image_processing
lowerCAmelCase_ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
lowerCAmelCase_ : int = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
lowerCAmelCase_ : str = image_processing(a_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 161
| 1
|
'''simple docstring'''
from jiwer import compute_measures
import datasets
_lowerCAmelCase = '''\
@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.}
}
'''
_lowerCAmelCase = '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (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 words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''
_lowerCAmelCase = '''
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = ["this is the prediction", "there is an other sample"]
>>> references = ["this is the reference", "there is another one"]
>>> wer = datasets.load_metric("wer")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase_ ( 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""",
] ,)
def UpperCAmelCase_ ( self ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=False ) -> Union[str, Any]:
if concatenate_texts:
return compute_measures(__UpperCAmelCase ,__UpperCAmelCase )["wer"]
else:
lowerCAmelCase__ : Tuple = 0
lowerCAmelCase__ : Dict = 0
for prediction, reference in zip(__UpperCAmelCase ,__UpperCAmelCase ):
lowerCAmelCase__ : Optional[Any] = compute_measures(__UpperCAmelCase ,__UpperCAmelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 37
|
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase = 1_00_00_00 ) -> int:
'''simple docstring'''
lowercase_ = 1
lowercase_ = 1
lowercase_ = {1: 1}
for inputa in range(2 , __lowerCAmelCase ):
lowercase_ = 0
lowercase_ = inputa
while True:
if number in counters:
counter += counters[number]
break
if number % 2 == 0:
number //= 2
counter += 1
else:
lowercase_ = (3 * number) + 1
counter += 1
if inputa not in counters:
lowercase_ = counter
if counter > pre_counter:
lowercase_ = inputa
lowercase_ = counter
return largest_number
if __name__ == "__main__":
print(solution(int(input().strip())))
| 136
| 0
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
a = False
@skip_mps
class lowercase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : int = StableDiffusionAttendAndExcitePipeline
UpperCAmelCase : List[Any] = False
UpperCAmelCase : Dict = TEXT_TO_IMAGE_PARAMS
UpperCAmelCase : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
UpperCAmelCase : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
UpperCAmelCase : Optional[int] = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def lowerCAmelCase_ ( cls : Any ):
super().setUpClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : List[Any] ):
super().tearDownClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
torch.manual_seed(0 )
_A = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , 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 , )
_A = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
torch.manual_seed(0 )
_A = 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 )
_A = 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=1_000 , hidden_act='gelu' , projection_dim=512 , )
_A = CLIPTextModel(_UpperCAmelCase )
_A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_A = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[Any]=0 ):
if str(_UpperCAmelCase ).startswith('mps' ):
_A = torch.manual_seed(_UpperCAmelCase )
else:
_A = torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
_A = _A = {
'prompt': 'a cat and a frog',
'token_indices': [2, 5],
'generator': generator,
'num_inference_steps': 1,
'guidance_scale': 6.0,
'output_type': 'numpy',
'max_iter_to_alter': 2,
'thresholds': {0: 0.7},
}
return inputs
def lowerCAmelCase_ ( self : List[Any] ):
_A = 'cpu'
_A = self.get_dummy_components()
_A = self.pipeline_class(**_UpperCAmelCase )
pipe.to(_UpperCAmelCase )
pipe.set_progress_bar_config(disable=_UpperCAmelCase )
_A = self.get_dummy_inputs(_UpperCAmelCase )
_A = pipe(**_UpperCAmelCase ).images
_A = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 64, 64, 3) )
_A = np.array(
[0.6390_5364, 0.6289_7307, 0.4859_9017, 0.513_3624, 0.555_0048, 0.4576_9516, 0.5032_6973, 0.502_3139, 0.4538_4496] )
_A = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_UpperCAmelCase , 1E-3 )
def lowerCAmelCase_ ( self : int ):
super().test_cpu_offload_forward_pass(expected_max_diff=5E-4 )
def lowerCAmelCase_ ( self : Any ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCAmelCase_ ( self : Any ):
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7E-4 )
def lowerCAmelCase_ ( self : List[str] ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def lowerCAmelCase_ ( self : Any ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4 )
def lowerCAmelCase_ ( self : Dict ):
super().test_save_load_local(expected_max_difference=5E-4 )
def lowerCAmelCase_ ( self : Optional[int] ):
super().test_save_load_optional_components(expected_max_difference=4E-4 )
@require_torch_gpu
@slow
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def lowerCAmelCase_ ( cls : int ):
super().setUpClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
@classmethod
def lowerCAmelCase_ ( cls : List[str] ):
super().tearDownClass()
torch.use_deterministic_algorithms(_UpperCAmelCase )
def lowerCAmelCase_ ( self : List[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self : List[Any] ):
_A = torch.manual_seed(51 )
_A = StableDiffusionAttendAndExcitePipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , safety_checker=_UpperCAmelCase , torch_dtype=torch.floataa )
pipe.to('cuda' )
_A = 'a painting of an elephant with glasses'
_A = [5, 7]
_A = pipe(
prompt=_UpperCAmelCase , token_indices=_UpperCAmelCase , guidance_scale=7.5 , generator=_UpperCAmelCase , num_inference_steps=5 , max_iter_to_alter=5 , output_type='numpy' , ).images[0]
_A = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy' )
assert np.abs((expected_image - image).max() ) < 5E-1
| 271
|
"""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 _snake_case ( _snake_case : int ) -> Any:
'''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 lowercase_ :
'''simple docstring'''
def __init__( self : Tuple , _UpperCAmelCase : Iterable[torch.nn.Parameter] , _UpperCAmelCase : float = 0.9999 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : int = 0 , _UpperCAmelCase : bool = False , _UpperCAmelCase : Union[float, int] = 1.0 , _UpperCAmelCase : Union[float, int] = 2 / 3 , _UpperCAmelCase : Optional[Any] = None , _UpperCAmelCase : Dict[str, Any] = None , **_UpperCAmelCase : Optional[int] , ):
if isinstance(_UpperCAmelCase , torch.nn.Module ):
_A = (
'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' , _UpperCAmelCase , standard_warn=_UpperCAmelCase , )
_A = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
_A = True
if kwargs.get('max_value' , _UpperCAmelCase ) is not None:
_A = 'The `max_value` argument is deprecated. Please use `decay` instead.'
deprecate('max_value' , '1.0.0' , _UpperCAmelCase , standard_warn=_UpperCAmelCase )
_A = kwargs['max_value']
if kwargs.get('min_value' , _UpperCAmelCase ) is not None:
_A = 'The `min_value` argument is deprecated. Please use `min_decay` instead.'
deprecate('min_value' , '1.0.0' , _UpperCAmelCase , standard_warn=_UpperCAmelCase )
_A = kwargs['min_value']
_A = list(_UpperCAmelCase )
_A = [p.clone().detach() for p in parameters]
if kwargs.get('device' , _UpperCAmelCase ) is not None:
_A = 'The `device` argument is deprecated. Please use `to` instead.'
deprecate('device' , '1.0.0' , _UpperCAmelCase , standard_warn=_UpperCAmelCase )
self.to(device=kwargs['device'] )
_A = None
_A = decay
_A = min_decay
_A = update_after_step
_A = use_ema_warmup
_A = inv_gamma
_A = power
_A = 0
_A = None # set in `step()`
_A = model_cls
_A = model_config
@classmethod
def lowerCAmelCase_ ( cls : Any , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : Optional[int] ):
_A , _A = model_cls.load_config(_UpperCAmelCase , return_unused_kwargs=_UpperCAmelCase )
_A = model_cls.from_pretrained(_UpperCAmelCase )
_A = cls(model.parameters() , model_cls=_UpperCAmelCase , model_config=model.config )
ema_model.load_state_dict(_UpperCAmelCase )
return ema_model
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : Tuple ):
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 = self.model_cls.from_config(self.model_config )
_A = self.state_dict()
state_dict.pop('shadow_params' , _UpperCAmelCase )
model.register_to_config(**_UpperCAmelCase )
self.copy_to(model.parameters() )
model.save_pretrained(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[Any] , _UpperCAmelCase : int ):
_A = max(0 , optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
_A = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
_A = (1 + step) / (10 + step)
_A = min(_UpperCAmelCase , self.decay )
# make sure decay is not smaller than min_decay
_A = max(_UpperCAmelCase , self.min_decay )
return cur_decay_value
@torch.no_grad()
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Iterable[torch.nn.Parameter] ):
if isinstance(_UpperCAmelCase , torch.nn.Module ):
_A = (
'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' , _UpperCAmelCase , standard_warn=_UpperCAmelCase , )
_A = parameters.parameters()
_A = list(_UpperCAmelCase )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
_A = self.get_decay(self.optimization_step )
_A = decay
_A = 1 - decay
_A = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params , _UpperCAmelCase ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
_A = deepspeed.zero.GatheredParameters(_UpperCAmelCase , modifier_rank=_UpperCAmelCase )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(_UpperCAmelCase )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Iterable[torch.nn.Parameter] ):
_A = list(_UpperCAmelCase )
for s_param, param in zip(self.shadow_params , _UpperCAmelCase ):
param.data.copy_(s_param.to(param.device ).data )
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Dict=None ):
_A = [
p.to(device=_UpperCAmelCase , dtype=_UpperCAmelCase ) if p.is_floating_point() else p.to(device=_UpperCAmelCase )
for p in self.shadow_params
]
def lowerCAmelCase_ ( self : Dict ):
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 lowerCAmelCase_ ( self : Any , _UpperCAmelCase : Iterable[torch.nn.Parameter] ):
_A = [param.detach().cpu().clone() for param in parameters]
def lowerCAmelCase_ ( self : List[Any] , _UpperCAmelCase : Iterable[torch.nn.Parameter] ):
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 , _UpperCAmelCase ):
param.data.copy_(c_param.data )
# Better memory-wise.
_A = None
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : dict ):
_A = copy.deepcopy(_UpperCAmelCase )
_A = 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 = state_dict.get('min_decay' , self.min_decay )
if not isinstance(self.min_decay , _UpperCAmelCase ):
raise ValueError('Invalid min_decay' )
_A = state_dict.get('optimization_step' , self.optimization_step )
if not isinstance(self.optimization_step , _UpperCAmelCase ):
raise ValueError('Invalid optimization_step' )
_A = state_dict.get('update_after_step' , self.update_after_step )
if not isinstance(self.update_after_step , _UpperCAmelCase ):
raise ValueError('Invalid update_after_step' )
_A = state_dict.get('use_ema_warmup' , self.use_ema_warmup )
if not isinstance(self.use_ema_warmup , _UpperCAmelCase ):
raise ValueError('Invalid use_ema_warmup' )
_A = state_dict.get('inv_gamma' , self.inv_gamma )
if not isinstance(self.inv_gamma , (float, int) ):
raise ValueError('Invalid inv_gamma' )
_A = state_dict.get('power' , self.power )
if not isinstance(self.power , (float, int) ):
raise ValueError('Invalid power' )
_A = state_dict.get('shadow_params' , _UpperCAmelCase )
if shadow_params is not None:
_A = shadow_params
if not isinstance(self.shadow_params , _UpperCAmelCase ):
raise ValueError('shadow_params must be a list' )
if not all(isinstance(_UpperCAmelCase , torch.Tensor ) for p in self.shadow_params ):
raise ValueError('shadow_params must all be Tensors' )
| 271
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCAmelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 37
|
'''simple docstring'''
from collections import UserDict
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_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
_lowerCAmelCase = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_ )
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self ,**__UpperCAmelCase ) -> Tuple:
super().__init__(**__UpperCAmelCase )
requires_backends(self ,"""vision""" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self ,__UpperCAmelCase ,**__UpperCAmelCase ) -> str:
return super().__call__(__UpperCAmelCase ,**__UpperCAmelCase )
def UpperCAmelCase_ ( self ,**__UpperCAmelCase ) -> str:
lowerCAmelCase__ : List[Any] = {}
if "candidate_labels" in kwargs:
lowerCAmelCase__ : int = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
lowerCAmelCase__ : Optional[int] = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase=None ,__UpperCAmelCase="This is a photo of {}." ) -> int:
lowerCAmelCase__ : str = load_image(__UpperCAmelCase )
lowerCAmelCase__ : Dict = self.image_processor(images=[image] ,return_tensors=self.framework )
lowerCAmelCase__ : List[Any] = candidate_labels
lowerCAmelCase__ : List[str] = [hypothesis_template.format(__UpperCAmelCase ) for x in candidate_labels]
lowerCAmelCase__ : Optional[Any] = self.tokenizer(__UpperCAmelCase ,return_tensors=self.framework ,padding=__UpperCAmelCase )
lowerCAmelCase__ : Tuple = [text_inputs]
return inputs
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Union[str, Any]:
lowerCAmelCase__ : Tuple = model_inputs.pop("""candidate_labels""" )
lowerCAmelCase__ : Union[str, Any] = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] ,__UpperCAmelCase ):
lowerCAmelCase__ : int = text_inputs[0]
else:
# Batching case.
lowerCAmelCase__ : Dict = text_inputs[0][0]
lowerCAmelCase__ : Any = self.model(**__UpperCAmelCase ,**__UpperCAmelCase )
lowerCAmelCase__ : Union[str, Any] = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_image,
}
return model_outputs
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Any:
lowerCAmelCase__ : Union[str, Any] = model_outputs.pop("""candidate_labels""" )
lowerCAmelCase__ : List[str] = model_outputs["""logits"""][0]
if self.framework == "pt":
lowerCAmelCase__ : List[str] = logits.softmax(dim=-1 ).squeeze(-1 )
lowerCAmelCase__ : Optional[Any] = probs.tolist()
if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ):
lowerCAmelCase__ : Dict = [scores]
elif self.framework == "tf":
lowerCAmelCase__ : Any = stable_softmax(__UpperCAmelCase ,axis=-1 )
lowerCAmelCase__ : List[Any] = probs.numpy().tolist()
else:
raise ValueError(F"""Unsupported framework: {self.framework}""" )
lowerCAmelCase__ : Tuple = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(__UpperCAmelCase ,__UpperCAmelCase ) ,key=lambda __UpperCAmelCase : -x[0] )
]
return result
| 37
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCamelCase : Union[str, Any] = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCamelCase : List[Any] = TaTokenizerFast
UpperCamelCase : List[str] = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : int = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Union[str, Any] = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Optional[Any] = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCamelCase : Optional[int] = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 360
|
"""simple docstring"""
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def A ( snake_case :List[Any] , snake_case :Dict=1 ) -> Optional[int]:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_prefix_segments:] )
else:
return ".".join(path.split('.' )[:n_shave_prefix_segments] )
def A ( snake_case :Dict , snake_case :int=0 ) -> Optional[int]:
__UpperCamelCase = []
for old_item in old_list:
__UpperCamelCase = old_item.replace('in_layers.0' , 'norm1' )
__UpperCamelCase = new_item.replace('in_layers.2' , 'conv1' )
__UpperCamelCase = new_item.replace('out_layers.0' , 'norm2' )
__UpperCamelCase = new_item.replace('out_layers.3' , 'conv2' )
__UpperCamelCase = new_item.replace('emb_layers.1' , 'time_emb_proj' )
__UpperCamelCase = new_item.replace('skip_connection' , 'conv_shortcut' )
__UpperCamelCase = shave_segments(snake_case , n_shave_prefix_segments=snake_case )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def A ( snake_case :Optional[Any] , snake_case :Tuple=0 ) -> Tuple:
__UpperCamelCase = []
for old_item in old_list:
__UpperCamelCase = old_item
__UpperCamelCase = new_item.replace('norm.weight' , 'group_norm.weight' )
__UpperCamelCase = new_item.replace('norm.bias' , 'group_norm.bias' )
__UpperCamelCase = new_item.replace('proj_out.weight' , 'proj_attn.weight' )
__UpperCamelCase = new_item.replace('proj_out.bias' , 'proj_attn.bias' )
__UpperCamelCase = shave_segments(snake_case , n_shave_prefix_segments=snake_case )
mapping.append({'old': old_item, 'new': new_item} )
return mapping
def A ( snake_case :int , snake_case :List[str] , snake_case :List[str] , snake_case :Any=None , snake_case :Optional[int]=None , snake_case :Union[str, Any]=None ) -> Optional[int]:
assert isinstance(snake_case , snake_case ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
__UpperCamelCase = old_checkpoint[path]
__UpperCamelCase = old_tensor.shape[0] // 3
__UpperCamelCase = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
__UpperCamelCase = old_tensor.shape[0] // config['num_head_channels'] // 3
__UpperCamelCase = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = old_tensor.split(channels // num_heads , dim=1 )
__UpperCamelCase = query.reshape(snake_case )
__UpperCamelCase = key.reshape(snake_case )
__UpperCamelCase = value.reshape(snake_case )
for path in paths:
__UpperCamelCase = path['new']
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
__UpperCamelCase = new_path.replace('middle_block.0' , 'mid_block.resnets.0' )
__UpperCamelCase = new_path.replace('middle_block.1' , 'mid_block.attentions.0' )
__UpperCamelCase = new_path.replace('middle_block.2' , 'mid_block.resnets.1' )
if additional_replacements is not None:
for replacement in additional_replacements:
__UpperCamelCase = new_path.replace(replacement['old'] , replacement['new'] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
__UpperCamelCase = old_checkpoint[path['old']][:, :, 0]
else:
__UpperCamelCase = old_checkpoint[path['old']]
def A ( snake_case :Optional[Any] , snake_case :Dict ) -> Optional[Any]:
__UpperCamelCase = {}
__UpperCamelCase = checkpoint['time_embed.0.weight']
__UpperCamelCase = checkpoint['time_embed.0.bias']
__UpperCamelCase = checkpoint['time_embed.2.weight']
__UpperCamelCase = checkpoint['time_embed.2.bias']
__UpperCamelCase = checkpoint['input_blocks.0.0.weight']
__UpperCamelCase = checkpoint['input_blocks.0.0.bias']
__UpperCamelCase = checkpoint['out.0.weight']
__UpperCamelCase = checkpoint['out.0.bias']
__UpperCamelCase = checkpoint['out.2.weight']
__UpperCamelCase = checkpoint['out.2.bias']
# Retrieves the keys for the input blocks only
__UpperCamelCase = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'input_blocks' in layer} )
__UpperCamelCase = {
layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
for layer_id in range(snake_case )
}
# Retrieves the keys for the middle blocks only
__UpperCamelCase = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'middle_block' in layer} )
__UpperCamelCase = {
layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
for layer_id in range(snake_case )
}
# Retrieves the keys for the output blocks only
__UpperCamelCase = len({'.'.join(layer.split('.' )[:2] ) for layer in checkpoint if 'output_blocks' in layer} )
__UpperCamelCase = {
layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
for layer_id in range(snake_case )
}
for i in range(1 , snake_case ):
__UpperCamelCase = (i - 1) // (config['num_res_blocks'] + 1)
__UpperCamelCase = (i - 1) % (config['num_res_blocks'] + 1)
__UpperCamelCase = [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
__UpperCamelCase = [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
if f'input_blocks.{i}.0.op.weight' in checkpoint:
__UpperCamelCase = checkpoint[
f'input_blocks.{i}.0.op.weight'
]
__UpperCamelCase = checkpoint[
f'input_blocks.{i}.0.op.bias'
]
continue
__UpperCamelCase = renew_resnet_paths(snake_case )
__UpperCamelCase = {'old': f'input_blocks.{i}.0', 'new': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
__UpperCamelCase = {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
assign_to_checkpoint(
snake_case , snake_case , snake_case , additional_replacements=[meta_path, resnet_op] , config=snake_case )
if len(snake_case ):
__UpperCamelCase = renew_attention_paths(snake_case )
__UpperCamelCase = {
'old': f'input_blocks.{i}.1',
'new': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
}
__UpperCamelCase = {
f'input_blocks.{i}.1.qkv.bias': {
'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
f'input_blocks.{i}.1.qkv.weight': {
'key': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
'query': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
'value': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
snake_case , snake_case , snake_case , additional_replacements=[meta_path] , attention_paths_to_split=snake_case , config=snake_case , )
__UpperCamelCase = middle_blocks[0]
__UpperCamelCase = middle_blocks[1]
__UpperCamelCase = middle_blocks[2]
__UpperCamelCase = renew_resnet_paths(snake_case )
assign_to_checkpoint(snake_case , snake_case , snake_case , config=snake_case )
__UpperCamelCase = renew_resnet_paths(snake_case )
assign_to_checkpoint(snake_case , snake_case , snake_case , config=snake_case )
__UpperCamelCase = renew_attention_paths(snake_case )
__UpperCamelCase = {
'middle_block.1.qkv.bias': {
'key': 'mid_block.attentions.0.key.bias',
'query': 'mid_block.attentions.0.query.bias',
'value': 'mid_block.attentions.0.value.bias',
},
'middle_block.1.qkv.weight': {
'key': 'mid_block.attentions.0.key.weight',
'query': 'mid_block.attentions.0.query.weight',
'value': 'mid_block.attentions.0.value.weight',
},
}
assign_to_checkpoint(
snake_case , snake_case , snake_case , attention_paths_to_split=snake_case , config=snake_case )
for i in range(snake_case ):
__UpperCamelCase = i // (config['num_res_blocks'] + 1)
__UpperCamelCase = i % (config['num_res_blocks'] + 1)
__UpperCamelCase = [shave_segments(snake_case , 2 ) for name in output_blocks[i]]
__UpperCamelCase = {}
for layer in output_block_layers:
__UpperCamelCase , __UpperCamelCase = layer.split('.' )[0], shave_segments(snake_case , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(snake_case )
else:
__UpperCamelCase = [layer_name]
if len(snake_case ) > 1:
__UpperCamelCase = [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
__UpperCamelCase = [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
__UpperCamelCase = renew_resnet_paths(snake_case )
__UpperCamelCase = renew_resnet_paths(snake_case )
__UpperCamelCase = {'old': f'output_blocks.{i}.0', 'new': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
assign_to_checkpoint(snake_case , snake_case , snake_case , additional_replacements=[meta_path] , config=snake_case )
if ["conv.weight", "conv.bias"] in output_block_list.values():
__UpperCamelCase = list(output_block_list.values() ).index(['conv.weight', 'conv.bias'] )
__UpperCamelCase = checkpoint[
f'output_blocks.{i}.{index}.conv.weight'
]
__UpperCamelCase = checkpoint[
f'output_blocks.{i}.{index}.conv.bias'
]
# Clear attentions as they have been attributed above.
if len(snake_case ) == 2:
__UpperCamelCase = []
if len(snake_case ):
__UpperCamelCase = renew_attention_paths(snake_case )
__UpperCamelCase = {
'old': f'output_blocks.{i}.1',
'new': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
}
__UpperCamelCase = {
f'output_blocks.{i}.1.qkv.bias': {
'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
},
f'output_blocks.{i}.1.qkv.weight': {
'key': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
'query': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
'value': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
},
}
assign_to_checkpoint(
snake_case , snake_case , snake_case , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any('qkv' in key for key in attentions ) else None , config=snake_case , )
else:
__UpperCamelCase = renew_resnet_paths(snake_case , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
__UpperCamelCase = '.'.join(['output_blocks', str(snake_case ), path['old']] )
__UpperCamelCase = '.'.join(['up_blocks', str(snake_case ), 'resnets', str(snake_case ), path['new']] )
__UpperCamelCase = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
UpperCamelCase : Any = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the architecture.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
UpperCamelCase : Dict = parser.parse_args()
UpperCamelCase : Optional[Any] = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
UpperCamelCase : int = json.loads(f.read())
UpperCamelCase : Dict = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
UpperCamelCase : Optional[Any] = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
UpperCamelCase : List[Any] = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
UpperCamelCase : Optional[int] = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))
UpperCamelCase : Dict = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 263
| 0
|
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( lowerCAmelCase__ ):
UpperCamelCase__ : Any =(DDPMParallelScheduler,)
def lowerCamelCase ( self : Any , **lowercase_ : List[str] ) -> int:
"""simple docstring"""
_lowerCamelCase : int ={
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'variance_type': 'fixed_small',
'clip_sample': True,
}
config.update(**__a )
return config
def lowerCamelCase ( self : Tuple ) -> List[str]:
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=__a )
def lowerCamelCase ( self : int ) -> List[str]:
"""simple docstring"""
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=__a , beta_end=__a )
def lowerCamelCase ( self : Optional[Any] ) -> str:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__a )
def lowerCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__a )
def lowerCamelCase ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__a )
def lowerCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
self.check_over_configs(thresholding=__a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , )
def lowerCamelCase ( self : str ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def lowerCamelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=__a )
def lowerCamelCase ( self : str ) -> int:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =self.scheduler_classes[0]
_lowerCamelCase : int =self.get_scheduler_config()
_lowerCamelCase : Any =scheduler_class(**__a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def lowerCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
_lowerCamelCase : Any =self.scheduler_classes[0]
_lowerCamelCase : Any =self.get_scheduler_config()
_lowerCamelCase : str =scheduler_class(**__a )
_lowerCamelCase : Dict =len(__a )
_lowerCamelCase : Union[str, Any] =self.dummy_model()
_lowerCamelCase : int =self.dummy_sample_deter
_lowerCamelCase : str =self.dummy_sample_deter + 0.1
_lowerCamelCase : List[str] =self.dummy_sample_deter - 0.1
_lowerCamelCase : List[str] =samplea.shape[0]
_lowerCamelCase : List[str] =torch.stack([samplea, samplea, samplea] , dim=0 )
_lowerCamelCase : List[str] =torch.arange(__a )[0:3, None].repeat(1 , __a )
_lowerCamelCase : Union[str, Any] =model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
_lowerCamelCase : Dict =scheduler.batch_step_no_noise(__a , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
_lowerCamelCase : str =torch.sum(torch.abs(__a ) )
_lowerCamelCase : Optional[Any] =torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 1153.1833 ) < 1E-2
assert abs(result_mean.item() - 0.5005 ) < 1E-3
def lowerCamelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : List[str] =self.scheduler_classes[0]
_lowerCamelCase : Optional[int] =self.get_scheduler_config()
_lowerCamelCase : List[str] =scheduler_class(**__a )
_lowerCamelCase : List[Any] =len(__a )
_lowerCamelCase : List[Any] =self.dummy_model()
_lowerCamelCase : Union[str, Any] =self.dummy_sample_deter
_lowerCamelCase : Optional[int] =torch.manual_seed(0 )
for t in reversed(range(__a ) ):
# 1. predict noise residual
_lowerCamelCase : int =model(__a , __a )
# 2. predict previous mean of sample x_t-1
_lowerCamelCase : Union[str, Any] =scheduler.step(__a , __a , __a , generator=__a ).prev_sample
_lowerCamelCase : Optional[Any] =pred_prev_sample
_lowerCamelCase : str =torch.sum(torch.abs(__a ) )
_lowerCamelCase : Optional[Any] =torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 258.9606 ) < 1E-2
assert abs(result_mean.item() - 0.3372 ) < 1E-3
def lowerCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : int =self.scheduler_classes[0]
_lowerCamelCase : Union[str, Any] =self.get_scheduler_config(prediction_type='v_prediction' )
_lowerCamelCase : Any =scheduler_class(**__a )
_lowerCamelCase : Any =len(__a )
_lowerCamelCase : List[Any] =self.dummy_model()
_lowerCamelCase : Any =self.dummy_sample_deter
_lowerCamelCase : Optional[int] =torch.manual_seed(0 )
for t in reversed(range(__a ) ):
# 1. predict noise residual
_lowerCamelCase : List[str] =model(__a , __a )
# 2. predict previous mean of sample x_t-1
_lowerCamelCase : Any =scheduler.step(__a , __a , __a , generator=__a ).prev_sample
_lowerCamelCase : str =pred_prev_sample
_lowerCamelCase : List[str] =torch.sum(torch.abs(__a ) )
_lowerCamelCase : Dict =torch.mean(torch.abs(__a ) )
assert abs(result_sum.item() - 202.0296 ) < 1E-2
assert abs(result_mean.item() - 0.2631 ) < 1E-3
def lowerCamelCase ( self : List[Any] ) -> Dict:
"""simple docstring"""
_lowerCamelCase : Union[str, Any] =self.scheduler_classes[0]
_lowerCamelCase : str =self.get_scheduler_config()
_lowerCamelCase : List[Any] =scheduler_class(**__a )
_lowerCamelCase : Tuple =[100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=__a )
_lowerCamelCase : Dict =scheduler.timesteps
for i, timestep in enumerate(__a ):
if i == len(__a ) - 1:
_lowerCamelCase : Any =-1
else:
_lowerCamelCase : str =timesteps[i + 1]
_lowerCamelCase : Union[str, Any] =scheduler.previous_timestep(__a )
_lowerCamelCase : List[Any] =prev_t.item()
self.assertEqual(__a , __a )
def lowerCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : int =self.scheduler_classes[0]
_lowerCamelCase : Dict =self.get_scheduler_config()
_lowerCamelCase : str =scheduler_class(**__a )
_lowerCamelCase : Dict =[100, 87, 50, 51, 0]
with self.assertRaises(__a , msg='`custom_timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=__a )
def lowerCamelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
_lowerCamelCase : str =self.scheduler_classes[0]
_lowerCamelCase : List[Any] =self.get_scheduler_config()
_lowerCamelCase : Any =scheduler_class(**__a )
_lowerCamelCase : Optional[Any] =[100, 87, 50, 1, 0]
_lowerCamelCase : Optional[Any] =len(__a )
with self.assertRaises(__a , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=__a , timesteps=__a )
def lowerCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
_lowerCamelCase : str =self.scheduler_classes[0]
_lowerCamelCase : Optional[int] =self.get_scheduler_config()
_lowerCamelCase : Optional[Any] =scheduler_class(**__a )
_lowerCamelCase : Any =[scheduler.config.num_train_timesteps]
with self.assertRaises(
__a , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=__a )
| 199
|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =["""image_processor""", """tokenizer"""]
__UpperCAmelCase : Optional[Any] ="""CLIPImageProcessor"""
__UpperCAmelCase : Union[str, Any] =("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
def __init__( self , __a=None , __a=None , **__a ):
__lowerCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __a , )
__lowerCAmelCase = kwargs.pop("feature_extractor" )
__lowerCAmelCase = 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__(__a , __a )
def __call__( self , __a=None , __a=None , __a=None , **__a ):
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
__lowerCAmelCase = self.tokenizer(__a , return_tensors=__a , **__a )
if images is not None:
__lowerCAmelCase = self.image_processor(__a , return_tensors=__a , **__a )
if text is not None and images is not None:
__lowerCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__a ) , tensor_type=__a )
def snake_case ( self , *__a , **__a ):
return self.tokenizer.batch_decode(*__a , **__a )
def snake_case ( self , *__a , **__a ):
return self.tokenizer.decode(*__a , **__a )
@property
def snake_case ( self ):
__lowerCAmelCase = self.tokenizer.model_input_names
__lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 57
| 0
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
UpperCAmelCase__ = None
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
},
"tokenizer_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
},
}
UpperCAmelCase__ = {
"google/fnet-base": 512,
"google/fnet-large": 512,
}
UpperCAmelCase__ = "▁"
class lowercase_ ( lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case = ['''input_ids''', '''token_type_ids''']
__snake_case = FNetTokenizer
def __init__( self : List[str] , __UpperCAmelCase : Union[str, Any]=None , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : Union[str, Any]=False , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Dict=True , __UpperCAmelCase : Dict="<unk>" , __UpperCAmelCase : Optional[Any]="[SEP]" , __UpperCAmelCase : List[str]="<pad>" , __UpperCAmelCase : Optional[int]="[CLS]" , __UpperCAmelCase : Any="[MASK]" , **__UpperCAmelCase : Tuple , ) ->List[Any]:
"""simple docstring"""
a = (
AddedToken(__UpperCAmelCase , lstrip=__UpperCAmelCase , rstrip=__UpperCAmelCase , normalized=__UpperCAmelCase )
if isinstance(__UpperCAmelCase , __UpperCAmelCase )
else mask_token
)
super().__init__(
__UpperCAmelCase , tokenizer_file=__UpperCAmelCase , do_lower_case=__UpperCAmelCase , remove_space=__UpperCAmelCase , keep_accents=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , mask_token=__UpperCAmelCase , **__UpperCAmelCase , )
a = do_lower_case
a = remove_space
a = keep_accents
a = vocab_file
a = False if not self.vocab_file else True
def __lowerCAmelCase ( self : List[str] , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def __lowerCAmelCase ( self : str , __UpperCAmelCase : List[int] , __UpperCAmelCase : Optional[List[int]] = None ) ->List[int]:
"""simple docstring"""
a = [self.sep_token_id]
a = [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 __lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : str , __UpperCAmelCase : Optional[str] = None ) ->Tuple[str]:
"""simple docstring"""
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'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ):
copyfile(self.vocab_file , __UpperCAmelCase )
return (out_vocab_file,)
| 26
|
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger("transformers.models.speecht5")
def _a ( a :Optional[Any] , a :Tuple , a :Dict ) -> List[str]:
hf_model.apply_weight_norm()
a = checkpoint['''input_conv.weight_g''']
a = checkpoint['''input_conv.weight_v''']
a = checkpoint['''input_conv.bias''']
for i in range(len(config.upsample_rates ) ):
a = checkpoint[F"""upsamples.{i}.1.weight_g"""]
a = checkpoint[F"""upsamples.{i}.1.weight_v"""]
a = checkpoint[F"""upsamples.{i}.1.bias"""]
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
a = checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]
a = checkpoint['''output_conv.1.weight_g''']
a = checkpoint['''output_conv.1.weight_v''']
a = checkpoint['''output_conv.1.bias''']
hf_model.remove_weight_norm()
@torch.no_grad()
def _a ( a :List[str] , a :Union[str, Any] , a :Dict , a :Dict=None , a :List[Any]=None , ) -> int:
if config_path is not None:
a = SpeechTaHifiGanConfig.from_pretrained(a )
else:
a = SpeechTaHifiGanConfig()
a = SpeechTaHifiGan(a )
a = torch.load(a )
load_weights(orig_checkpoint['''model''']['''generator'''] , a , a )
a = np.load(a )
a = stats[0].reshape(-1 )
a = stats[1].reshape(-1 )
a = torch.from_numpy(a ).float()
a = torch.from_numpy(a ).float()
model.save_pretrained(a )
if repo_id:
print('''Pushing to the hub...''' )
model.push_to_hub(a )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
)
parser.add_argument(
"--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
)
UpperCAmelCase__ = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 26
| 1
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
__A = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"adapter_layer": "encoder.layers.*.adapter_layer",
"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",
"pooling_layer.linear": "projector",
"pooling_layer.projection": "classifier",
}
__A = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"projector",
"classifier",
]
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ) -> int:
"""simple docstring"""
__lowerCamelCase = {}
with open(UpperCamelCase__ , 'r' ) as file:
for line_number, line in enumerate(UpperCamelCase__ ):
__lowerCamelCase = line.strip()
if line:
__lowerCamelCase = line.split()
__lowerCamelCase = line_number
__lowerCamelCase = words[0]
__lowerCamelCase = value
return result
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
for attribute in key.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(UpperCamelCase__ ):
__lowerCamelCase = PARAM_MAPPING[full_name.split('.' )[-1]]
__lowerCamelCase = 'param'
if weight_type is not None and weight_type != "param":
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
elif weight_type is not None and weight_type == "param":
__lowerCamelCase = hf_pointer
for attribute in hf_param_name.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = shape_pointer.shape
# let's reduce dimension
__lowerCamelCase = value[0]
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 == "param":
for attribute in hf_param_name.split('.' ):
__lowerCamelCase = getattr(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = value
else:
__lowerCamelCase = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(UpperCamelCase__ ):
__lowerCamelCase = PARAM_MAPPING[full_name.split('.' )[-1]]
__lowerCamelCase = 'param'
if weight_type is not None and weight_type != "param":
__lowerCamelCase = '.'.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
__lowerCamelCase = '.'.join([key, hf_param_name] )
else:
__lowerCamelCase = key
__lowerCamelCase = value if 'lm_head' in full_key else value[0]
__A = {
"W_a": "linear_1.weight",
"W_b": "linear_2.weight",
"b_a": "linear_1.bias",
"b_b": "linear_2.bias",
"ln_W": "norm.weight",
"ln_b": "norm.bias",
}
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Optional[int]=None ) -> Union[str, Any]:
"""simple docstring"""
__lowerCamelCase = False
for key, mapped_key in MAPPING.items():
__lowerCamelCase = 'wav2vec2.' + 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 "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'
else:
__lowerCamelCase = None
if hf_dict is not None:
rename_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
else:
set_recursively(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return is_used
return is_used
def lowerCamelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = []
__lowerCamelCase = fairseq_model.state_dict()
__lowerCamelCase = hf_model.wavaveca.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:
__lowerCamelCase = load_wavaveca_layer(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
if not is_used:
unused_weights.append(UpperCamelCase__ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : int ) -> Any:
"""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 lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Any=None , UpperCamelCase__ : str=None , UpperCamelCase__ : int=True , UpperCamelCase__ : str=False ) -> str:
"""simple docstring"""
if config_path is not None:
__lowerCamelCase = WavaVecaConfig.from_pretrained(UpperCamelCase__ )
else:
__lowerCamelCase = WavaVecaConfig()
if is_seq_class:
__lowerCamelCase = read_txt_into_dict(UpperCamelCase__ )
__lowerCamelCase = idalabel
__lowerCamelCase = WavaVecaForSequenceClassification(UpperCamelCase__ )
__lowerCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , )
feature_extractor.save_pretrained(UpperCamelCase__ )
elif 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=1_6000 , padding_value=0 , do_normalize=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , )
__lowerCamelCase = WavaVecaProcessor(feature_extractor=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
processor.save_pretrained(UpperCamelCase__ )
__lowerCamelCase = WavaVecaForCTC(UpperCamelCase__ )
else:
__lowerCamelCase = WavaVecaForPreTraining(UpperCamelCase__ )
if is_finetuned or is_seq_class:
__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 = 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"
)
parser.add_argument(
"--is_seq_class",
action="store_true",
help="Whether the model to convert is a fine-tuned sequence classification model or not",
)
__A = parser.parse_args()
__A = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 90
|
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : Any=1024 ) -> Dict:
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase = [], []
__lowerCamelCase = list(zip(UpperCamelCase__ , UpperCamelCase__ ) )
__lowerCamelCase , __lowerCamelCase = sorted_examples[0]
def is_too_big(UpperCamelCase__ : List[str] ):
return tok(UpperCamelCase__ , return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
__lowerCamelCase = new_src + ' ' + src
__lowerCamelCase = new_tgt + ' ' + tgt
if is_too_big(UpperCamelCase__ ) or is_too_big(UpperCamelCase__ ): # cant fit, finalize example
finished_src.append(UpperCamelCase__ )
finished_tgt.append(UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase = src, tgt
else: # can fit, keep adding
__lowerCamelCase , __lowerCamelCase = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(UpperCamelCase__ )
finished_tgt.append(UpperCamelCase__ )
return finished_src, finished_tgt
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : Path , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : str ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = Path(UpperCamelCase__ )
save_path.mkdir(exist_ok=UpperCamelCase__ )
for split in ["train"]:
__lowerCamelCase , __lowerCamelCase = data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
__lowerCamelCase = [x.rstrip() for x in Path(UpperCamelCase__ ).open().readlines()]
__lowerCamelCase = [x.rstrip() for x in Path(UpperCamelCase__ ).open().readlines()]
__lowerCamelCase , __lowerCamelCase = pack_examples(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
print(F"""packed {split} split from {len(UpperCamelCase__ )} examples -> {len(UpperCamelCase__ )}.""" )
Path(save_path / F"""{split}.source""" ).open('w' ).write('\n'.join(UpperCamelCase__ ) )
Path(save_path / F"""{split}.target""" ).open('w' ).write('\n'.join(UpperCamelCase__ ) )
for split in ["val", "test"]:
__lowerCamelCase , __lowerCamelCase = data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
shutil.copyfile(UpperCamelCase__ , save_path / F"""{split}.source""" )
shutil.copyfile(UpperCamelCase__ , save_path / F"""{split}.target""" )
def lowerCamelCase_ ( ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('--tok_name' , type=UpperCamelCase__ , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('--max_seq_len' , type=UpperCamelCase__ , default=128 )
parser.add_argument('--data_dir' , type=UpperCamelCase__ )
parser.add_argument('--save_path' , type=UpperCamelCase__ )
__lowerCamelCase = parser.parse_args()
__lowerCamelCase = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(UpperCamelCase__ , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 90
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase : int = {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[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
__UpperCamelCase : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 74
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = '''▁'''
__UpperCamelCase : Union[str, Any] = {'''vocab_file''': '''spiece.model'''}
__UpperCamelCase : Tuple = {
'''vocab_file''': {
'''google/reformer-crime-and-punishment''': (
'''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
)
}
}
__UpperCamelCase : Optional[Any] = {
'''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = ["input_ids", "attention_mask"]
def __init__( self : List[Any] ,lowercase_ : List[str] ,lowercase_ : Optional[int]="</s>" ,lowercase_ : List[Any]="<unk>" ,lowercase_ : Optional[Any]=[] ,lowercase_ : Optional[Dict[str, Any]] = None ,**lowercase_ : int ,):
lowerCAmelCase__ : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=lowercase_ ,unk_token=lowercase_ ,additional_special_tokens=lowercase_ ,sp_model_kwargs=self.sp_model_kwargs ,**lowercase_ ,)
lowerCAmelCase__ : List[str] = vocab_file
lowerCAmelCase__ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase_ )
@property
def __lowerCAmelCase ( self : List[str] ):
return self.sp_model.get_piece_size()
def __lowerCAmelCase ( self : Any ):
lowerCAmelCase__ : Optional[Any] = {self.convert_ids_to_tokens(lowercase_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
lowerCAmelCase__ : str = self.__dict__.copy()
lowerCAmelCase__ : Any = None
return state
def __setstate__( self : List[str] ,lowercase_ : Any ):
lowerCAmelCase__ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs''' ):
lowerCAmelCase__ : Tuple = {}
lowerCAmelCase__ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCAmelCase ( self : Dict ,lowercase_ : str ):
return self.sp_model.encode(lowercase_ ,out_type=lowercase_ )
def __lowerCAmelCase ( self : List[Any] ,lowercase_ : int ):
return self.sp_model.piece_to_id(lowercase_ )
def __lowerCAmelCase ( self : Optional[Any] ,lowercase_ : Dict ):
if index < self.sp_model.get_piece_size():
lowerCAmelCase__ : List[Any] = self.sp_model.IdToPiece(lowercase_ )
return token
def __lowerCAmelCase ( self : Optional[Any] ,lowercase_ : List[Any] ):
lowerCAmelCase__ : int = []
lowerCAmelCase__ : Optional[Any] = ''''''
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(lowercase_ ) + token
lowerCAmelCase__ : Dict = []
else:
current_sub_tokens.append(lowercase_ )
out_string += self.sp_model.decode(lowercase_ )
return out_string.strip()
def __lowerCAmelCase ( self : Optional[Any] ,lowercase_ : str ,lowercase_ : Optional[str] = None ):
if not os.path.isdir(lowercase_ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase__ : List[Any] = os.path.join(
lowercase_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file ,lowercase_ )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase_ ,'''wb''' ) as fi:
lowerCAmelCase__ : Any = self.sp_model.serialized_model_proto()
fi.write(lowercase_ )
return (out_vocab_file,)
| 74
| 1
|
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[str] ) -> Dict:
_lowerCAmelCase : Any = min(_lowerCamelCase ) # min() finds the minimum value
_lowerCAmelCase : Dict = max(_lowerCamelCase ) # max() finds the maximum value
_lowerCAmelCase : Tuple = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
_lowerCAmelCase : Optional[Any] = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(_lowerCamelCase ,_lowerCamelCase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
_lowerCAmelCase : Tuple = 0
for count in range(_lowerCamelCase ):
while holes[count] > 0:
holes[count] -= 1
_lowerCAmelCase : Optional[int] = count + min_val
i += 1
def SCREAMING_SNAKE_CASE ( ) -> List[Any]:
_lowerCAmelCase : List[str] = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(_lowerCamelCase )
print("""Sorted order is:""" ,""" """.join(_lowerCamelCase ) )
if __name__ == "__main__":
main()
| 44
|
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
raise Exception('requires fairseq >= 1.0.0a')
logging.set_verbosity_info()
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[str] = 'Hello world! cécé herlolip'
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ):
SCREAMING_SNAKE_CASE__ : int = FairseqRobertaModel.from_pretrained(_snake_case )
roberta.eval() # disable dropout
SCREAMING_SNAKE_CASE__ : Any = roberta.model.encoder.sentence_encoder
SCREAMING_SNAKE_CASE__ : Any = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,hidden_size=roberta.cfg.model.encoder_embed_dim ,num_hidden_layers=roberta.cfg.model.encoder_layers ,num_attention_heads=roberta.cfg.model.encoder_attention_heads ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,max_position_embeddings=514 ,type_vocab_size=1 ,layer_norm_eps=1E-5 ,)
if classification_head:
SCREAMING_SNAKE_CASE__ : Dict = roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our RoBERTa config:""" ,_snake_case )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = XLMRobertaXLForSequenceClassification(_snake_case ) if classification_head else XLMRobertaXLForMaskedLM(_snake_case )
model.eval()
# Now let's copy all the weights.
# Embeddings
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_sent_encoder.embed_tokens.weight
SCREAMING_SNAKE_CASE__ : int = roberta_sent_encoder.embed_positions.weight
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_sent_encoder.layer_norm.weight
SCREAMING_SNAKE_CASE__ : Dict = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
SCREAMING_SNAKE_CASE__ : BertLayer = model.roberta.encoder.layer[i]
SCREAMING_SNAKE_CASE__ : TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
SCREAMING_SNAKE_CASE__ : RobertaAttention = layer.attention
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn_layer_norm.weight
SCREAMING_SNAKE_CASE__ : List[Any] = roberta_layer.self_attn_layer_norm.bias
# self attention
SCREAMING_SNAKE_CASE__ : BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn.q_proj.weight
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.self_attn.q_proj.bias
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.self_attn.k_proj.weight
SCREAMING_SNAKE_CASE__ : int = roberta_layer.self_attn.k_proj.bias
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn.v_proj.weight
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta_layer.self_attn.v_proj.bias
# self-attention output
SCREAMING_SNAKE_CASE__ : BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta_layer.self_attn.out_proj.weight
SCREAMING_SNAKE_CASE__ : List[str] = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.final_layer_norm.weight
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_layer.final_layer_norm.bias
# intermediate
SCREAMING_SNAKE_CASE__ : BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE__ : List[Any] = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.fca.bias
# output
SCREAMING_SNAKE_CASE__ : BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
SCREAMING_SNAKE_CASE__ : Tuple = roberta_layer.fca.weight
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta_layer.fca.bias
# end of layer
if classification_head:
SCREAMING_SNAKE_CASE__ : List[Any] = roberta.model.classification_heads["""mnli"""].dense.weight
SCREAMING_SNAKE_CASE__ : Optional[Any] = roberta.model.classification_heads["""mnli"""].dense.bias
SCREAMING_SNAKE_CASE__ : Optional[Any] = roberta.model.classification_heads["""mnli"""].out_proj.weight
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
SCREAMING_SNAKE_CASE__ : str = roberta.model.encoder.lm_head.dense.weight
SCREAMING_SNAKE_CASE__ : List[Any] = roberta.model.encoder.lm_head.dense.bias
SCREAMING_SNAKE_CASE__ : Union[str, Any] = roberta.model.encoder.lm_head.layer_norm.weight
SCREAMING_SNAKE_CASE__ : Dict = roberta.model.encoder.lm_head.layer_norm.bias
SCREAMING_SNAKE_CASE__ : Optional[int] = roberta.model.encoder.lm_head.weight
SCREAMING_SNAKE_CASE__ : List[str] = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
SCREAMING_SNAKE_CASE__ : torch.Tensor = roberta.encode(_snake_case ).unsqueeze(0 ) # batch of size 1
SCREAMING_SNAKE_CASE__ : Tuple = model(_snake_case )[0]
if classification_head:
SCREAMING_SNAKE_CASE__ : Dict = roberta.model.classification_heads["""mnli"""](roberta.extract_features(_snake_case ) )
else:
SCREAMING_SNAKE_CASE__ : Tuple = roberta.model(_snake_case )[0]
print(our_output.shape ,their_output.shape )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
SCREAMING_SNAKE_CASE__ : Tuple = torch.allclose(_snake_case ,_snake_case ,atol=1E-3 )
print("""Do both models output the same tensors?""" ,"""🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
pathlib.Path(_snake_case ).mkdir(parents=_snake_case ,exist_ok=_snake_case )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
if __name__ == "__main__":
UpperCAmelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
UpperCAmelCase__ : Any = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 25
| 0
|
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __lowerCamelCase ( __snake_case ):
def __init__( self , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=None , **lowerCamelCase ) -> Union[str, Any]:
snake_case_ = parent
snake_case_ = config_class
snake_case_ = has_text_modality
snake_case_ = kwargs
snake_case_ = common_properties
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case_ = self.config_class(**self.inputs_dict )
snake_case_ = (
["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
if self.common_properties is None
else self.common_properties
)
# Add common fields for text models
if self.has_text_modality:
common_properties.extend(["""vocab_size"""] )
# Test that config has the common properties as getters
for prop in common_properties:
self.parent.assertTrue(hasattr(lowerCamelCase , lowerCamelCase ) , msg=f'''`{prop}` does not exist''' )
# Test that config has the common properties as setter
for idx, name in enumerate(lowerCamelCase ):
try:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
self.parent.assertEqual(
getattr(lowerCamelCase , lowerCamelCase ) , lowerCamelCase , msg=f'''`{name} value {idx} expected, but was {getattr(lowerCamelCase , lowerCamelCase )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
# Test if config class can be called with Config(prop_name=..)
for idx, name in enumerate(lowerCamelCase ):
try:
snake_case_ = self.config_class(**{name: idx} )
self.parent.assertEqual(
getattr(lowerCamelCase , lowerCamelCase ) , lowerCamelCase , msg=f'''`{name} value {idx} expected, but was {getattr(lowerCamelCase , lowerCamelCase )}''' )
except NotImplementedError:
# Some models might not be able to implement setters for common_properties
# In that case, a NotImplementedError is raised
pass
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case_ = self.config_class(**self.inputs_dict )
snake_case_ = json.loads(config.to_json_string() )
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key] , lowerCamelCase )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case_ = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ = os.path.join(lowerCamelCase , """config.json""" )
config_first.to_json_file(lowerCamelCase )
snake_case_ = self.config_class.from_json_file(lowerCamelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case_ = self.config_class(**self.inputs_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
config_first.save_pretrained(lowerCamelCase )
snake_case_ = self.config_class.from_pretrained(lowerCamelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def lowerCAmelCase_ ( self ) -> Any:
snake_case_ = self.config_class(**self.inputs_dict )
snake_case_ = """test"""
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ = os.path.join(lowerCamelCase , lowerCamelCase )
config_first.save_pretrained(lowerCamelCase )
snake_case_ = self.config_class.from_pretrained(lowerCamelCase , subfolder=lowerCamelCase )
self.parent.assertEqual(config_second.to_dict() , config_first.to_dict() )
def lowerCAmelCase_ ( self ) -> Any:
snake_case_ = self.config_class(**self.inputs_dict , num_labels=5 )
self.parent.assertEqual(len(config.idalabel ) , 5 )
self.parent.assertEqual(len(config.labelaid ) , 5 )
snake_case_ = 3
self.parent.assertEqual(len(config.idalabel ) , 3 )
self.parent.assertEqual(len(config.labelaid ) , 3 )
def lowerCAmelCase_ ( self ) -> List[str]:
if self.config_class.is_composition:
return
snake_case_ = self.config_class()
self.parent.assertIsNotNone(lowerCamelCase )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case_ = copy.deepcopy(lowerCamelCase )
snake_case_ = self.config_class(**lowerCamelCase )
snake_case_ = []
for key, value in config_common_kwargs.items():
if key == "torch_dtype":
if not is_torch_available():
continue
else:
import torch
if config.torch_dtype != torch.floataa:
wrong_values.append(("""torch_dtype""", config.torch_dtype, torch.floataa) )
elif getattr(lowerCamelCase , lowerCamelCase ) != value:
wrong_values.append((key, getattr(lowerCamelCase , lowerCamelCase ), value) )
if len(lowerCamelCase ) > 0:
snake_case_ = """\n""".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
raise ValueError(f'''The following keys were not properly set in the config:\n{errors}''' )
def lowerCAmelCase_ ( self ) -> Tuple:
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
self.create_and_test_config_from_and_save_pretrained()
self.create_and_test_config_from_and_save_pretrained_subfolder()
self.create_and_test_config_with_num_labels()
self.check_config_can_be_init_without_params()
self.check_config_arguments_init()
| 368
|
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
snake_case_ = FlaxAutoModelForSeqaSeqLM.from_config(config=lowercase_ )
snake_case_ = checkpoints.load_tax_checkpoint(lowercase_ )
snake_case_ = """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
if config.model_type == "t5":
snake_case_ = """SelfAttention"""
if config.model_type == "longt5" and config.encoder_attention_type == "local":
snake_case_ = """LocalSelfAttention"""
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ = """TransientGlobalSelfAttention"""
else:
raise ValueError(
"""Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
""" attribute with a value from ['local', 'transient-global].""" )
# Encoder
for layer_index in range(config.num_layers ):
snake_case_ = f'''layers_{str(lowercase_ )}'''
# Self-Attention
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]
# Layer Normalization
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
if split_mlp_wi:
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
else:
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
# Layer Normalization
snake_case_ = tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
# Assigning
snake_case_ = flax_model.params["""encoder"""]["""block"""][str(lowercase_ )]["""layer"""]
snake_case_ = tax_attention_key
snake_case_ = tax_attention_out
snake_case_ = tax_attention_query
snake_case_ = tax_attention_value
snake_case_ = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ = tax_global_layer_norm
if split_mlp_wi:
snake_case_ = tax_mlp_wi_a
snake_case_ = tax_mlp_wi_a
else:
snake_case_ = tax_mlp_wi
snake_case_ = tax_mlp_wo
snake_case_ = tax_mlp_layer_norm
snake_case_ = flax_model_encoder_layer_block
# Only for layer 0:
snake_case_ = tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
snake_case_ = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
snake_case_ = tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
snake_case_ = tax_encoder_global_rel_embedding
# Assigning
snake_case_ = tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
snake_case_ = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
snake_case_ = f'''layers_{str(lowercase_ )}'''
# Self-Attention
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
# Layer Normalization
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
"""scale"""
]
# Encoder-Decoder-Attention
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
snake_case_ = tax_enc_dec_attention_module["""key"""]["""kernel"""]
snake_case_ = tax_enc_dec_attention_module["""out"""]["""kernel"""]
snake_case_ = tax_enc_dec_attention_module["""query"""]["""kernel"""]
snake_case_ = tax_enc_dec_attention_module["""value"""]["""kernel"""]
# Layer Normalization
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
# MLP
if split_mlp_wi:
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
else:
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
# Layer Normalization
snake_case_ = tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
# Assigning
snake_case_ = flax_model.params["""decoder"""]["""block"""][str(lowercase_ )]["""layer"""]
snake_case_ = tax_attention_key
snake_case_ = tax_attention_out
snake_case_ = tax_attention_query
snake_case_ = tax_attention_value
snake_case_ = tax_pre_attention_layer_norm
snake_case_ = tax_enc_dec_attention_key
snake_case_ = tax_enc_dec_attention_out
snake_case_ = tax_enc_dec_attention_query
snake_case_ = tax_enc_dec_attention_value
snake_case_ = tax_cross_layer_norm
if split_mlp_wi:
snake_case_ = tax_mlp_wi_a
snake_case_ = tax_mlp_wi_a
else:
snake_case_ = tax_mlp_wi
snake_case_ = tax_mlp_wo
snake_case_ = txa_mlp_layer_norm
snake_case_ = flax_model_decoder_layer_block
# Decoder Normalization
snake_case_ = tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
snake_case_ = txa_decoder_norm
# Only for layer 0:
snake_case_ = tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
snake_case_ = tax_decoder_rel_embedding
# Token Embeddings
snake_case_ = tax_model["""target"""]["""token_embedder"""]["""embedding"""]
snake_case_ = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
snake_case_ = tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
flax_model.save_pretrained(lowercase_ )
print("""T5X Model was sucessfully converted!""" )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
)
parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
parser.add_argument(
'''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
)
lowerCamelCase_ = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 34
| 0
|
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Any = logging.get_logger(__name__)
lowerCAmelCase__ : List[str] = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class __snake_case ( _lowerCamelCase ):
__lowerCamelCase = """xlm-prophetnet"""
__lowerCamelCase = ["""past_key_values"""]
__lowerCamelCase = {
"""num_attention_heads""": """num_encoder_attention_heads""",
}
def __init__( self , __UpperCamelCase = 0.1 , __UpperCamelCase = "gelu" , __UpperCamelCase = 30522 , __UpperCamelCase = 1024 , __UpperCamelCase = 4096 , __UpperCamelCase = 12 , __UpperCamelCase = 16 , __UpperCamelCase = 4096 , __UpperCamelCase = 12 , __UpperCamelCase = 16 , __UpperCamelCase = 0.1 , __UpperCamelCase = 0.1 , __UpperCamelCase = 512 , __UpperCamelCase = 0.0_2 , __UpperCamelCase = True , __UpperCamelCase = True , __UpperCamelCase = 0 , __UpperCamelCase = 2 , __UpperCamelCase = 32 , __UpperCamelCase = 128 , __UpperCamelCase = False , __UpperCamelCase = 0.0 , __UpperCamelCase = True , __UpperCamelCase = 0 , __UpperCamelCase = 1 , __UpperCamelCase = 2 , **__UpperCamelCase , ) -> Dict:
'''simple docstring'''
snake_case__ : Tuple = vocab_size
snake_case__ : Optional[Any] = hidden_size
snake_case__ : List[str] = encoder_ffn_dim
snake_case__ : List[str] = num_encoder_layers
snake_case__ : List[Any] = num_encoder_attention_heads
snake_case__ : Union[str, Any] = decoder_ffn_dim
snake_case__ : List[Any] = num_decoder_layers
snake_case__ : Union[str, Any] = num_decoder_attention_heads
snake_case__ : Tuple = max_position_embeddings
snake_case__ : Optional[int] = init_std # Normal(0, this parameter)
snake_case__ : List[str] = activation_function
# parameters for xlmprophetnet
snake_case__ : List[str] = ngram
snake_case__ : List[Any] = num_buckets
snake_case__ : str = relative_max_distance
snake_case__ : List[str] = disable_ngram_loss
snake_case__ : Tuple = eps
# 3 Types of Dropout
snake_case__ : str = attention_dropout
snake_case__ : Tuple = activation_dropout
snake_case__ : Optional[Any] = dropout
snake_case__ : Optional[int] = use_cache
super().__init__(
pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , is_encoder_decoder=__UpperCamelCase , add_cross_attention=__UpperCamelCase , decoder_start_token_id=__UpperCamelCase , **__UpperCamelCase , )
@property
def __a ( self ) -> int:
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def __a ( self , __UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
raise NotImplementedError(
'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
' `num_decoder_layers`.' )
| 143
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" ,"""False""" ) ) is not True ,reason="""Skipping test because should only be run when releasing minor transformers version""" ,)
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue_model_parallelism.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
] )
class __snake_case ( unittest.TestCase ):
def __a ( self ) -> Tuple:
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding='utf-8' , check=__UpperCamelCase , )
assert hasattr(self , 'env' )
def __a ( self , __UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
snake_case__ : Tuple = {
'enabled': True,
'processes_per_host': 8,
}
snake_case__ : Any = {
'enabled': True,
'parameters': {
'microbatches': 4,
'placement_strategy': 'spread',
'pipeline': 'interleaved',
'optimize': 'speed',
'partitions': 4,
'ddp': True,
},
}
snake_case__ : Optional[int] = {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}
snake_case__ : int = 'trainer' if self.script == 'run_glue.py' else 'smtrainer'
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=__UpperCamelCase , instance_type=self.instance_type , debugger_hook_config=__UpperCamelCase , hyperparameters={
**self.env.hyperparameters,
'model_name_or_path': self.model_name_or_path,
'max_steps': 500,
} , metric_definitions=self.env.metric_definitions , distribution=__UpperCamelCase , py_version='py36' , )
def __a ( self , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
TrainingJobAnalytics(__UpperCamelCase ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def __a ( self , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
snake_case__ : str = self.create_estimator(__UpperCamelCase )
# run training
estimator.fit()
# result dataframe
snake_case__ : Dict = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
snake_case__ : Any = list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value'] )
snake_case__ : List[str] = list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value'] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
snake_case__ : Any = (
Session().describe_training_job(estimator.latest_training_job.name ).get('TrainingTimeInSeconds' , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy )
assert all(t <= self.results['eval_loss'] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , 'w' ) as outfile:
json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss} , __UpperCamelCase )
| 143
| 1
|
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(lowerCamelCase_ ).count('''1''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 358
|
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Any=False , SCREAMING_SNAKE_CASE : Any=False ):
"""simple docstring"""
UpperCamelCase__ : str = '''backbone.''' if is_semantic else ''''''
UpperCamelCase__ : Union[str, Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
(F"{prefix}cls_token", '''beit.embeddings.cls_token'''),
(F"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
(F"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
(F"{prefix}pos_embed", '''beit.embeddings.position_embeddings'''),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
('''mask_token''', '''beit.embeddings.mask_token'''),
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any=False , SCREAMING_SNAKE_CASE : int=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
UpperCamelCase__ : Union[str, Any] = '''backbone.''' if is_semantic else ''''''
# queries, keys and values
UpperCamelCase__ : int = state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight" )
UpperCamelCase__ : List[str] = state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias" )
UpperCamelCase__ : Tuple = state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias" )
UpperCamelCase__ : List[str] = in_proj_weight[
: config.hidden_size, :
]
UpperCamelCase__ : Optional[int] = q_bias
UpperCamelCase__ : List[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase__ : Union[str, Any] = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
UpperCamelCase__ : List[Any] = state_dict.pop(F"{prefix}blocks.{i}.gamma_1" )
UpperCamelCase__ : List[str] = state_dict.pop(F"{prefix}blocks.{i}.gamma_2" )
UpperCamelCase__ : Any = gamma_a
UpperCamelCase__ : str = gamma_a
def _a ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = dct.pop(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Union[str, Any] = val
def _a ( ):
"""simple docstring"""
UpperCamelCase__ : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCamelCase__ : Optional[Any] = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def _a ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict=False ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = False if '''rvlcdip''' in checkpoint_url else True
UpperCamelCase__ : str = BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE , use_mask_token=SCREAMING_SNAKE_CASE )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
UpperCamelCase__ : List[str] = 1024
UpperCamelCase__ : Union[str, Any] = 4096
UpperCamelCase__ : Optional[int] = 24
UpperCamelCase__ : List[str] = 16
# labels
if "rvlcdip" in checkpoint_url:
UpperCamelCase__ : Any = 16
UpperCamelCase__ : Optional[int] = '''huggingface/label-files'''
UpperCamelCase__ : Union[str, Any] = '''rvlcdip-id2label.json'''
UpperCamelCase__ : Dict = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
UpperCamelCase__ : Optional[Any] = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
UpperCamelCase__ : int = idalabel
UpperCamelCase__ : Union[str, Any] = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
UpperCamelCase__ : Optional[int] = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE , map_location='''cpu''' )['''model''']
UpperCamelCase__ : str = create_rename_keys(SCREAMING_SNAKE_CASE , has_lm_head=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 , has_lm_head=SCREAMING_SNAKE_CASE )
# load HuggingFace model
UpperCamelCase__ : Tuple = BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE )
model.eval()
model.load_state_dict(SCREAMING_SNAKE_CASE )
# Check outputs on an image
UpperCamelCase__ : List[str] = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Optional[Any] = prepare_img()
UpperCamelCase__ : Union[str, Any] = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
UpperCamelCase__ : Union[str, Any] = encoding['''pixel_values''']
UpperCamelCase__ : str = model(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Any = outputs.logits
# verify logits
UpperCamelCase__ : Dict = [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE ), "Shape 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 image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE )
if push_to_hub:
if has_lm_head:
UpperCamelCase__ : Any = '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
else:
UpperCamelCase__ : Optional[Any] = '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
image_processor.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=SCREAMING_SNAKE_CASE , )
model.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=SCREAMING_SNAKE_CASE , )
if __name__ == "__main__":
__UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
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."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
__UpperCamelCase : Dict = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 51
| 0
|
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def a_ ( __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : str , __snake_case : Optional[int]=None , __snake_case : List[str]=None , __snake_case : Optional[Any]=None , __snake_case : Optional[int]=None , __snake_case : List[str]=None , ) -> int:
"""simple docstring"""
if attention_mask is None:
lowerCamelCase_ =input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCamelCase_ =decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCamelCase_ =torch.ones(config.encoder_layers , config.encoder_attention_heads , device=__snake_case )
if decoder_head_mask is None:
lowerCamelCase_ =torch.ones(config.decoder_layers , config.decoder_attention_heads , device=__snake_case )
if cross_attn_head_mask is None:
lowerCamelCase_ =torch.ones(config.decoder_layers , config.decoder_attention_heads , device=__snake_case )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class __UpperCamelCase :
def __init__( self, lowerCAmelCase, lowerCAmelCase=13, lowerCAmelCase=7, lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase=99, lowerCAmelCase=16, lowerCAmelCase=2, lowerCAmelCase=4, lowerCAmelCase=4, lowerCAmelCase="relu", lowerCAmelCase=0.1, lowerCAmelCase=0.1, lowerCAmelCase=0.0, lowerCAmelCase=0.0, lowerCAmelCase=20, lowerCAmelCase=2, lowerCAmelCase=1, lowerCAmelCase=0, ):
"""simple docstring"""
lowerCamelCase_ =parent
lowerCamelCase_ =batch_size
lowerCamelCase_ =seq_length
lowerCamelCase_ =is_training
lowerCamelCase_ =use_labels
lowerCamelCase_ =vocab_size
lowerCamelCase_ =hidden_size
lowerCamelCase_ =num_hidden_layers
lowerCamelCase_ =num_attention_heads
lowerCamelCase_ =intermediate_size
lowerCamelCase_ =hidden_act
lowerCamelCase_ =hidden_dropout_prob
lowerCamelCase_ =attention_probs_dropout_prob
lowerCamelCase_ =encoder_layerdrop
lowerCamelCase_ =decoder_layerdrop
lowerCamelCase_ =max_position_embeddings
lowerCamelCase_ =eos_token_id
lowerCamelCase_ =pad_token_id
lowerCamelCase_ =bos_token_id
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase_ =self.eos_token_id # Eos Token
lowerCamelCase_ =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCamelCase_ =input_ids.clamp(self.pad_token_id + 1 )
lowerCamelCase_ =decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCamelCase_ =self.get_config()
lowerCamelCase_ =prepare_mam_aaa_inputs_dict(lowerCAmelCase, lowerCAmelCase, lowerCAmelCase )
return config, inputs_dict
def lowercase__ ( self ):
"""simple docstring"""
return MaMaaaConfig(
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, encoder_layerdrop=self.encoder_layerdrop, decoder_layerdrop=self.decoder_layerdrop, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.prepare_config_and_inputs()
return config, inputs_dict
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =MaMaaaModel(config=lowerCAmelCase ).get_decoder().to(lowerCAmelCase ).eval()
lowerCamelCase_ =inputs_dict['''input_ids''']
lowerCamelCase_ =inputs_dict['''attention_mask''']
lowerCamelCase_ =inputs_dict['''head_mask''']
# first forward pass
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase, head_mask=lowerCAmelCase, use_cache=lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_ =outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
lowerCamelCase_ =ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase_ =ids_tensor((self.batch_size, 3), 2 )
# append to next input_ids and
lowerCamelCase_ =torch.cat([input_ids, next_tokens], dim=-1 )
lowerCamelCase_ =torch.cat([attention_mask, next_attn_mask], dim=-1 )
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase )['''last_hidden_state''']
lowerCamelCase_ =model(lowerCAmelCase, attention_mask=lowerCAmelCase, past_key_values=lowerCAmelCase )[
'''last_hidden_state'''
]
# select random slice
lowerCamelCase_ =ids_tensor((1,), output_from_past.shape[-1] ).item()
lowerCamelCase_ =output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCamelCase_ =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase, lowerCAmelCase, atol=1e-2 ) )
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =MaMaaaModel(config=lowerCAmelCase ).to(lowerCAmelCase ).eval()
lowerCamelCase_ =model(**lowerCAmelCase )
lowerCamelCase_ =outputs.encoder_last_hidden_state
lowerCamelCase_ =outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ =model.get_encoder()
encoder.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =MaMaaaEncoder.from_pretrained(lowerCAmelCase ).to(lowerCAmelCase )
lowerCamelCase_ =encoder(inputs_dict['''input_ids'''], attention_mask=inputs_dict['''attention_mask'''] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase_ =model.get_decoder()
decoder.save_pretrained(lowerCAmelCase )
lowerCamelCase_ =MaMaaaDecoder.from_pretrained(lowerCAmelCase ).to(lowerCAmelCase )
lowerCamelCase_ =decoder(
input_ids=inputs_dict['''decoder_input_ids'''], attention_mask=inputs_dict['''decoder_attention_mask'''], encoder_hidden_states=lowerCAmelCase, encoder_attention_mask=inputs_dict['''attention_mask'''], )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase : Union[str, Any] =(
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
lowercase : Dict =(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
lowercase : Optional[int] =(
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
lowercase : Dict =True
lowercase : Tuple =True
lowercase : Optional[Any] =False
lowercase : int =False
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase, lowerCAmelCase ):
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MaMaaaModelTester(self )
lowerCamelCase_ =ConfigTester(self, config_class=lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCamelCase_ =model_class(lowerCAmelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase )
lowerCamelCase_, lowerCamelCase_ =model_class.from_pretrained(lowerCAmelCase, output_loading_info=lowerCAmelCase )
self.assertEqual(info['''missing_keys'''], [] )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
lowerCamelCase_ =model_class(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
lowerCamelCase_ =copy.deepcopy(self._prepare_for_class(lowerCAmelCase, lowerCAmelCase ) )
if not self.is_encoder_decoder:
lowerCamelCase_ =inputs['''input_ids''']
del inputs["input_ids"]
else:
lowerCamelCase_ =inputs['''input_ids''']
lowerCamelCase_ =inputs.get('''decoder_input_ids''', lowerCAmelCase )
del inputs["input_ids"]
inputs.pop('''decoder_input_ids''', lowerCAmelCase )
lowerCamelCase_ =model.get_input_embeddings()
if not self.is_encoder_decoder:
lowerCamelCase_ =wte(lowerCAmelCase )
else:
lowerCamelCase_ =wte(lowerCAmelCase )
lowerCamelCase_ =wte(lowerCAmelCase )
with torch.no_grad():
model(**lowerCAmelCase )[0]
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_, lowerCamelCase_ =self.model_tester.prepare_config_and_inputs()
lowerCamelCase_ =input_dict['''input_ids''']
lowerCamelCase_ =input_ids.ne(1 ).to(lowerCAmelCase )
lowerCamelCase_ =MaMaaaForConditionalGeneration(lowerCAmelCase ).eval().to(lowerCAmelCase )
if torch_device == "cuda":
model.half()
model.generate(lowerCAmelCase, attention_mask=lowerCAmelCase )
model.generate(num_beams=4, do_sample=lowerCAmelCase, early_stopping=lowerCAmelCase, num_return_sequences=3 )
def a_ ( __snake_case : Tuple ) -> List[Any]:
"""simple docstring"""
return torch.tensor(__snake_case , dtype=torch.long , device=__snake_case )
a_ : int = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def lowercase__ ( self ):
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''' )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MaMaaaModel.from_pretrained('''facebook/m2m100_418M''' ).to(lowerCAmelCase )
lowerCamelCase_ =_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
lowerCamelCase_ =_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
lowerCamelCase_ =prepare_mam_aaa_inputs_dict(model.config, lowerCAmelCase, lowerCAmelCase )
with torch.no_grad():
lowerCamelCase_ =model(**lowerCAmelCase )[0]
lowerCamelCase_ =torch.Size((1, 11, 1_024) )
self.assertEqual(output.shape, lowerCAmelCase )
# change to expected output here
lowerCamelCase_ =torch.tensor(
[[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]], device=lowerCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3], lowerCAmelCase, atol=lowerCAmelCase ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''' ).to(lowerCAmelCase )
# change to intended input
lowerCamelCase_ =_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]] )
lowerCamelCase_ =_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]] )
lowerCamelCase_ =prepare_mam_aaa_inputs_dict(model.config, lowerCAmelCase, lowerCAmelCase )
with torch.no_grad():
lowerCamelCase_ =model(**lowerCAmelCase )[0]
lowerCamelCase_ =torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape, lowerCAmelCase )
# change to expected output here
lowerCamelCase_ =torch.tensor(
[[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]], device=lowerCAmelCase )
self.assertTrue(torch.allclose(output[:, :3, :3], lowerCAmelCase, atol=lowerCAmelCase ) )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M''' ).to(lowerCAmelCase )
lowerCamelCase_ =MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M''', src_lang='''fr''', tgt_lang='''en''' )
lowerCamelCase_ =[
'''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
'''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
'''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
lowerCamelCase_ =tokenizer(lowerCAmelCase, padding=lowerCAmelCase, return_tensors='''pt''' )
lowerCamelCase_ =model.generate(
input_ids=dct['''input_ids'''].to(lowerCAmelCase ), attention_mask=dct['''attention_mask'''].to(lowerCAmelCase ), num_beams=5, forced_bos_token_id=tokenizer.get_lang_id('''en''' ), )
lowerCamelCase_ =[
'''The NSA case highlights the total absence of intelligence debate''',
'''I think there are two levels of response from the French government.''',
'''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
''' communications in France.''',
]
lowerCamelCase_ =tokenizer.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
assert generated == expected_en
| 75
|
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A_ :
'''simple docstring'''
pass
| 61
| 0
|
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Dict = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
lowerCAmelCase_ :Tuple = 1_2_8
elif "12-12" in model_name:
lowerCAmelCase_ :Optional[Any] = 1_2
lowerCAmelCase_ :Dict = 1_2
elif "14-14" in model_name:
lowerCAmelCase_ :Any = 1_4
lowerCAmelCase_ :int = 1_4
elif "16-16" in model_name:
lowerCAmelCase_ :Optional[int] = 1_6
lowerCAmelCase_ :str = 1_6
else:
raise ValueError("""Model not supported""" )
lowerCAmelCase_ :Optional[Any] = """huggingface/label-files"""
if "speech-commands" in model_name:
lowerCAmelCase_ :Any = 3_5
lowerCAmelCase_ :str = """speech-commands-v2-id2label.json"""
else:
lowerCAmelCase_ :Any = 5_2_7
lowerCAmelCase_ :List[Any] = """audioset-id2label.json"""
lowerCAmelCase_ :Tuple = json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type="""dataset""" ) , """r""" ) )
lowerCAmelCase_ :int = {int(lowercase__ ): v for k, v in idalabel.items()}
lowerCAmelCase_ :List[str] = idalabel
lowerCAmelCase_ :str = {v: k for k, v in idalabel.items()}
return config
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
if "module.v" in name:
lowerCAmelCase_ :Optional[int] = name.replace("""module.v""" , """audio_spectrogram_transformer""" )
if "cls_token" in name:
lowerCAmelCase_ :Tuple = name.replace("""cls_token""" , """embeddings.cls_token""" )
if "dist_token" in name:
lowerCAmelCase_ :Tuple = name.replace("""dist_token""" , """embeddings.distillation_token""" )
if "pos_embed" in name:
lowerCAmelCase_ :Union[str, Any] = name.replace("""pos_embed""" , """embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
lowerCAmelCase_ :List[Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
# transformer blocks
if "blocks" in name:
lowerCAmelCase_ :List[str] = name.replace("""blocks""" , """encoder.layer""" )
if "attn.proj" in name:
lowerCAmelCase_ :Dict = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
lowerCAmelCase_ :str = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowerCAmelCase_ :str = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowerCAmelCase_ :Any = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowerCAmelCase_ :List[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowerCAmelCase_ :Tuple = name.replace("""mlp.fc2""" , """output.dense""" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
lowerCAmelCase_ :Any = name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" )
# classifier head
if "module.mlp_head.0" in name:
lowerCAmelCase_ :Tuple = name.replace("""module.mlp_head.0""" , """classifier.layernorm""" )
if "module.mlp_head.1" in name:
lowerCAmelCase_ :Tuple = name.replace("""module.mlp_head.1""" , """classifier.dense""" )
return name
def _snake_case ( lowercase__ : List[str] , lowercase__ : Dict ) -> List[str]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
lowerCAmelCase_ :Dict = orig_state_dict.pop(lowercase__ )
if "qkv" in key:
lowerCAmelCase_ :Union[str, Any] = key.split(""".""" )
lowerCAmelCase_ :Union[str, Any] = int(key_split[3] )
lowerCAmelCase_ :Optional[int] = config.hidden_size
if "weight" in key:
lowerCAmelCase_ :Optional[Any] = val[:dim, :]
lowerCAmelCase_ :Optional[Any] = val[dim : dim * 2, :]
lowerCAmelCase_ :str = val[-dim:, :]
else:
lowerCAmelCase_ :str = val[:dim]
lowerCAmelCase_ :str = val[dim : dim * 2]
lowerCAmelCase_ :Tuple = val[-dim:]
else:
lowerCAmelCase_ :Any = val
return orig_state_dict
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Any = [
"""module.v.head.weight""",
"""module.v.head.bias""",
"""module.v.head_dist.weight""",
"""module.v.head_dist.bias""",
]
for k in ignore_keys:
state_dict.pop(lowercase__ , lowercase__ )
@torch.no_grad()
def _snake_case ( lowercase__ : int , lowercase__ : List[str] , lowercase__ : int=False ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = get_audio_spectrogram_transformer_config(lowercase__ )
lowerCAmelCase_ :str = {
"""ast-finetuned-audioset-10-10-0.4593""": (
"""https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.450""": (
"""https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448""": (
"""https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
),
"""ast-finetuned-audioset-10-10-0.448-v2""": (
"""https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
),
"""ast-finetuned-audioset-12-12-0.447""": (
"""https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
),
"""ast-finetuned-audioset-14-14-0.443""": (
"""https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
),
"""ast-finetuned-audioset-16-16-0.442""": (
"""https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
),
"""ast-finetuned-speech-commands-v2""": (
"""https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
),
}
# load original state_dict
lowerCAmelCase_ :List[Any] = model_name_to_url[model_name]
lowerCAmelCase_ :int = torch.hub.load_state_dict_from_url(lowercase__ , map_location="""cpu""" )
# remove some keys
remove_keys(lowercase__ )
# rename some keys
lowerCAmelCase_ :List[Any] = convert_state_dict(lowercase__ , lowercase__ )
# load 🤗 model
lowerCAmelCase_ :int = ASTForAudioClassification(lowercase__ )
model.eval()
model.load_state_dict(lowercase__ )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
lowerCAmelCase_ :Dict = -4.2677393 if """speech-commands""" not in model_name else -6.845978
lowerCAmelCase_ :Union[str, Any] = 4.5689974 if """speech-commands""" not in model_name else 5.5654526
lowerCAmelCase_ :Optional[Any] = 1_0_2_4 if """speech-commands""" not in model_name else 1_2_8
lowerCAmelCase_ :str = ASTFeatureExtractor(mean=lowercase__ , std=lowercase__ , max_length=lowercase__ )
if "speech-commands" in model_name:
lowerCAmelCase_ :Any = load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" )
lowerCAmelCase_ :List[str] = dataset[0]["""audio"""]["""array"""]
else:
lowerCAmelCase_ :Any = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , )
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = torchaudio.load(lowercase__ )
lowerCAmelCase_ :Union[str, Any] = waveform.squeeze().numpy()
lowerCAmelCase_ :List[str] = feature_extractor(lowercase__ , sampling_rate=1_6_0_0_0 , return_tensors="""pt""" )
# forward pass
lowerCAmelCase_ :str = model(**lowercase__ )
lowerCAmelCase_ :Optional[int] = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
lowerCAmelCase_ :Dict = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
lowerCAmelCase_ :Optional[int] = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
lowerCAmelCase_ :Optional[int] = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
lowerCAmelCase_ :Union[str, Any] = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
lowerCAmelCase_ :List[Any] = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
lowerCAmelCase_ :List[str] = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
lowerCAmelCase_ :int = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
lowerCAmelCase_ :Any = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError("""Unknown model name""" )
if not torch.allclose(logits[0, :3] , lowercase__ , atol=1E-4 ):
raise ValueError("""Logits don't match""" )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase__ )
print(f"""Saving feature extractor to {pytorch_dump_folder_path}""" )
feature_extractor.save_pretrained(lowercase__ )
if push_to_hub:
print("""Pushing model and feature extractor to the hub...""" )
model.push_to_hub(f"""MIT/{model_name}""" )
feature_extractor.push_to_hub(f"""MIT/{model_name}""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
__UpperCAmelCase = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 1
|
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
# See all DETR models at https://huggingface.co/models?filter=detr
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "detr"
UpperCAmelCase_ :str = ["past_key_values"]
UpperCAmelCase_ :Tuple = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self , __A=True , __A=None , __A=3 , __A=100 , __A=6 , __A=2048 , __A=8 , __A=6 , __A=2048 , __A=8 , __A=0.0 , __A=0.0 , __A=True , __A="relu" , __A=256 , __A=0.1 , __A=0.0 , __A=0.0 , __A=0.0_2 , __A=1.0 , __A=False , __A="sine" , __A="resnet50" , __A=True , __A=False , __A=1 , __A=5 , __A=2 , __A=1 , __A=1 , __A=5 , __A=2 , __A=0.1 , **__A , ) -> List[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.""" )
lowerCAmelCase_ :int = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__A , __A ):
lowerCAmelCase_ :str = backbone_config.get("""model_type""" )
lowerCAmelCase_ :List[Any] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ :Optional[Any] = config_class.from_dict(__A )
# set timm attributes to None
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = None, None, None
lowerCAmelCase_ :Tuple = use_timm_backbone
lowerCAmelCase_ :Optional[int] = backbone_config
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :int = num_queries
lowerCAmelCase_ :List[Any] = d_model
lowerCAmelCase_ :Optional[int] = encoder_ffn_dim
lowerCAmelCase_ :Tuple = encoder_layers
lowerCAmelCase_ :int = encoder_attention_heads
lowerCAmelCase_ :Optional[Any] = decoder_ffn_dim
lowerCAmelCase_ :List[str] = decoder_layers
lowerCAmelCase_ :Dict = decoder_attention_heads
lowerCAmelCase_ :Dict = dropout
lowerCAmelCase_ :Tuple = attention_dropout
lowerCAmelCase_ :Union[str, Any] = activation_dropout
lowerCAmelCase_ :Any = activation_function
lowerCAmelCase_ :List[str] = init_std
lowerCAmelCase_ :Optional[int] = init_xavier_std
lowerCAmelCase_ :int = encoder_layerdrop
lowerCAmelCase_ :Union[str, Any] = decoder_layerdrop
lowerCAmelCase_ :List[str] = encoder_layers
lowerCAmelCase_ :Union[str, Any] = auxiliary_loss
lowerCAmelCase_ :str = position_embedding_type
lowerCAmelCase_ :List[Any] = backbone
lowerCAmelCase_ :str = use_pretrained_backbone
lowerCAmelCase_ :str = dilation
# Hungarian matcher
lowerCAmelCase_ :List[Any] = class_cost
lowerCAmelCase_ :Union[str, Any] = bbox_cost
lowerCAmelCase_ :Tuple = giou_cost
# Loss coefficients
lowerCAmelCase_ :Optional[int] = mask_loss_coefficient
lowerCAmelCase_ :Union[str, Any] = dice_loss_coefficient
lowerCAmelCase_ :Tuple = bbox_loss_coefficient
lowerCAmelCase_ :Tuple = giou_loss_coefficient
lowerCAmelCase_ :Dict = eos_coefficient
super().__init__(is_encoder_decoder=__A , **__A )
@property
def __lowerCAmelCase ( self ) -> int:
return self.encoder_attention_heads
@property
def __lowerCAmelCase ( self ) -> int:
return self.d_model
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> Any:
return cls(backbone_config=__A , **__A )
def __lowerCAmelCase ( self ) -> Dict[str, any]:
lowerCAmelCase_ :List[str] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
lowerCAmelCase_ :Dict = self.backbone_config.to_dict()
lowerCAmelCase_ :str = self.__class__.model_type
return output
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :List[Any] = 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"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-5
@property
def __lowerCAmelCase ( self ) -> int:
return 12
| 1
| 1
|
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
a__ : Optional[Any] = {"UserAgent": UserAgent().random}
def snake_case ( UpperCAmelCase )-> dict:
"""simple docstring"""
__A = script.contents[0]
__A = json.loads(data[data.find('{"config"' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class UpperCamelCase__ :
def __init__( self :Optional[Any] , _A :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
__A = F'https://www.instagram.com/{username}/'
__A = self.get_json()
def lowercase_ ( self :Union[str, Any] ) -> dict:
'''simple docstring'''
__A = requests.get(self.url , headers=_A ).text
__A = 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 :Union[str, Any] ) -> str:
'''simple docstring'''
return F'{self.__class__.__name__}(\'{self.username}\')'
def __str__( self :List[Any] ) -> str:
'''simple docstring'''
return F'{self.fullname} ({self.username}) is {self.biography}'
@property
def lowercase_ ( self :Optional[Any] ) -> str:
'''simple docstring'''
return self.user_data["username"]
@property
def lowercase_ ( self :str ) -> str:
'''simple docstring'''
return self.user_data["full_name"]
@property
def lowercase_ ( self :Union[str, Any] ) -> str:
'''simple docstring'''
return self.user_data["biography"]
@property
def lowercase_ ( self :str ) -> str:
'''simple docstring'''
return self.user_data["business_email"]
@property
def lowercase_ ( self :Tuple ) -> str:
'''simple docstring'''
return self.user_data["external_url"]
@property
def lowercase_ ( self :int ) -> int:
'''simple docstring'''
return self.user_data["edge_followed_by"]["count"]
@property
def lowercase_ ( self :List[Any] ) -> int:
'''simple docstring'''
return self.user_data["edge_follow"]["count"]
@property
def lowercase_ ( self :Tuple ) -> int:
'''simple docstring'''
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def lowercase_ ( self :Tuple ) -> str:
'''simple docstring'''
return self.user_data["profile_pic_url_hd"]
@property
def lowercase_ ( self :Dict ) -> bool:
'''simple docstring'''
return self.user_data["is_verified"]
@property
def lowercase_ ( self :Union[str, Any] ) -> bool:
'''simple docstring'''
return self.user_data["is_private"]
def snake_case ( UpperCAmelCase = "github" )-> None:
"""simple docstring"""
import os
if os.environ.get('CI' ):
return # test failing on GitHub Actions
__A = 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 > 1_5_0
assert instagram_user.number_of_followers > 1_2_0_0_0_0
assert instagram_user.number_of_followings > 1_5
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__ : List[str] = 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 = }''')
| 161
|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
a__ : Dict = logging.getLogger(__name__)
def snake_case ( UpperCAmelCase , UpperCAmelCase )-> Optional[int]:
"""simple docstring"""
return (preds == labels).mean()
@dataclass
class UpperCamelCase__ :
UpperCAmelCase__ : str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
UpperCAmelCase__ : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained config name or path if not the same as model_name'})
UpperCAmelCase__ : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
UpperCAmelCase__ : Optional[str] = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class UpperCamelCase__ :
UpperCAmelCase__ : str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys())})
UpperCAmelCase__ : str = field(metadata={'help': 'Should contain the data files for the task.'})
UpperCAmelCase__ : int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
UpperCAmelCase__ : bool = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Overwrite the cached training and evaluation sets'})
def snake_case ( )-> int:
"""simple docstring"""
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__A = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__A , __A , __A = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
' --overwrite_output_dir to overcome.' )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , UpperCAmelCase )
# Set seed
set_seed(training_args.seed )
try:
__A = processors[data_args.task_name]()
__A = processor.get_labels()
__A = len(UpperCAmelCase )
except KeyError:
raise ValueError('Task not found: %s' % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__A = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
__A = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
__A = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=UpperCAmelCase , cache_dir=model_args.cache_dir , )
# Get datasets
__A = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=UpperCAmelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
__A = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=UpperCAmelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(UpperCAmelCase ) -> Dict:
__A = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(UpperCAmelCase , p.label_ids )}
# Data collator
__A = DataCollatorWithPadding(UpperCAmelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
__A = Trainer(
model=UpperCAmelCase , args=UpperCAmelCase , train_dataset=UpperCAmelCase , eval_dataset=UpperCAmelCase , compute_metrics=UpperCAmelCase , data_collator=UpperCAmelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__A = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
__A = trainer.evaluate()
__A = os.path.join(training_args.output_dir , 'eval_results.txt' )
if trainer.is_world_master():
with open(UpperCAmelCase , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in result.items():
logger.info(' %s = %s' , UpperCAmelCase , UpperCAmelCase )
writer.write('%s = %s\n' % (key, value) )
results.update(UpperCAmelCase )
return results
def snake_case ( UpperCAmelCase )-> List[str]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
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"""simple docstring"""
from math import factorial
SCREAMING_SNAKE_CASE : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def __UpperCAmelCase ( snake_case_ : int ) -> int:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(snake_case_ ) )
def __UpperCAmelCase ( snake_case_ : int = 60 , snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not isinstance(snake_case_ , snake_case_ ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
_lowerCAmelCase = 0
# the cached sizes of the previous chains
_lowerCAmelCase = {}
for start_chain_element in range(1 , snake_case_ ):
# The temporary set will contain the elements of the chain
_lowerCAmelCase = set()
_lowerCAmelCase = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
_lowerCAmelCase = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(snake_case_ )
chain_set_length += 1
_lowerCAmelCase = digit_factorial_sum(snake_case_ )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
_lowerCAmelCase = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{solution()}')
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|
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = None
def __UpperCAmelCase ( ) -> TreeNode:
"""simple docstring"""
print("""\n********Press N to stop entering at any point of time********\n""" )
_lowerCAmelCase = input("""Enter the value of the root node: """ ).strip().lower()
_lowerCAmelCase = queue.Queue()
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
_lowerCAmelCase = F"""Enter the left node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = left_node
q.put(snake_case_ )
_lowerCAmelCase = F"""Enter the right node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = right_node
q.put(snake_case_ )
raise
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = []
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(snake_case_ )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(snake_case_ )
_lowerCAmelCase = n.left
# end of while means current node doesn't have left child
_lowerCAmelCase = stack.pop()
# start to traverse its right child
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n:
stack.append(snake_case_ )
_lowerCAmelCase = n.left
_lowerCAmelCase = stack.pop()
print(n.data , end=""",""" )
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase , _lowerCAmelCase = [], []
_lowerCAmelCase = node
stacka.append(snake_case_ )
while stacka: # to find the reversed order of post order, store it in stack2
_lowerCAmelCase = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(snake_case_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : str = "" , snake_case_ : int=50 , snake_case_ : Dict="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
_lowerCAmelCase , _lowerCAmelCase = divmod(width - len(snake_case_ ) - 2 , 2 )
return F"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
SCREAMING_SNAKE_CASE : TreeNode = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 5_0 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt())
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|
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__SCREAMING_SNAKE_CASE , int(b / 2 ) ) * actual_power(__SCREAMING_SNAKE_CASE , int(b / 2 ) )
else:
return a * actual_power(__SCREAMING_SNAKE_CASE , int(b / 2 ) ) * actual_power(__SCREAMING_SNAKE_CASE , int(b / 2 ) )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
if b < 0:
return 1 / actual_power(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
return actual_power(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(power(-2, -3))
| 195
|
from manim import *
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = Rectangle(height=0.5 , width=0.5 )
lowercase = Rectangle(height=0.25 , width=0.25 )
lowercase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
lowercase = [mem.copy() for i in range(6 )]
lowercase = [mem.copy() for i in range(6 )]
lowercase = VGroup(*snake_case ).arrange(snake_case , buff=0 )
lowercase = VGroup(*snake_case ).arrange(snake_case , buff=0 )
lowercase = VGroup(snake_case , snake_case ).arrange(snake_case , buff=0 )
lowercase = Text('CPU' , font_size=24 )
lowercase = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
cpu.move_to([-2.5, -0.5, 0] )
self.add(snake_case )
lowercase = [mem.copy() for i in range(4 )]
lowercase = VGroup(*snake_case ).arrange(snake_case , buff=0 )
lowercase = Text('GPU' , font_size=24 )
lowercase = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
gpu.move_to([-1, -1, 0] )
self.add(snake_case )
lowercase = [mem.copy() for i in range(6 )]
lowercase = VGroup(*snake_case ).arrange(snake_case , buff=0 )
lowercase = Text('Model' , font_size=24 )
lowercase = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
model.move_to([3, -1.0, 0] )
self.add(snake_case )
lowercase = []
lowercase = []
lowercase = []
for i, rect in enumerate(snake_case ):
rect.set_stroke(snake_case )
lowercase = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(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=snake_case )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=snake_case , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=snake_case , buff=0.0 )
self.add(snake_case )
model_cpu_arr.append(snake_case )
self.add(*snake_case , *snake_case , *snake_case )
lowercase = [mem.copy() for i in range(6 )]
lowercase = VGroup(*snake_case ).arrange(snake_case , buff=0 )
lowercase = Text('Loaded Checkpoint' , font_size=24 )
lowercase = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
checkpoint.move_to([3, 0.5, 0] )
self.add(snake_case )
lowercase = []
lowercase = []
for i, rect in enumerate(snake_case ):
lowercase = fill.copy().set_fill(snake_case , opacity=0.7 )
target.move_to(snake_case )
ckpt_arr.append(snake_case )
lowercase = 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(snake_case )
self.add(*snake_case , *snake_case )
lowercase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
lowercase = 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(snake_case , snake_case )
lowercase = MarkupText(
F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(snake_case , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(snake_case )
lowercase = 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] )
lowercase = [meta_mem.copy() for i in range(6 )]
lowercase = [meta_mem.copy() for i in range(6 )]
lowercase = VGroup(*snake_case ).arrange(snake_case , buff=0 )
lowercase = VGroup(*snake_case ).arrange(snake_case , buff=0 )
lowercase = VGroup(snake_case , snake_case ).arrange(snake_case , buff=0 )
lowercase = Text('Disk' , font_size=24 )
lowercase = Group(snake_case , snake_case ).arrange(snake_case , buff=0.5 , aligned_edge=snake_case )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(snake_case , run_time=3 ) , Write(snake_case , run_time=1 ) , Create(snake_case , run_time=1 ) )
lowercase = []
for i, rect in enumerate(snake_case ):
lowercase = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(snake_case , run_time=1.5 ) )
self.play(*snake_case )
self.play(FadeOut(snake_case ) )
lowercase = 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(snake_case , run_time=3 ) )
self.play(
FadeOut(snake_case , snake_case , *snake_case , *snake_case ) , )
self.wait()
| 195
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|
"""simple docstring"""
__UpperCamelCase = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 353
|
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase (SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : str ) -> Any:
# Load configuration defined in the metadata file
with open(SCREAMING_SNAKE_CASE_ ) as metadata_file:
SCREAMING_SNAKE_CASE = json.load(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = LukeConfig(use_entity_aware_attention=SCREAMING_SNAKE_CASE_ , **metadata['model_config'] )
# Load in the weights from the checkpoint_path
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location='cpu' )['module']
# Load the entity vocab file
SCREAMING_SNAKE_CASE = load_original_entity_vocab(SCREAMING_SNAKE_CASE_ )
# add an entry for [MASK2]
SCREAMING_SNAKE_CASE = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
SCREAMING_SNAKE_CASE = XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name'] )
# Add special tokens to the token vocabulary for downstream tasks
SCREAMING_SNAKE_CASE = AddedToken('<ent>' , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = AddedToken('<ent2>' , lstrip=SCREAMING_SNAKE_CASE_ , rstrip=SCREAMING_SNAKE_CASE_ )
tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ , 'tokenizer_config.json' ) , 'r' ) as f:
SCREAMING_SNAKE_CASE = json.load(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = 'MLukeTokenizer'
with open(os.path.join(SCREAMING_SNAKE_CASE_ , 'tokenizer_config.json' ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
with open(os.path.join(SCREAMING_SNAKE_CASE_ , MLukeTokenizer.vocab_files_names['entity_vocab_file'] ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Initialize the embeddings of the special tokens
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(['@'] )[0]
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(['#'] )[0]
SCREAMING_SNAKE_CASE = state_dict['embeddings.word_embeddings.weight']
SCREAMING_SNAKE_CASE = word_emb[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = word_emb[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
SCREAMING_SNAKE_CASE = state_dict[bias_name]
SCREAMING_SNAKE_CASE = decoder_bias[ent_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = decoder_bias[enta_init_index].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
SCREAMING_SNAKE_CASE = F'encoder.layer.{layer_index}.attention.self.'
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
SCREAMING_SNAKE_CASE = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
SCREAMING_SNAKE_CASE = state_dict['entity_embeddings.entity_embeddings.weight']
SCREAMING_SNAKE_CASE = entity_emb[entity_vocab['[MASK]']].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
SCREAMING_SNAKE_CASE = state_dict['entity_predictions.bias']
SCREAMING_SNAKE_CASE = entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0 )
SCREAMING_SNAKE_CASE = torch.cat([entity_prediction_bias, entity_mask_bias] )
SCREAMING_SNAKE_CASE = LukeForMaskedLM(config=SCREAMING_SNAKE_CASE_ ).eval()
state_dict.pop('entity_predictions.decoder.weight' )
state_dict.pop('lm_head.decoder.weight' )
state_dict.pop('lm_head.decoder.bias' )
SCREAMING_SNAKE_CASE = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith('lm_head' ) or key.startswith('entity_predictions' )):
SCREAMING_SNAKE_CASE = state_dict[key]
else:
SCREAMING_SNAKE_CASE = state_dict[key]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
if set(SCREAMING_SNAKE_CASE_ ) != {"luke.embeddings.position_ids"}:
raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}' )
if set(SCREAMING_SNAKE_CASE_ ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ , task='entity_classification' )
SCREAMING_SNAKE_CASE = 'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
SCREAMING_SNAKE_CASE = (0, 9)
SCREAMING_SNAKE_CASE = tokenizer(SCREAMING_SNAKE_CASE_ , entity_spans=[span] , return_tensors='pt' )
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE = torch.Size((1, 33, 7_68) )
SCREAMING_SNAKE_CASE = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
SCREAMING_SNAKE_CASE = torch.Size((1, 1, 7_68) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
F' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-4 ):
raise ValueError
# Verify masked word/entity prediction
SCREAMING_SNAKE_CASE = MLukeTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = 'Tokyo is the capital of <mask>.'
SCREAMING_SNAKE_CASE = (24, 30)
SCREAMING_SNAKE_CASE = tokenizer(SCREAMING_SNAKE_CASE_ , entity_spans=[span] , return_tensors='pt' )
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = encoding['input_ids'][0].tolist()
SCREAMING_SNAKE_CASE = input_ids.index(tokenizer.convert_tokens_to_ids('<mask>' ) )
SCREAMING_SNAKE_CASE = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.entity_logits[0][0].argmax().item()
SCREAMING_SNAKE_CASE = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith('en:' )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print('Saving PyTorch model to {}'.format(SCREAMING_SNAKE_CASE_ ) )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = ['[MASK]', '[PAD]', '[UNK]']
SCREAMING_SNAKE_CASE = [json.loads(SCREAMING_SNAKE_CASE_ ) for line in open(SCREAMING_SNAKE_CASE_ )]
SCREAMING_SNAKE_CASE = {}
for entry in data:
SCREAMING_SNAKE_CASE = entry['id']
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
SCREAMING_SNAKE_CASE = entity_id
break
SCREAMING_SNAKE_CASE = F'{language}:{entity_name}'
SCREAMING_SNAKE_CASE = entity_id
return new_mapping
if __name__ == "__main__":
__UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
parser.add_argument(
'''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
)
parser.add_argument(
'''--entity_vocab_path''',
default=None,
type=str,
help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
)
parser.add_argument(
'''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
)
__UpperCamelCase = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 38
| 0
|
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase__ ( a ):
"""simple docstring"""
lowerCAmelCase__ = (DPMSolverSDEScheduler,)
lowerCAmelCase__ = 10
def UpperCAmelCase__ ( self : str , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {
"""num_train_timesteps""": 1_100,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""noise_sampler_seed""": 0,
}
config.update(**__SCREAMING_SNAKE_CASE )
return config
def UpperCAmelCase__ ( self : str ) -> str:
"""simple docstring"""
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE , beta_end=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Dict ) -> List[str]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(self.num_inference_steps )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
__SCREAMING_SNAKE_CASE = sample.to(__SCREAMING_SNAKE_CASE )
for i, t in enumerate(scheduler.timesteps ):
__SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = output.prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47821044921875 ) < 1E-2
assert abs(result_mean.item() - 0.2178705964565277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59352111816406 ) < 1E-2
assert abs(result_mean.item() - 0.22342906892299652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def UpperCAmelCase__ ( self : List[str] ) -> Tuple:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config(prediction_type="""v_prediction""" )
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(self.num_inference_steps )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter * scheduler.init_noise_sigma
__SCREAMING_SNAKE_CASE = sample.to(__SCREAMING_SNAKE_CASE )
for i, t in enumerate(scheduler.timesteps ):
__SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = output.prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77149200439453 ) < 1E-2
assert abs(result_mean.item() - 0.16226289014816284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1663360595703 ) < 1E-2
assert abs(result_mean.item() - 0.16688326001167297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8487548828125 ) < 1E-2
assert abs(result_mean.item() - 0.1560530662536621 ) < 1E-3
def UpperCAmelCase__ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(self.num_inference_steps , device=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = output.prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46957397460938 ) < 1E-2
assert abs(result_mean.item() - 0.21805934607982635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59353637695312 ) < 1E-2
assert abs(result_mean.item() - 0.22342908382415771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52383422851562 ) < 1E-2
assert abs(result_mean.item() - 0.211619570851326 ) < 1E-3
def UpperCAmelCase__ ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE = scheduler_class(**__SCREAMING_SNAKE_CASE , use_karras_sigmas=__SCREAMING_SNAKE_CASE )
scheduler.set_timesteps(self.num_inference_steps , device=__SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = self.dummy_model()
__SCREAMING_SNAKE_CASE = self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE ) * scheduler.init_noise_sigma
__SCREAMING_SNAKE_CASE = sample.to(__SCREAMING_SNAKE_CASE )
for t in scheduler.timesteps:
__SCREAMING_SNAKE_CASE = scheduler.scale_model_input(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = model(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__SCREAMING_SNAKE_CASE = output.prev_sample
__SCREAMING_SNAKE_CASE = torch.sum(torch.abs(__SCREAMING_SNAKE_CASE ) )
__SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__SCREAMING_SNAKE_CASE ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66974135742188 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63653564453125 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3135223388672 ) < 1E-2
assert abs(result_mean.item() - 0.23003872730981811 ) < 1E-2
| 267
|
'''simple docstring'''
import numpy as np
def a__ ( a__ , a__ , a__ = 1E-1_2 , a__ = 1_00 , ):
"""simple docstring"""
assert np.shape(a__ )[0] == np.shape(a__ )[1]
# Ensure proper dimensionality.
assert np.shape(a__ )[0] == np.shape(a__ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(a__ ) == np.iscomplexobj(a__ )
__SCREAMING_SNAKE_CASE = np.iscomplexobj(a__ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(a__ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1E1_2
while not convergence:
# Multiple matrix by the vector.
__SCREAMING_SNAKE_CASE = np.dot(a__ , a__ )
# Normalize the resulting output vector.
__SCREAMING_SNAKE_CASE = w / np.linalg.norm(a__ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
__SCREAMING_SNAKE_CASE = vector.conj().T if is_complex else vector.T
__SCREAMING_SNAKE_CASE = np.dot(a__ , np.dot(a__ , a__ ) )
# Check convergence.
__SCREAMING_SNAKE_CASE = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = lambda_
if is_complex:
__SCREAMING_SNAKE_CASE = np.real(lambda_ )
return lambda_, vector
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
__SCREAMING_SNAKE_CASE = np.array([41, 4, 20] )
__SCREAMING_SNAKE_CASE = real_input_matrix.astype(np.complexaaa )
__SCREAMING_SNAKE_CASE = np.triu(1J * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
__SCREAMING_SNAKE_CASE = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
__SCREAMING_SNAKE_CASE = real_input_matrix
__SCREAMING_SNAKE_CASE = real_vector
elif problem_type == "complex":
__SCREAMING_SNAKE_CASE = complex_input_matrix
__SCREAMING_SNAKE_CASE = complex_vector
# Our implementation.
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = power_iteration(a__ , a__ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = np.linalg.eigh(a__ )
# Last eigenvalue is the maximum one.
__SCREAMING_SNAKE_CASE = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
__SCREAMING_SNAKE_CASE = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(a__ ) - np.abs(a__ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 267
| 1
|
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def lowerCamelCase__ ( _A , _A , _A=None , **_A ):
a : Optional[Any] = [x.strip() for x in open(_A ).readlines()]
a : Dict = [x.strip() for x in open(_A ).readlines()][: len(_A )]
a : Optional[Any] = calculate_rouge(_A , _A , **_A )
if save_path is not None:
save_json(_A , _A , indent=_A )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 351
|
'''simple docstring'''
from math import factorial, pi
def lowerCamelCase__ ( _A , _A = 30 ):
if not isinstance(_A , (int, float) ):
raise ValueError('maclaurin_sin() requires either an int or float for theta' )
if not isinstance(_A , _A ) or accuracy <= 0:
raise ValueError('maclaurin_sin() requires a positive int for accuracy' )
a : Dict = float(_A )
a : List[Any] = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(_A ) )
def lowerCamelCase__ ( _A , _A = 30 ):
if not isinstance(_A , (int, float) ):
raise ValueError('maclaurin_cos() requires either an int or float for theta' )
if not isinstance(_A , _A ) or accuracy <= 0:
raise ValueError('maclaurin_cos() requires a positive int for accuracy' )
a : int = float(_A )
a : str = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(_A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(1_0))
print(maclaurin_sin(-1_0))
print(maclaurin_sin(1_0, 1_5))
print(maclaurin_sin(-1_0, 1_5))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(1_0, 1_5))
print(maclaurin_cos(-1_0, 1_5))
| 96
| 0
|
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()
_snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : str = []
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_ ( snake_case_,snake_case_ ):
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
_A : Tuple = state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight''' )
_A : Tuple = in_proj_weight[
: encoder_config.hidden_size, :
]
_A : Optional[int] = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
_A : Dict = in_proj_weight[
-encoder_config.hidden_size :, :
]
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : List[Any] = dct.pop(snake_case_ )
_A : Dict = val
def lowerCAmelCase_ ( snake_case_ ):
if "handwritten" in checkpoint_url:
_A : Union[str, 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:
_A : Dict = """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
_A : Union[str, Any] = Image.open(requests.get(snake_case_,stream=snake_case_ ).raw ).convert("""RGB""" )
return im
@torch.no_grad()
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : Any = ViTConfig(image_size=384,qkv_bias=snake_case_ )
_A : Tuple = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
_A : int = 768
elif "large" in checkpoint_url:
# use ViT-large encoder
_A : int = 1024
_A : Tuple = 4096
_A : Union[str, Any] = 24
_A : str = 16
_A : int = 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:
_A : Optional[Any] = False
_A : Union[str, Any] = """relu"""
_A : int = 1024
_A : Union[str, Any] = True
_A : Union[str, Any] = False
_A : Optional[Any] = False
# load HuggingFace model
_A : List[str] = ViTModel(snake_case_,add_pooling_layer=snake_case_ )
_A : List[str] = TrOCRForCausalLM(snake_case_ )
_A : Any = VisionEncoderDecoderModel(encoder=snake_case_,decoder=snake_case_ )
model.eval()
# load state_dict of original model, rename some keys
_A : Dict = torch.hub.load_state_dict_from_url(snake_case_,map_location="""cpu""",check_hash=snake_case_ )["""model"""]
_A : Optional[int] = create_rename_keys(snake_case_,snake_case_ )
for src, dest in rename_keys:
rename_key(snake_case_,snake_case_,snake_case_ )
read_in_q_k_v(snake_case_,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():
_A : List[str] = state_dict.pop(snake_case_ )
if key.startswith("""decoder""" ) and "output_projection" not in key:
_A : Optional[int] = val
else:
_A : Dict = val
# load state dict
model.load_state_dict(snake_case_ )
# Check outputs on an image
_A : List[Any] = ViTImageProcessor(size=encoder_config.image_size )
_A : List[str] = RobertaTokenizer.from_pretrained("""roberta-large""" )
_A : Dict = TrOCRProcessor(snake_case_,snake_case_ )
_A : Tuple = processor(images=prepare_img(snake_case_ ),return_tensors="""pt""" ).pixel_values
# verify logits
_A : Dict = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
_A : Optional[int] = model(pixel_values=snake_case_,decoder_input_ids=snake_case_ )
_A : int = outputs.logits
_A : Optional[int] = torch.Size([1, 1, 50265] )
if "trocr-base-handwritten" in checkpoint_url:
_A : Dict = torch.tensor(
[-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11] )
elif "trocr-large-handwritten" in checkpoint_url:
_A : Tuple = torch.tensor(
[-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70] )
elif "trocr-base-printed" in checkpoint_url:
_A : int = torch.tensor(
[-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10] )
elif "trocr-large-printed" in checkpoint_url:
_A : Optional[int] = torch.tensor(
[-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :10],snake_case_,atol=1e-3 ), "First elements of logits not as expected"
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(snake_case_ )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(snake_case_ )
if __name__ == "__main__":
_snake_case = 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."
)
_snake_case = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 26
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",UpperCamelCase__,)
class lowercase ( UpperCamelCase__ ):
_a = RobertaConfig
_a = "roberta"
def __init__( self , _a ) -> Optional[int]:
super().__init__(_a )
_A : Union[str, Any] = RobertaEmbeddings(_a )
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ",UpperCamelCase__,)
class lowercase ( UpperCamelCase__ ):
_a = RobertaConfig
_a = "roberta"
def __init__( self , _a ) -> str:
super().__init__(_a )
_A : Any = config.num_labels
_A : Dict = config.num_hidden_layers
_A : List[str] = DeeRobertaModel(_a )
_A : int = nn.Dropout(config.hidden_dropout_prob )
_A : int = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(_a )
def a__ ( self , _a=None , _a=None , _a=None , _a=None , _a=None , _a=None , _a=None , _a=-1 , _a=False , ) -> Any:
_A : Optional[int] = self.num_layers
try:
_A : List[str] = self.roberta(
_a , attention_mask=_a , token_type_ids=_a , position_ids=_a , head_mask=_a , inputs_embeds=_a , )
_A : List[str] = outputs[1]
_A : List[str] = self.dropout(_a )
_A : Optional[Any] = self.classifier(_a )
_A : List[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_A : List[Any] = e.message
_A : Optional[int] = e.exit_layer
_A : Optional[int] = outputs[0]
if not self.training:
_A : int = entropy(_a )
_A : int = []
_A : int = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_A : Union[str, Any] = MSELoss()
_A : Tuple = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_A : List[Any] = CrossEntropyLoss()
_A : Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_A : Optional[Any] = []
for highway_exit in outputs[-1]:
_A : Tuple = highway_exit[0]
if not self.training:
highway_logits_all.append(_a )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_A : List[str] = MSELoss()
_A : Optional[int] = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_A : List[Any] = CrossEntropyLoss()
_A : Tuple = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(_a )
if train_highway:
_A : Dict = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_A : int = (loss,) + outputs
if not self.training:
_A : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_A : Union[str, Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 26
| 1
|
"""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 : Tuple = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
__A : int = 25_0004
__A : Tuple = 25_0020
@require_sentencepiece
@require_tokenizers
class __A ( lowerCAmelCase , unittest.TestCase ):
lowerCAmelCase_ : List[str] = MBartTokenizer
lowerCAmelCase_ : List[Any] = MBartTokenizerFast
lowerCAmelCase_ : int = True
lowerCAmelCase_ : Tuple = True
def lowercase__ ( self : int ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase : Any = MBartTokenizer(UpperCAmelCase_ , keep_accents=UpperCAmelCase_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self : List[Any] ):
lowerCAmelCase : Dict = MBartTokenizer(UpperCAmelCase_ , keep_accents=UpperCAmelCase_ )
lowerCAmelCase : List[str] = tokenizer.tokenize('This is a test' )
self.assertListEqual(UpperCAmelCase_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCAmelCase_ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCAmelCase : Dict = 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',
'.',
] , )
lowerCAmelCase : Dict = tokenizer.convert_tokens_to_ids(UpperCAmelCase_ )
self.assertListEqual(
UpperCAmelCase_ , [
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 : 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>',
'.',
] , )
def lowercase__ ( self : Tuple ):
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 : str = (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 : int = self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
lowerCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(UpperCAmelCase_ , **UpperCAmelCase_ )
lowerCAmelCase : int = tempfile.mkdtemp()
lowerCAmelCase : Tuple = tokenizer_r.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase : Tuple = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# 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 : List[Any] = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(UpperCAmelCase_ , UpperCAmelCase_ )
# Checks everything loads correctly in the same way
lowerCAmelCase : List[str] = tokenizer_r.from_pretrained(UpperCAmelCase_ )
lowerCAmelCase : List[Any] = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCAmelCase_ )
# Save tokenizer rust, legacy_format=True
lowerCAmelCase : List[Any] = tempfile.mkdtemp()
lowerCAmelCase : int = tokenizer_r.save_pretrained(UpperCAmelCase_ , legacy_format=UpperCAmelCase_ )
lowerCAmelCase : Dict = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCAmelCase_ , UpperCAmelCase_ )
# Checks everything loads correctly in the same way
lowerCAmelCase : List[str] = tokenizer_r.from_pretrained(UpperCAmelCase_ )
lowerCAmelCase : List[str] = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
shutil.rmtree(UpperCAmelCase_ )
# Save tokenizer rust, legacy_format=False
lowerCAmelCase : Optional[Any] = tempfile.mkdtemp()
lowerCAmelCase : Union[str, Any] = tokenizer_r.save_pretrained(UpperCAmelCase_ , legacy_format=UpperCAmelCase_ )
lowerCAmelCase : Tuple = tokenizer_p.save_pretrained(UpperCAmelCase_ )
# 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 : Optional[int] = tokenizer_r.from_pretrained(UpperCAmelCase_ )
lowerCAmelCase : List[Any] = tokenizer_p.from_pretrained(UpperCAmelCase_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCAmelCase_ , UpperCAmelCase_ ) )
shutil.rmtree(UpperCAmelCase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __A ( unittest.TestCase ):
lowerCAmelCase_ : str = "facebook/mbart-large-en-ro"
lowerCAmelCase_ : List[Any] = [
" 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.",
]
lowerCAmelCase_ : Any = [
"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.",
]
lowerCAmelCase_ : List[Any] = [8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]
@classmethod
def lowercase__ ( cls : int ):
lowerCAmelCase : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en_XX' , tgt_lang='ro_RO' )
lowerCAmelCase : int = 1
return cls
def lowercase__ ( self : str ):
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 lowercase__ ( self : str ):
lowerCAmelCase : str = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase_ )
def lowercase__ ( self : int ):
self.assertIn(UpperCAmelCase_ , self.tokenizer.all_special_ids )
lowerCAmelCase : Optional[int] = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
lowerCAmelCase : List[str] = self.tokenizer.decode(UpperCAmelCase_ , skip_special_tokens=UpperCAmelCase_ )
lowerCAmelCase : str = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=UpperCAmelCase_ )
self.assertEqual(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertNotIn(self.tokenizer.eos_token , UpperCAmelCase_ )
def lowercase__ ( self : List[str] ):
lowerCAmelCase : int = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , UpperCAmelCase_ )
lowerCAmelCase : Any = 10
lowerCAmelCase : str = self.tokenizer(UpperCAmelCase_ , max_length=UpperCAmelCase_ , truncation=UpperCAmelCase_ ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , UpperCAmelCase_ )
self.assertEqual(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
def lowercase__ ( self : Optional[Any] ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [250026, 250001] )
def lowercase__ ( self : Optional[Any] ):
lowerCAmelCase : Any = tempfile.mkdtemp()
lowerCAmelCase : Union[str, Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = MBartTokenizer.from_pretrained(UpperCAmelCase_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , UpperCAmelCase_ )
@require_torch
def lowercase__ ( self : Optional[int] ):
lowerCAmelCase : int = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase_ , return_tensors='pt' )
lowerCAmelCase : str = 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 lowercase__ ( self : Any ):
lowerCAmelCase : Tuple = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
lowerCAmelCase : Optional[int] = shift_tokens_right(batch['labels'] , self.tokenizer.pad_token_id )
self.assertIsInstance(UpperCAmelCase_ , UpperCAmelCase_ )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCAmelCase : Optional[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , UpperCAmelCase_ )
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 lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Optional[int] = self.tokenizer(self.src_text , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , max_length=3 , return_tensors='pt' )
lowerCAmelCase : Any = self.tokenizer(
text_target=self.tgt_text , padding=UpperCAmelCase_ , truncation=UpperCAmelCase_ , max_length=10 , return_tensors='pt' )
lowerCAmelCase : List[Any] = targets['input_ids']
lowerCAmelCase : List[str] = shift_tokens_right(UpperCAmelCase_ , 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 lowercase__ ( self : str ):
lowerCAmelCase : Tuple = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='en_XX' , tgt_lang='ar_AR' )
self.assertEqual(
nested_simplify(UpperCAmelCase_ ) , {
# A, test, EOS, en_XX
'input_ids': [[62, 3034, 2, 250004]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 250001,
} , )
| 366
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] = {
'''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
),
'''microsoft/deberta-v2-xxlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
),
}
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Union[str, Any] = "deberta-v2"
def __init__( self : int , UpperCAmelCase_ : Dict=128100 , UpperCAmelCase_ : Optional[int]=1536 , UpperCAmelCase_ : Tuple=24 , UpperCAmelCase_ : Any=24 , UpperCAmelCase_ : Any=6144 , UpperCAmelCase_ : Tuple="gelu" , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : List[Any]=512 , UpperCAmelCase_ : List[str]=0 , UpperCAmelCase_ : List[Any]=0.02 , UpperCAmelCase_ : Optional[int]=1E-7 , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Dict=-1 , UpperCAmelCase_ : Tuple=0 , UpperCAmelCase_ : int=True , UpperCAmelCase_ : int=None , UpperCAmelCase_ : List[Any]=0 , UpperCAmelCase_ : int="gelu" , **UpperCAmelCase_ : int , ):
super().__init__(**UpperCAmelCase_ )
lowerCAmelCase : Dict = hidden_size
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : str = num_attention_heads
lowerCAmelCase : List[str] = intermediate_size
lowerCAmelCase : List[Any] = hidden_act
lowerCAmelCase : int = hidden_dropout_prob
lowerCAmelCase : int = attention_probs_dropout_prob
lowerCAmelCase : Any = max_position_embeddings
lowerCAmelCase : str = type_vocab_size
lowerCAmelCase : Union[str, Any] = initializer_range
lowerCAmelCase : Union[str, Any] = relative_attention
lowerCAmelCase : List[Any] = max_relative_positions
lowerCAmelCase : List[Any] = pad_token_id
lowerCAmelCase : Optional[Any] = position_biased_input
# Backwards compatibility
if type(UpperCAmelCase_ ) == str:
lowerCAmelCase : Tuple = [x.strip() for x in pos_att_type.lower().split('|' )]
lowerCAmelCase : str = pos_att_type
lowerCAmelCase : Dict = vocab_size
lowerCAmelCase : Optional[Any] = layer_norm_eps
lowerCAmelCase : str = kwargs.get('pooler_hidden_size' , UpperCAmelCase_ )
lowerCAmelCase : Tuple = pooler_dropout
lowerCAmelCase : Union[str, Any] = pooler_hidden_act
class __A ( lowerCAmelCase ):
@property
def lowercase__ ( self : Optional[Any] ):
if self.task == "multiple-choice":
lowerCAmelCase : Tuple = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase : Union[str, Any] = {0: 'batch', 1: 'sequence'}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
else:
return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )
@property
def lowercase__ ( self : int ):
return 12
def lowercase__ ( self : Union[str, Any] , UpperCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional["TensorType"] = None , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : int = 40 , UpperCAmelCase_ : int = 40 , UpperCAmelCase_ : "PreTrainedTokenizerBase" = None , ):
lowerCAmelCase : List[str] = super().generate_dummy_inputs(preprocessor=UpperCAmelCase_ , framework=UpperCAmelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 323
| 0
|
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase__ (lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : Any = PhobertTokenizer
lowerCamelCase_ : List[str] = False
def _lowercase ( self ) -> Dict:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase : Union[str, Any] = ["T@@", "i", "I", "R@@", "r", "e@@"]
lowerCamelCase : Optional[Any] = dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
lowerCamelCase : Union[str, Any] = ["#version: 0.2", "l à</w>"]
lowerCamelCase : Dict = {"unk_token": "<unk>"}
lowerCamelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
lowerCamelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
for token in vocab_tokens:
fp.write(F'''{token} {vocab_tokens[token]}\n''' )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCamelCase__ ) )
def _lowercase ( self , **UpperCamelCase__ ) -> Tuple:
kwargs.update(self.special_tokens_map )
return PhobertTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ ) -> List[str]:
lowerCamelCase : Union[str, Any] = "Tôi là VinAI Research"
lowerCamelCase : List[Any] = "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
return input_text, output_text
def _lowercase ( self ) -> Tuple:
lowerCamelCase : Optional[int] = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCamelCase : Any = "Tôi là VinAI Research"
lowerCamelCase : str = "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
lowerCamelCase : Tuple = tokenizer.tokenize(UpperCamelCase__ )
print(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Optional[int] = tokens + [tokenizer.unk_token]
lowerCamelCase : Any = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , UpperCamelCase__ )
| 48
|
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
A__ : Optional[int] = logging.get_logger(__name__)
A__ : List[Any] = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
"""self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
"""self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
A__ : Tuple = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] ) -> Tuple:
for attribute in key.split('.' ):
__lowerCamelCase : List[Any] = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
if weight_type is not None:
__lowerCamelCase : Any = getattr(UpperCAmelCase_ , UpperCAmelCase_ ).shape
else:
__lowerCamelCase : Any = hf_pointer.shape
assert hf_shape == value.shape, (
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}'
)
if weight_type == "weight":
__lowerCamelCase : Tuple = value
elif weight_type == "weight_g":
__lowerCamelCase : Optional[int] = value
elif weight_type == "weight_v":
__lowerCamelCase : str = value
elif weight_type == "bias":
__lowerCamelCase : List[Any] = value
else:
__lowerCamelCase : List[str] = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union[str, Any] ) -> Optional[int]:
__lowerCamelCase : Optional[Any] = []
__lowerCamelCase : Dict = fairseq_model.state_dict()
__lowerCamelCase : List[str] = hf_model.feature_extractor
for name, value in fairseq_dict.items():
__lowerCamelCase : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCamelCase : str = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
__lowerCamelCase : int = True
if "*" in mapped_key:
__lowerCamelCase : Optional[int] = name.split(UpperCAmelCase_ )[0].split('.' )[-2]
__lowerCamelCase : List[str] = mapped_key.replace('*' , UpperCAmelCase_ )
if "weight_g" in name:
__lowerCamelCase : Dict = 'weight_g'
elif "weight_v" in name:
__lowerCamelCase : Any = 'weight_v'
elif "bias" in name and "relative_attention_bias" not in name:
__lowerCamelCase : Any = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCamelCase : List[Any] = 'weight'
else:
__lowerCamelCase : str = 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 UpperCAmelCase__ ( UpperCAmelCase_ : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any] ) -> Tuple:
__lowerCamelCase : List[str] = full_name.split('conv_layers.' )[-1]
__lowerCamelCase : List[Any] = name.split('.' )
__lowerCamelCase : Any = int(items[0] )
__lowerCamelCase : Optional[Any] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
__lowerCamelCase : Union[str, Any] = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
__lowerCamelCase : Tuple = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
__lowerCamelCase : str = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
__lowerCamelCase : List[Any] = 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 UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any]=None ) -> Optional[int]:
# load the pre-trained checkpoints
__lowerCamelCase : Optional[Any] = torch.load(UpperCAmelCase_ )
__lowerCamelCase : Optional[Any] = WavLMConfigOrig(checkpoint['cfg'] )
__lowerCamelCase : Union[str, Any] = WavLMOrig(UpperCAmelCase_ )
model.load_state_dict(checkpoint['model'] )
model.eval()
if config_path is not None:
__lowerCamelCase : Optional[int] = WavLMConfig.from_pretrained(UpperCAmelCase_ )
else:
__lowerCamelCase : Any = WavLMConfig()
__lowerCamelCase : Optional[int] = WavLMModel(UpperCAmelCase_ )
recursively_load_weights(UpperCAmelCase_ , UpperCAmelCase_ )
hf_wavlm.save_pretrained(UpperCAmelCase_ )
if __name__ == "__main__":
A__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
A__ : List[str] = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 185
| 0
|
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A( UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = OpenAIGPTTokenizer
UpperCamelCase = OpenAIGPTTokenizerFast
UpperCamelCase = True
UpperCamelCase = False
def a__ ( self : str ) -> Any:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'w</w>',
'r</w>',
't</w>',
'lo',
'low',
'er</w>',
'low</w>',
'lowest</w>',
'newer</w>',
'wider</w>',
'<unk>',
]
lowerCamelCase_ = dict(zip(_snake_case , range(len(_snake_case ) ) ) )
lowerCamelCase_ = ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' ) as fp:
fp.write(json.dumps(_snake_case ) )
with open(self.merges_file , 'w' ) as fp:
fp.write('\n'.join(_snake_case ) )
def a__ ( self : Optional[int] , A_ : List[Any] ) -> List[str]:
"""simple docstring"""
return "lower newer", "lower newer"
def a__ ( self : int ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = OpenAIGPTTokenizer(self.vocab_file , self.merges_file )
lowerCamelCase_ = 'lower'
lowerCamelCase_ = ['low', 'er</w>']
lowerCamelCase_ = tokenizer.tokenize(_snake_case )
self.assertListEqual(_snake_case , _snake_case )
lowerCamelCase_ = tokens + ['<unk>']
lowerCamelCase_ = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ) , _snake_case )
def a__ ( self : int , A_ : List[Any]=15 ) -> Dict:
"""simple docstring"""
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(_snake_case , **_snake_case )
# Simple input
lowerCamelCase_ = 'This is a simple input'
lowerCamelCase_ = ['This is a simple input 1', 'This is a simple input 2']
lowerCamelCase_ = ('This is a simple input', 'This is a pair')
lowerCamelCase_ = [
('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
self.assertRaises(_snake_case , tokenizer_r.encode , _snake_case , max_length=_snake_case , padding='max_length' )
# Simple input
self.assertRaises(_snake_case , tokenizer_r.encode_plus , _snake_case , max_length=_snake_case , padding='max_length' )
# Simple input
self.assertRaises(
_snake_case , tokenizer_r.batch_encode_plus , _snake_case , max_length=_snake_case , padding='max_length' , )
# Pair input
self.assertRaises(_snake_case , tokenizer_r.encode , _snake_case , max_length=_snake_case , padding='max_length' )
# Pair input
self.assertRaises(_snake_case , tokenizer_r.encode_plus , _snake_case , max_length=_snake_case , padding='max_length' )
# Pair input
self.assertRaises(
_snake_case , tokenizer_r.batch_encode_plus , _snake_case , max_length=_snake_case , padding='max_length' , )
def a__ ( self : Tuple ) -> List[str]:
"""simple docstring"""
pass
@require_ftfy
@require_spacy
@require_tokenizers
class A( UpperCamelCase ):
'''simple docstring'''
pass
| 357
|
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 208
| 0
|
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""b0""": efficientnet.EfficientNetBa,
"""b1""": efficientnet.EfficientNetBa,
"""b2""": efficientnet.EfficientNetBa,
"""b3""": efficientnet.EfficientNetBa,
"""b4""": efficientnet.EfficientNetBa,
"""b5""": efficientnet.EfficientNetBa,
"""b6""": efficientnet.EfficientNetBa,
"""b7""": efficientnet.EfficientNetBa,
}
_UpperCAmelCase = {
"""b0""": {
"""hidden_dim""": 1_2_8_0,
"""width_coef""": 1.0,
"""depth_coef""": 1.0,
"""image_size""": 2_2_4,
"""dropout_rate""": 0.2,
"""dw_padding""": [],
},
"""b1""": {
"""hidden_dim""": 1_2_8_0,
"""width_coef""": 1.0,
"""depth_coef""": 1.1,
"""image_size""": 2_4_0,
"""dropout_rate""": 0.2,
"""dw_padding""": [1_6],
},
"""b2""": {
"""hidden_dim""": 1_4_0_8,
"""width_coef""": 1.1,
"""depth_coef""": 1.2,
"""image_size""": 2_6_0,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 8, 1_6],
},
"""b3""": {
"""hidden_dim""": 1_5_3_6,
"""width_coef""": 1.2,
"""depth_coef""": 1.4,
"""image_size""": 3_0_0,
"""dropout_rate""": 0.3,
"""dw_padding""": [5, 1_8],
},
"""b4""": {
"""hidden_dim""": 1_7_9_2,
"""width_coef""": 1.4,
"""depth_coef""": 1.8,
"""image_size""": 3_8_0,
"""dropout_rate""": 0.4,
"""dw_padding""": [6],
},
"""b5""": {
"""hidden_dim""": 2_0_4_8,
"""width_coef""": 1.6,
"""depth_coef""": 2.2,
"""image_size""": 4_5_6,
"""dropout_rate""": 0.4,
"""dw_padding""": [1_3, 2_7],
},
"""b6""": {
"""hidden_dim""": 2_3_0_4,
"""width_coef""": 1.8,
"""depth_coef""": 2.6,
"""image_size""": 5_2_8,
"""dropout_rate""": 0.5,
"""dw_padding""": [3_1],
},
"""b7""": {
"""hidden_dim""": 2_5_6_0,
"""width_coef""": 2.0,
"""depth_coef""": 3.1,
"""image_size""": 6_0_0,
"""dropout_rate""": 0.5,
"""dw_padding""": [1_8],
},
}
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: int =EfficientNetConfig()
SCREAMING_SNAKE_CASE_: Any =CONFIG_MAP[model_name]["""hidden_dim"""]
SCREAMING_SNAKE_CASE_: Optional[int] =CONFIG_MAP[model_name]["""width_coef"""]
SCREAMING_SNAKE_CASE_: List[str] =CONFIG_MAP[model_name]["""depth_coef"""]
SCREAMING_SNAKE_CASE_: List[Any] =CONFIG_MAP[model_name]["""image_size"""]
SCREAMING_SNAKE_CASE_: List[Any] =CONFIG_MAP[model_name]["""dropout_rate"""]
SCREAMING_SNAKE_CASE_: Dict =CONFIG_MAP[model_name]["""dw_padding"""]
SCREAMING_SNAKE_CASE_: List[str] ="""huggingface/label-files"""
SCREAMING_SNAKE_CASE_: Optional[Any] ="""imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE_: List[str] =1000
SCREAMING_SNAKE_CASE_: Any =json.load(open(hf_hub_download(_a , _a , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE_: List[Any] ={int(_a ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE_: Optional[Any] =idalabel
SCREAMING_SNAKE_CASE_: Dict ={v: k for k, v in idalabel.items()}
return config
def __magic_name__ ( ):
SCREAMING_SNAKE_CASE_: Dict ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE_: Optional[int] =Image.open(requests.get(_a , stream=_a ).raw )
return im
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Dict =CONFIG_MAP[model_name]["""image_size"""]
SCREAMING_SNAKE_CASE_: Union[str, Any] =EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] , do_center_crop=_a , )
return preprocessor
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: List[str] =[v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
SCREAMING_SNAKE_CASE_: int =sorted(set(_a ) )
SCREAMING_SNAKE_CASE_: Dict =len(_a )
SCREAMING_SNAKE_CASE_: Union[str, Any] ={b: str(_a ) for b, i in zip(_a , range(_a ) )}
SCREAMING_SNAKE_CASE_: Tuple =[]
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
SCREAMING_SNAKE_CASE_: int =block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
SCREAMING_SNAKE_CASE_: Tuple ={}
for item in rename_keys:
if item[0] in original_param_names:
SCREAMING_SNAKE_CASE_: Any ="""efficientnet.""" + item[1]
SCREAMING_SNAKE_CASE_: List[str] ="""classifier.weight"""
SCREAMING_SNAKE_CASE_: List[str] ="""classifier.bias"""
return key_mapping
def __magic_name__ ( lowercase , lowercase , lowercase ):
for key, value in tf_params.items():
if "normalization" in key:
continue
SCREAMING_SNAKE_CASE_: Optional[Any] =key_mapping[key]
if "_conv" in key and "kernel" in key:
SCREAMING_SNAKE_CASE_: Tuple =torch.from_numpy(_a ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
SCREAMING_SNAKE_CASE_: List[Any] =torch.from_numpy(_a ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
SCREAMING_SNAKE_CASE_: str =torch.from_numpy(np.transpose(_a ) )
else:
SCREAMING_SNAKE_CASE_: List[str] =torch.from_numpy(_a )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_a )
@torch.no_grad()
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: Tuple =model_classes[model_name](
include_top=_a , weights="""imagenet""" , input_tensor=_a , input_shape=_a , pooling=_a , classes=1000 , classifier_activation="""softmax""" , )
SCREAMING_SNAKE_CASE_: int =original_model.trainable_variables
SCREAMING_SNAKE_CASE_: Optional[int] =original_model.non_trainable_variables
SCREAMING_SNAKE_CASE_: List[str] ={param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
SCREAMING_SNAKE_CASE_: str =param.numpy()
SCREAMING_SNAKE_CASE_: Tuple =list(tf_params.keys() )
# Load HuggingFace model
SCREAMING_SNAKE_CASE_: Optional[Any] =get_efficientnet_config(_a )
SCREAMING_SNAKE_CASE_: List[str] =EfficientNetForImageClassification(_a ).eval()
SCREAMING_SNAKE_CASE_: Tuple =hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
SCREAMING_SNAKE_CASE_: Optional[Any] =rename_keys(_a )
replace_params(_a , _a , _a )
# Initialize preprocessor and preprocess input image
SCREAMING_SNAKE_CASE_: int =convert_image_processor(_a )
SCREAMING_SNAKE_CASE_: Tuple =preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE_: Dict =hf_model(**_a )
SCREAMING_SNAKE_CASE_: Optional[int] =outputs.logits.detach().numpy()
# Original model inference
SCREAMING_SNAKE_CASE_: Optional[Any] =False
SCREAMING_SNAKE_CASE_: Tuple =CONFIG_MAP[model_name]["""image_size"""]
SCREAMING_SNAKE_CASE_: Dict =prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
SCREAMING_SNAKE_CASE_: Union[str, Any] =image.img_to_array(_a )
SCREAMING_SNAKE_CASE_: Dict =np.expand_dims(_a , axis=0 )
SCREAMING_SNAKE_CASE_: Optional[Any] =original_model.predict(_a )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_a , _a , atol=1e-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(_a ):
os.mkdir(_a )
# Save converted model and image processor
hf_model.save_pretrained(_a )
preprocessor.save_pretrained(_a )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
SCREAMING_SNAKE_CASE_: List[Any] =f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(_a )
hf_model.push_to_hub(_a )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""b0""",
type=str,
help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""hf_model""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
_UpperCAmelCase = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 173
|
'''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
A =logging.getLogger(__name__)
require_version('pytorch_lightning>=1.0.4')
A ={
'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
A ={
'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
}
A =sorted(arg_to_scheduler.keys())
A ='{' + ', '.join(arg_to_scheduler_choices) + '}'
class _a ( pl.LightningModule ):
def __init__( self : List[str] , lowercase : argparse.Namespace , lowercase : List[Any]=None , lowercase : Dict="base" , lowercase : Optional[int]=None , lowercase : Dict=None , lowercase : Tuple=None , **lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(lowercase )
UpperCAmelCase = 0
UpperCAmelCase = Path(self.hparams.output_dir )
UpperCAmelCase = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
UpperCAmelCase = 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=lowercase , **lowercase , )
else:
UpperCAmelCase = config
UpperCAmelCase = ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
for p in extra_model_params:
if getattr(self.hparams , lowercase , lowercase ):
assert hasattr(self.config , lowercase ), f"model config doesn't have a `{p}` attribute"
setattr(self.config , lowercase , getattr(self.hparams , lowercase ) )
if tokenizer is None:
UpperCAmelCase = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=lowercase , )
else:
UpperCAmelCase = tokenizer
UpperCAmelCase = MODEL_MODES[mode]
if model is None:
UpperCAmelCase = 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=lowercase , )
else:
UpperCAmelCase = model
def A ( self : List[Any] , *lowercase : List[str] , **lowercase : List[str] ):
'''simple docstring'''
UpperCAmelCase = self.model_type.from_pretrained(*lowercase , **lowercase )
def A ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.hparams.lr_scheduler]
UpperCAmelCase = get_schedule_func(
self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps() )
UpperCAmelCase = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
return scheduler
def A ( self : str ):
'''simple docstring'''
UpperCAmelCase = self.model
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''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:
UpperCAmelCase = Adafactor(
lowercase , lr=self.hparams.learning_rate , scale_parameter=lowercase , relative_step=lowercase )
else:
UpperCAmelCase = AdamW(
lowercase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon )
UpperCAmelCase = optimizer
UpperCAmelCase = self.get_lr_scheduler()
return [optimizer], [scheduler]
def A ( self : List[Any] , lowercase : int , lowercase : List[str] ):
'''simple docstring'''
return self.validation_step(lowercase , lowercase )
def A ( self : List[Any] , lowercase : Tuple ):
'''simple docstring'''
return self.validation_end(lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase = max(1 , self.hparams.gpus ) # TODO: consider num_tpu_cores
UpperCAmelCase = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def A ( self : List[str] , lowercase : Any ):
'''simple docstring'''
if stage == "test":
UpperCAmelCase = len(self.test_dataloader().dataset )
else:
UpperCAmelCase = self.get_dataloader('''train''' , self.hparams.train_batch_size , shuffle=lowercase )
UpperCAmelCase = len(self.train_dataloader().dataset )
def A ( self : List[str] , lowercase : str , lowercase : int , lowercase : bool = False ):
'''simple docstring'''
raise NotImplementedError('''You must implement this for your task''' )
def A ( self : Union[str, Any] ):
'''simple docstring'''
return self.train_loader
def A ( self : Optional[Any] ):
'''simple docstring'''
return self.get_dataloader('''dev''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : List[Any] ):
'''simple docstring'''
return self.get_dataloader('''test''' , self.hparams.eval_batch_size , shuffle=lowercase )
def A ( self : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir , '''cached_{}_{}_{}'''.format(
lowercase , list(filter(lowercase , self.hparams.model_name_or_path.split('''/''' ) ) ).pop() , str(self.hparams.max_seq_length ) , ) , )
@pl.utilities.rank_zero_only
def A ( self : List[str] , lowercase : Dict[str, Any] ):
'''simple docstring'''
UpperCAmelCase = self.output_dir.joinpath('''best_tfmr''' )
UpperCAmelCase = self.step_count
self.model.save_pretrained(lowercase )
self.tokenizer.save_pretrained(lowercase )
@staticmethod
def A ( lowercase : Optional[int] , lowercase : List[str] ):
'''simple docstring'''
parser.add_argument(
'''--model_name_or_path''' , default=lowercase , type=lowercase , required=lowercase , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
parser.add_argument(
'''--config_name''' , default='''''' , type=lowercase , help='''Pretrained config name or path if not the same as model_name''' )
parser.add_argument(
'''--tokenizer_name''' , default=lowercase , type=lowercase , help='''Pretrained tokenizer name or path if not the same as model_name''' , )
parser.add_argument(
'''--cache_dir''' , default=str(Path(lowercase ).parent / '''test_run''' / '''cache''' ) , type=lowercase , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
parser.add_argument(
'''--encoder_layerdrop''' , type=lowercase , help='''Encoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--decoder_layerdrop''' , type=lowercase , help='''Decoder layer dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--dropout''' , type=lowercase , help='''Dropout probability (Optional). Goes into model.config''' , )
parser.add_argument(
'''--attention_dropout''' , type=lowercase , help='''Attention dropout probability (Optional). Goes into model.config''' , )
parser.add_argument('''--learning_rate''' , default=5E-5 , type=lowercase , help='''The initial learning rate for Adam.''' )
parser.add_argument(
'''--lr_scheduler''' , default='''linear''' , choices=lowercase , metavar=lowercase , type=lowercase , help='''Learning rate scheduler''' , )
parser.add_argument('''--weight_decay''' , default=0.0 , type=lowercase , help='''Weight decay if we apply some.''' )
parser.add_argument('''--adam_epsilon''' , default=1E-8 , type=lowercase , help='''Epsilon for Adam optimizer.''' )
parser.add_argument('''--warmup_steps''' , default=0 , type=lowercase , help='''Linear warmup over warmup_steps.''' )
parser.add_argument('''--num_workers''' , default=4 , type=lowercase , help='''kwarg passed to DataLoader''' )
parser.add_argument('''--num_train_epochs''' , dest='''max_epochs''' , default=3 , type=lowercase )
parser.add_argument('''--train_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--eval_batch_size''' , default=32 , type=lowercase )
parser.add_argument('''--adafactor''' , action='''store_true''' )
class _a ( pl.Callback ):
def A ( self : Dict , lowercase : Optional[Any] , lowercase : List[Any] ):
'''simple docstring'''
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 _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Union[str, Any] , lowercase : Any ):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(lowercase )
class _a ( pl.Callback ):
def A ( self : Optional[int] , lowercase : Optional[int] , lowercase : Dict ):
'''simple docstring'''
UpperCAmelCase = trainer.lr_schedulers[0]['''scheduler''']
UpperCAmelCase = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
pl_module.logger.log_metrics(lowercase )
def A ( self : Tuple , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Validation results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log results
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def A ( self : Dict , lowercase : pl.Trainer , lowercase : pl.LightningModule ):
'''simple docstring'''
rank_zero_info('''***** Test results *****''' )
UpperCAmelCase = trainer.callback_metrics
# Log and save results to file
UpperCAmelCase = os.path.join(pl_module.hparams.output_dir , '''test_results.txt''' )
with open(lowercase , '''w''' ) as writer:
for key in sorted(lowercase ):
if key not in ["log", "progress_bar"]:
rank_zero_info('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
writer.write('''{} = {}\n'''.format(lowercase , str(metrics[key] ) ) )
def snake_case_ (_a : int , _a : Optional[Any] ):
# To allow all pl args uncomment the following line
# parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument(
'''--output_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''model_checkpoints''' ) , type=_a , 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=_a , 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=_a )
parser.add_argument('''--max_grad_norm''' , dest='''gradient_clip_val''' , default=1.0 , type=_a , 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=_a , default=1 , help='''Number of updates steps to accumulate before performing a backward/update pass.''' , )
parser.add_argument('''--seed''' , type=_a , default=4_2 , help='''random seed for initialization''' )
parser.add_argument(
'''--data_dir''' , default=str(Path(_a ).parent / '''test_run''' / '''dummy-train-data''' ) , type=_a , help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.''' , )
def snake_case_ (_a : BaseTransformer , _a : argparse.Namespace , _a : List[Any]=None , _a : Tuple=True , _a : int=[] , _a : Any=None , _a : int=None , **_a : Optional[Any] , ):
pl.seed_everything(args.seed )
# init model
UpperCAmelCase = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_a )
# add custom checkpoints
if checkpoint_callback is None:
UpperCAmelCase = 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(_a )
if logging_callback is None:
UpperCAmelCase = LoggingCallback()
UpperCAmelCase = {}
if args.fpaa:
UpperCAmelCase = 1_6
if args.gpus > 1:
UpperCAmelCase = '''auto'''
UpperCAmelCase = '''ddp'''
UpperCAmelCase = args.accumulate_grad_batches
UpperCAmelCase = None
UpperCAmelCase = '''auto'''
UpperCAmelCase = pl.Trainer.from_argparse_args(
_a , weights_summary=_a , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_a , val_check_interval=1 , num_sanity_val_steps=2 , **_a , )
if args.do_train:
trainer.fit(_a )
else:
print('''RAG modeling tests with new set functions successfuly executed!''' )
return trainer
| 34
| 0
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
lowerCamelCase__ = None
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
lowerCamelCase__ = {
'vocab_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
},
'tokenizer_file': {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
},
}
lowerCamelCase__ = {
'xlnet-base-cased': None,
'xlnet-large-cased': None,
}
lowerCamelCase__ = '▁'
# Segments (not really needed)
lowerCamelCase__ = 0
lowerCamelCase__ = 1
lowerCamelCase__ = 2
lowerCamelCase__ = 3
lowerCamelCase__ = 4
class lowerCAmelCase__ ( UpperCAmelCase__ ):
lowerCAmelCase : List[Any] = VOCAB_FILES_NAMES
lowerCAmelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase : List[Any] = "left"
lowerCAmelCase : Tuple = XLNetTokenizer
def __init__( self : Tuple , lowerCamelCase__ : List[Any]=None , lowerCamelCase__ : Any=None , lowerCamelCase__ : Tuple=False , lowerCamelCase__ : Tuple=True , lowerCamelCase__ : int=False , lowerCamelCase__ : List[str]="<s>" , lowerCamelCase__ : Union[str, Any]="</s>" , lowerCamelCase__ : Optional[int]="<unk>" , lowerCamelCase__ : Dict="<sep>" , lowerCamelCase__ : Dict="<pad>" , lowerCamelCase__ : Any="<cls>" , lowerCamelCase__ : Any="<mask>" , lowerCamelCase__ : int=["<eop>", "<eod>"] , **lowerCamelCase__ : Any , ) ->Dict:
'''simple docstring'''
_UpperCAmelCase : Tuple = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
vocab_file=_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , do_lower_case=_SCREAMING_SNAKE_CASE , remove_space=_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase : Optional[Any] = 3
_UpperCAmelCase : List[Any] = do_lower_case
_UpperCAmelCase : Optional[Any] = remove_space
_UpperCAmelCase : Tuple = keep_accents
_UpperCAmelCase : str = vocab_file
_UpperCAmelCase : List[str] = False if not self.vocab_file else True
def lowerCAmelCase__ ( self : List[Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Dict = None ) ->List[int]:
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def lowerCAmelCase__ ( self : List[str] , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Any = None ) ->List[int]:
'''simple docstring'''
_UpperCAmelCase : str = [self.sep_token_id]
_UpperCAmelCase : List[str] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def lowerCAmelCase__ ( self : Tuple , lowerCamelCase__ : str , lowerCamelCase__ : int = None ) ->Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : Union[str, Any] = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 371
|
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
lowerCamelCase__ = {
'return_dict': False,
'output_hidden_states': True,
'output_attentions': True,
'torchscript': True,
'torch_dtype': 'float16',
'use_bfloat16': True,
'tf_legacy_loss': True,
'pruned_heads': {'a': 1},
'tie_word_embeddings': False,
'is_decoder': True,
'cross_attention_hidden_size': 128,
'add_cross_attention': True,
'tie_encoder_decoder': True,
'max_length': 50,
'min_length': 3,
'do_sample': True,
'early_stopping': True,
'num_beams': 3,
'num_beam_groups': 3,
'diversity_penalty': 0.5,
'temperature': 2.0,
'top_k': 10,
'top_p': 0.7,
'typical_p': 0.2,
'repetition_penalty': 0.8,
'length_penalty': 0.8,
'no_repeat_ngram_size': 5,
'encoder_no_repeat_ngram_size': 5,
'bad_words_ids': [1, 2, 3],
'num_return_sequences': 3,
'chunk_size_feed_forward': 5,
'output_scores': True,
'return_dict_in_generate': True,
'forced_bos_token_id': 2,
'forced_eos_token_id': 3,
'remove_invalid_values': True,
'architectures': ['BertModel'],
'finetuning_task': 'translation',
'id2label': {0: 'label'},
'label2id': {'label': '0'},
'tokenizer_class': 'BertTokenizerFast',
'prefix': 'prefix',
'bos_token_id': 6,
'pad_token_id': 7,
'eos_token_id': 8,
'sep_token_id': 9,
'decoder_start_token_id': 10,
'exponential_decay_length_penalty': (5, 1.01),
'suppress_tokens': [0, 1],
'begin_suppress_tokens': 2,
'task_specific_params': {'translation': 'some_params'},
'problem_type': 'regression',
}
@is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
@classmethod
def lowerCAmelCase__ ( cls : List[str] ) ->str:
'''simple docstring'''
_UpperCAmelCase : Tuple = TOKEN
HfFolder.save_token(lowerCamelCase__ )
@classmethod
def lowerCAmelCase__ ( cls : Union[str, Any] ) ->int:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="test-config" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-config-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-config" )
except HTTPError:
pass
def lowerCAmelCase__ ( self : int ) ->Any:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("test-config" , use_auth_token=self._token )
_UpperCAmelCase : List[str] = BertConfig.from_pretrained(F"""{USER}/test-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase__ , getattr(lowerCamelCase__ , lowerCamelCase__ ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-config" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ , repo_id="test-config" , push_to_hub=lowerCamelCase__ , use_auth_token=self._token )
_UpperCAmelCase : Dict = BertConfig.from_pretrained(F"""{USER}/test-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase__ , getattr(lowerCamelCase__ , lowerCamelCase__ ) )
def lowerCAmelCase__ ( self : Optional[Any] ) ->Optional[int]:
'''simple docstring'''
_UpperCAmelCase : str = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("valid_org/test-config-org" , use_auth_token=self._token )
_UpperCAmelCase : List[str] = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase__ , getattr(lowerCamelCase__ , lowerCamelCase__ ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-config-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase__ , repo_id="valid_org/test-config-org" , push_to_hub=lowerCamelCase__ , use_auth_token=self._token )
_UpperCAmelCase : int = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase__ , getattr(lowerCamelCase__ , lowerCamelCase__ ) )
def lowerCAmelCase__ ( self : List[str] ) ->Any:
'''simple docstring'''
CustomConfig.register_for_auto_class()
_UpperCAmelCase : int = CustomConfig(attribute=42 )
config.push_to_hub("test-dynamic-config" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {"AutoConfig": "custom_configuration.CustomConfig"} )
_UpperCAmelCase : str = AutoConfig.from_pretrained(F"""{USER}/test-dynamic-config""" , trust_remote_code=lowerCamelCase__ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , "CustomConfig" )
self.assertEqual(new_config.attribute , 42 )
class lowerCAmelCase__ ( unittest.TestCase ):
def lowerCAmelCase__ ( self : List[str] ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : Tuple = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_UpperCAmelCase : Any = c.n_embd + 1 # int
_UpperCAmelCase : List[Any] = c.resid_pdrop + 1.0 # float
_UpperCAmelCase : Tuple = not c.scale_attn_weights # bool
_UpperCAmelCase : List[Any] = c.summary_type + "foo" # str
c.update_from_string(
F"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}""" )
self.assertEqual(lowerCamelCase__ , c.n_embd , "mismatch for key: n_embd" )
self.assertEqual(lowerCamelCase__ , c.resid_pdrop , "mismatch for key: resid_pdrop" )
self.assertEqual(lowerCamelCase__ , c.scale_attn_weights , "mismatch for key: scale_attn_weights" )
self.assertEqual(lowerCamelCase__ , c.summary_type , "mismatch for key: summary_type" )
def lowerCAmelCase__ ( self : Optional[Any] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : Any = PretrainedConfig()
_UpperCAmelCase : Tuple = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCamelCase__ , ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"] )
_UpperCAmelCase : List[str] = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCamelCase__ , lowerCamelCase__ )]
if len(lowerCamelCase__ ) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
F""" {', '.join(lowerCamelCase__ )}.""" )
def lowerCAmelCase__ ( self : Optional[int] ) ->int:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
# config is in subfolder, the following should not work without specifying the subfolder
_UpperCAmelCase : Any = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" )
_UpperCAmelCase : Any = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" , subfolder="bert" )
self.assertIsNotNone(lowerCamelCase__ )
def lowerCAmelCase__ ( self : Optional[int] ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = mock.Mock()
_UpperCAmelCase : List[str] = 5_00
_UpperCAmelCase : Dict = {}
_UpperCAmelCase : Tuple = HTTPError
_UpperCAmelCase : Any = {}
# Download this model to make sure it's in the cache.
_UpperCAmelCase : int = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=lowerCamelCase__ ) as mock_head:
_UpperCAmelCase : Union[str, Any] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase__ ( self : Optional[int] ) ->Any:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json" )
def lowerCAmelCase__ ( self : Optional[Any] ) ->Optional[Any]:
'''simple docstring'''
_UpperCAmelCase : int = AutoConfig.from_pretrained("bert-base-cased" )
_UpperCAmelCase : str = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCamelCase__ )
_UpperCAmelCase : Dict = 2
json.dump(configuration.to_dict() , open(os.path.join(lowerCamelCase__ , "config.4.0.0.json" ) , "w" ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_UpperCAmelCase : Optional[int] = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_UpperCAmelCase : Dict = ["config.42.0.0.json"]
_UpperCAmelCase : Union[str, Any] = 7_68
configuration.save_pretrained(lowerCamelCase__ )
shutil.move(os.path.join(lowerCamelCase__ , "config.4.0.0.json" ) , os.path.join(lowerCamelCase__ , "config.42.0.0.json" ) )
_UpperCAmelCase : Dict = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(new_configuration.hidden_size , 7_68 )
def lowerCAmelCase__ ( self : List[str] ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
_UpperCAmelCase : Any = "v4.0.0"
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCamelCase__ , return_unused_kwargs=lowerCamelCase__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCamelCase__ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_UpperCAmelCase : List[Any] = "v3.0.0"
_UpperCAmelCase : int = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(old_configuration.hidden_size , 7_68 )
| 322
| 0
|
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __A:
def __init__( self , _snake_case , _snake_case=12 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=0.02 , _snake_case=0 , _snake_case=None , ) -> Dict:
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_input_mask
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = projection_dim
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = dropout
__a = attention_dropout
__a = max_position_embeddings
__a = initializer_range
__a = scope
__a = bos_token_id
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''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] )
if input_mask is not None:
__a = input_mask.numpy()
__a , __a = input_mask.shape
__a = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_snake_case ):
__a = 1
__a = 0
__a = self.get_config()
return config, input_ids, tf.convert_to_tensor(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = TFBlipTextModel(config=_snake_case )
__a = model(_snake_case , attention_mask=_snake_case , training=_snake_case )
__a = model(_snake_case , training=_snake_case )
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 SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __A( a , unittest.TestCase ):
snake_case_ = (TFBlipTextModel,) if is_tf_available() else ()
snake_case_ = False
snake_case_ = False
snake_case_ = False
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = BlipTextModelTester(self )
__a = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
pass
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = TFBlipTextModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=True ) -> Optional[Any]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case )
| 6
|
snake_case_ : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingface-hub": "huggingface-hub>=0.13.2",
"requests-mock": "requests-mock==1.10.0",
"importlib_metadata": "importlib_metadata",
"invisible-watermark": "invisible-watermark",
"isort": "isort>=5.5.4",
"jax": "jax>=0.2.8,!=0.3.2",
"jaxlib": "jaxlib>=0.1.65",
"Jinja2": "Jinja2",
"k-diffusion": "k-diffusion>=0.0.12",
"torchsde": "torchsde",
"note_seq": "note_seq",
"librosa": "librosa",
"numpy": "numpy",
"omegaconf": "omegaconf",
"parameterized": "parameterized",
"protobuf": "protobuf>=3.20.3,<4",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",
"ruff": "ruff>=0.0.241",
"safetensors": "safetensors",
"sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
"scipy": "scipy",
"onnx": "onnx",
"regex": "regex!=2019.12.17",
"requests": "requests",
"tensorboard": "tensorboard",
"torch": "torch>=1.4",
"torchvision": "torchvision",
"transformers": "transformers>=4.25.1",
"urllib3": "urllib3<=2.0.0",
}
| 51
| 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
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : str = {
"hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCAmelCase = 'yolos'
def __init__(self : List[str] , _lowerCAmelCase : Optional[int]=768 , _lowerCAmelCase : Optional[int]=12 , _lowerCAmelCase : Any=12 , _lowerCAmelCase : List[str]=3072 , _lowerCAmelCase : Any="gelu" , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : List[Any]=0.0 , _lowerCAmelCase : Dict=0.02 , _lowerCAmelCase : Optional[int]=1e-12 , _lowerCAmelCase : List[Any]=[512, 864] , _lowerCAmelCase : Any=16 , _lowerCAmelCase : Any=3 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : List[str]=100 , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Any=False , _lowerCAmelCase : List[str]=1 , _lowerCAmelCase : Tuple=5 , _lowerCAmelCase : Union[str, Any]=2 , _lowerCAmelCase : int=5 , _lowerCAmelCase : Union[str, Any]=2 , _lowerCAmelCase : Dict=0.1 , **_lowerCAmelCase : Dict , ):
super().__init__(**a_ )
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 = initializer_range
A = layer_norm_eps
A = image_size
A = patch_size
A = num_channels
A = qkv_bias
A = num_detection_tokens
A = use_mid_position_embeddings
A = auxiliary_loss
# Hungarian matcher
A = class_cost
A = bbox_cost
A = giou_cost
# Loss coefficients
A = bbox_loss_coefficient
A = giou_loss_coefficient
A = eos_coefficient
class __UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCAmelCase = version.parse('''1.11''' )
@property
def A (self : str ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A (self : Tuple ):
return 1e-4
@property
def A (self : Optional[int] ):
return 12
| 359
|
'''simple docstring'''
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase , int(b / 2 ) ) * actual_power(UpperCAmelCase , int(b / 2 ) )
else:
return a * actual_power(UpperCAmelCase , int(b / 2 ) ) * actual_power(UpperCAmelCase , int(b / 2 ) )
def __a ( UpperCAmelCase , UpperCAmelCase ) ->float:
"""simple docstring"""
if b < 0:
return 1 / actual_power(UpperCAmelCase , UpperCAmelCase )
return actual_power(UpperCAmelCase , UpperCAmelCase )
if __name__ == "__main__":
print(power(-2, -3))
| 337
| 0
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_: Optional[int] =logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_ : Any ) -> int:
'''simple docstring'''
UpperCAmelCase_ = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
UpperCAmelCase_ = 1_28
elif "12-12" in model_name:
UpperCAmelCase_ = 12
UpperCAmelCase_ = 12
elif "14-14" in model_name:
UpperCAmelCase_ = 14
UpperCAmelCase_ = 14
elif "16-16" in model_name:
UpperCAmelCase_ = 16
UpperCAmelCase_ = 16
else:
raise ValueError("Model not supported" )
UpperCAmelCase_ = "huggingface/label-files"
if "speech-commands" in model_name:
UpperCAmelCase_ = 35
UpperCAmelCase_ = "speech-commands-v2-id2label.json"
else:
UpperCAmelCase_ = 5_27
UpperCAmelCase_ = "audioset-id2label.json"
UpperCAmelCase_ = json.load(open(hf_hub_download(snake_case_ , snake_case_ , repo_type="dataset" ) , "r" ) )
UpperCAmelCase_ = {int(snake_case_ ): v for k, v in idalabel.items()}
UpperCAmelCase_ = idalabel
UpperCAmelCase_ = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase_ ( snake_case_ : List[str] ) -> Any:
'''simple docstring'''
if "module.v" in name:
UpperCAmelCase_ = name.replace("module.v" , "audio_spectrogram_transformer" )
if "cls_token" in name:
UpperCAmelCase_ = name.replace("cls_token" , "embeddings.cls_token" )
if "dist_token" in name:
UpperCAmelCase_ = name.replace("dist_token" , "embeddings.distillation_token" )
if "pos_embed" in name:
UpperCAmelCase_ = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
UpperCAmelCase_ = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
# transformer blocks
if "blocks" in name:
UpperCAmelCase_ = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
UpperCAmelCase_ = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
UpperCAmelCase_ = name.replace("attn" , "attention.self" )
if "norm1" in name:
UpperCAmelCase_ = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
UpperCAmelCase_ = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
UpperCAmelCase_ = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
UpperCAmelCase_ = name.replace("mlp.fc2" , "output.dense" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
UpperCAmelCase_ = name.replace("audio_spectrogram_transformer.norm" , "audio_spectrogram_transformer.layernorm" )
# classifier head
if "module.mlp_head.0" in name:
UpperCAmelCase_ = name.replace("module.mlp_head.0" , "classifier.layernorm" )
if "module.mlp_head.1" in name:
UpperCAmelCase_ = name.replace("module.mlp_head.1" , "classifier.dense" )
return name
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCAmelCase_ = orig_state_dict.pop(snake_case_ )
if "qkv" in key:
UpperCAmelCase_ = key.split("." )
UpperCAmelCase_ = int(key_split[3] )
UpperCAmelCase_ = config.hidden_size
if "weight" in key:
UpperCAmelCase_ = val[:dim, :]
UpperCAmelCase_ = val[dim : dim * 2, :]
UpperCAmelCase_ = val[-dim:, :]
else:
UpperCAmelCase_ = val[:dim]
UpperCAmelCase_ = val[dim : dim * 2]
UpperCAmelCase_ = val[-dim:]
else:
UpperCAmelCase_ = val
return orig_state_dict
def lowerCAmelCase_ ( snake_case_ : Any ) -> str:
'''simple docstring'''
UpperCAmelCase_ = [
"module.v.head.weight",
"module.v.head.bias",
"module.v.head_dist.weight",
"module.v.head_dist.bias",
]
for k in ignore_keys:
state_dict.pop(snake_case_ , snake_case_ )
@torch.no_grad()
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : Any , snake_case_ : Optional[int]=False ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = get_audio_spectrogram_transformer_config(snake_case_ )
UpperCAmelCase_ = {
"ast-finetuned-audioset-10-10-0.4593": (
"https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.450": (
"https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448": (
"https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448-v2": (
"https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
),
"ast-finetuned-audioset-12-12-0.447": (
"https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
),
"ast-finetuned-audioset-14-14-0.443": (
"https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
),
"ast-finetuned-audioset-16-16-0.442": (
"https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
),
"ast-finetuned-speech-commands-v2": (
"https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
),
}
# load original state_dict
UpperCAmelCase_ = model_name_to_url[model_name]
UpperCAmelCase_ = torch.hub.load_state_dict_from_url(snake_case_ , map_location="cpu" )
# remove some keys
remove_keys(snake_case_ )
# rename some keys
UpperCAmelCase_ = convert_state_dict(snake_case_ , snake_case_ )
# load 🤗 model
UpperCAmelCase_ = ASTForAudioClassification(snake_case_ )
model.eval()
model.load_state_dict(snake_case_ )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
UpperCAmelCase_ = -4.267_7393 if "speech-commands" not in model_name else -6.84_5978
UpperCAmelCase_ = 4.568_9974 if "speech-commands" not in model_name else 5.565_4526
UpperCAmelCase_ = 10_24 if "speech-commands" not in model_name else 1_28
UpperCAmelCase_ = ASTFeatureExtractor(mean=snake_case_ , std=snake_case_ , max_length=snake_case_ )
if "speech-commands" in model_name:
UpperCAmelCase_ = load_dataset("speech_commands" , "v0.02" , split="validation" )
UpperCAmelCase_ = dataset[0]["audio"]["array"]
else:
UpperCAmelCase_ = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint" , filename="sample_audio.flac" , repo_type="dataset" , )
UpperCAmelCase_ , UpperCAmelCase_ = torchaudio.load(snake_case_ )
UpperCAmelCase_ = waveform.squeeze().numpy()
UpperCAmelCase_ = feature_extractor(snake_case_ , sampling_rate=1_60_00 , return_tensors="pt" )
# forward pass
UpperCAmelCase_ = model(**snake_case_ )
UpperCAmelCase_ = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
UpperCAmelCase_ = torch.tensor([-0.8760, -7.0042, -8.6602] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
UpperCAmelCase_ = torch.tensor([-1.1986, -7.0903, -8.2718] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
UpperCAmelCase_ = torch.tensor([-2.6128, -8.0080, -9.4344] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
UpperCAmelCase_ = torch.tensor([-1.5080, -7.4534, -8.8917] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
UpperCAmelCase_ = torch.tensor([-0.5050, -6.5833, -8.0843] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
UpperCAmelCase_ = torch.tensor([-0.3826, -7.0336, -8.2413] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
UpperCAmelCase_ = torch.tensor([-1.2113, -6.9101, -8.3470] )
elif model_name == "ast-finetuned-speech-commands-v2":
UpperCAmelCase_ = torch.tensor([6.1589, -8.0566, -8.7984] )
else:
raise ValueError("Unknown model name" )
if not torch.allclose(logits[0, :3] , snake_case_ , atol=1E-4 ):
raise ValueError("Logits don't match" )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(snake_case_ )
print(f"""Saving feature extractor to {pytorch_dump_folder_path}""" )
feature_extractor.save_pretrained(snake_case_ )
if push_to_hub:
print("Pushing model and feature extractor to the hub..." )
model.push_to_hub(f"""MIT/{model_name}""" )
feature_extractor.push_to_hub(f"""MIT/{model_name}""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_: Tuple =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='ast-finetuned-audioset-10-10-0.4593',
type=str,
help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
SCREAMING_SNAKE_CASE_: List[Any] =parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Dict =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Tuple ={}
class __A ( UpperCamelCase__ ):
a__ : int = """llama"""
a__ : Any = ["""past_key_values"""]
def __init__(self : List[str] , __a : List[str]=32000 , __a : Tuple=4096 , __a : List[Any]=11008 , __a : Dict=32 , __a : Tuple=32 , __a : Any=None , __a : Any="silu" , __a : List[Any]=2048 , __a : List[Any]=0.02 , __a : str=1E-6 , __a : Optional[Any]=True , __a : Union[str, Any]=0 , __a : Any=1 , __a : Dict=2 , __a : Dict=1 , __a : str=False , __a : str=None , **__a : Optional[Any] , ):
UpperCAmelCase_ = vocab_size
UpperCAmelCase_ = max_position_embeddings
UpperCAmelCase_ = hidden_size
UpperCAmelCase_ = intermediate_size
UpperCAmelCase_ = num_hidden_layers
UpperCAmelCase_ = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
UpperCAmelCase_ = num_attention_heads
UpperCAmelCase_ = num_key_value_heads
UpperCAmelCase_ = hidden_act
UpperCAmelCase_ = initializer_range
UpperCAmelCase_ = rms_norm_eps
UpperCAmelCase_ = pretraining_tp
UpperCAmelCase_ = use_cache
UpperCAmelCase_ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , tie_word_embeddings=__a , **__a , )
def _lowercase (self : List[str] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , __a ) or len(self.rope_scaling ) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
f"""got {self.rope_scaling}""" )
UpperCAmelCase_ = self.rope_scaling.get("type" , __a )
UpperCAmelCase_ = self.rope_scaling.get("factor" , __a )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
if rope_scaling_factor is None or not isinstance(__a , __a ) or rope_scaling_factor <= 1.0:
raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 1
| 1
|
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = "x" , _UpperCAmelCase = 10**-10 , _UpperCAmelCase = 1 , ):
__a = symbols(_UpperCAmelCase )
__a = lambdify(_UpperCAmelCase , _UpperCAmelCase )
__a = lambdify(_UpperCAmelCase , diff(_UpperCAmelCase , _UpperCAmelCase ) )
__a = starting_point
while True:
if diff_function(_UpperCAmelCase ) != 0:
__a = prev_guess - multiplicity * func(_UpperCAmelCase ) / diff_function(
_UpperCAmelCase )
else:
raise ZeroDivisionError('''Could not find root''' ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
__a = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
# Find root of polynomial
# Find fourth Root of 5
print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}')
# Find value of e
print(
'''The root of log(y) - 1 = 0 is ''',
f'{newton_raphson("log(y) - 1", 2, variable="y")}',
)
# Exponential Roots
print(
'''The root of exp(x) - 1 = 0 is''',
f'{newton_raphson("exp(x) - 1", 10, precision=0.0_0_5)}',
)
# Find root of cos(x)
print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')
| 131
|
def __snake_case ( _UpperCAmelCase ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('''Input value must be an \'int\' type''' )
__a = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod()
| 131
| 1
|
from math import factorial
a__ = {str(digit): factorial(digit) for digit in range(10)}
def lowercase ( SCREAMING_SNAKE_CASE__ : int ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(SCREAMING_SNAKE_CASE__ ) )
def lowercase ( SCREAMING_SNAKE_CASE__ : int = 60 , SCREAMING_SNAKE_CASE__ : int = 1_000_000 ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
_snake_case : Tuple = 0
# the cached sizes of the previous chains
_snake_case : dict[int, int] = {}
for start_chain_element in range(1 , SCREAMING_SNAKE_CASE__ ):
# The temporary set will contain the elements of the chain
_snake_case : Optional[int] = set()
_snake_case : int = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
_snake_case : int = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(SCREAMING_SNAKE_CASE__ )
chain_set_length += 1
_snake_case : Optional[Any] = digit_factorial_sum(SCREAMING_SNAKE_CASE__ )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
_snake_case : Optional[Any] = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{solution()}''')
| 317
|
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
a__ = datasets.utils.logging.get_logger(__name__)
a__ = ["""names""", """prefix"""]
a__ = ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
a__ = ["""encoding_errors""", """on_bad_lines"""]
a__ = ["""date_format"""]
@dataclass
class snake_case ( datasets.BuilderConfig ):
'''simple docstring'''
snake_case_ : str = ","
snake_case_ : Optional[str] = None
snake_case_ : Optional[Union[int, List[int], str]] = "infer"
snake_case_ : Optional[List[str]] = None
snake_case_ : Optional[List[str]] = None
snake_case_ : Optional[Union[int, str, List[int], List[str]]] = None
snake_case_ : Optional[Union[List[int], List[str]]] = None
snake_case_ : Optional[str] = None
snake_case_ : bool = True
snake_case_ : Optional[Literal["c", "python", "pyarrow"]] = None
snake_case_ : Dict[Union[int, str], Callable[[Any], Any]] = None
snake_case_ : Optional[list] = None
snake_case_ : Optional[list] = None
snake_case_ : bool = False
snake_case_ : Optional[Union[int, List[int]]] = None
snake_case_ : Optional[int] = None
snake_case_ : Optional[Union[str, List[str]]] = None
snake_case_ : bool = True
snake_case_ : bool = True
snake_case_ : bool = False
snake_case_ : bool = True
snake_case_ : Optional[str] = None
snake_case_ : str = "."
snake_case_ : Optional[str] = None
snake_case_ : str = '"'
snake_case_ : int = 0
snake_case_ : Optional[str] = None
snake_case_ : Optional[str] = None
snake_case_ : Optional[str] = None
snake_case_ : Optional[str] = None
snake_case_ : bool = True
snake_case_ : bool = True
snake_case_ : int = 0
snake_case_ : bool = True
snake_case_ : bool = False
snake_case_ : Optional[str] = None
snake_case_ : int = 1_00_00
snake_case_ : Optional[datasets.Features] = None
snake_case_ : Optional[str] = "strict"
snake_case_ : Literal["error", "warn", "skip"] = "error"
snake_case_ : Optional[str] = None
def UpperCamelCase_ ( self : List[Any]) -> Dict:
"""simple docstring"""
if self.delimiter is not None:
_snake_case : str = self.delimiter
if self.column_names is not None:
_snake_case : str = self.column_names
@property
def UpperCamelCase_ ( self : List[Any]) -> str:
"""simple docstring"""
_snake_case : Dict = {
"""sep""": self.sep,
"""header""": self.header,
"""names""": self.names,
"""index_col""": self.index_col,
"""usecols""": self.usecols,
"""prefix""": self.prefix,
"""mangle_dupe_cols""": self.mangle_dupe_cols,
"""engine""": self.engine,
"""converters""": self.converters,
"""true_values""": self.true_values,
"""false_values""": self.false_values,
"""skipinitialspace""": self.skipinitialspace,
"""skiprows""": self.skiprows,
"""nrows""": self.nrows,
"""na_values""": self.na_values,
"""keep_default_na""": self.keep_default_na,
"""na_filter""": self.na_filter,
"""verbose""": self.verbose,
"""skip_blank_lines""": self.skip_blank_lines,
"""thousands""": self.thousands,
"""decimal""": self.decimal,
"""lineterminator""": self.lineterminator,
"""quotechar""": self.quotechar,
"""quoting""": self.quoting,
"""escapechar""": self.escapechar,
"""comment""": self.comment,
"""encoding""": self.encoding,
"""dialect""": self.dialect,
"""error_bad_lines""": self.error_bad_lines,
"""warn_bad_lines""": self.warn_bad_lines,
"""skipfooter""": self.skipfooter,
"""doublequote""": self.doublequote,
"""memory_map""": self.memory_map,
"""float_precision""": self.float_precision,
"""chunksize""": self.chunksize,
"""encoding_errors""": self.encoding_errors,
"""on_bad_lines""": self.on_bad_lines,
"""date_format""": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() , lowerCAmelCase):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class snake_case ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
snake_case_ : Union[str, Any] = CsvConfig
def UpperCamelCase_ ( self : str) -> List[str]:
"""simple docstring"""
return datasets.DatasetInfo(features=self.config.features)
def UpperCamelCase_ ( self : Optional[Any] , lowerCAmelCase : Union[str, Any]) -> List[Any]:
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}''')
_snake_case : Union[str, Any] = dl_manager.download_and_extract(self.config.data_files)
if isinstance(lowerCAmelCase , (str, list, tuple)):
_snake_case : int = data_files
if isinstance(lowerCAmelCase , lowerCAmelCase):
_snake_case : int = [files]
_snake_case : int = [dl_manager.iter_files(lowerCAmelCase) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files})]
_snake_case : Union[str, Any] = []
for split_name, files in data_files.items():
if isinstance(lowerCAmelCase , lowerCAmelCase):
_snake_case : List[str] = [files]
_snake_case : Any = [dl_manager.iter_files(lowerCAmelCase) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCAmelCase , gen_kwargs={"""files""": files}))
return splits
def UpperCamelCase_ ( self : Optional[Any] , lowerCAmelCase : pa.Table) -> pa.Table:
"""simple docstring"""
if self.config.features is not None:
_snake_case : List[str] = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCAmelCase) for feature in self.config.features.values()):
# cheaper cast
_snake_case : Optional[Any] = pa.Table.from_arrays([pa_table[field.name] for field in schema] , schema=lowerCAmelCase)
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
_snake_case : Dict = table_cast(lowerCAmelCase , lowerCAmelCase)
return pa_table
def UpperCamelCase_ ( self : str , lowerCAmelCase : str) -> Dict:
"""simple docstring"""
_snake_case : Union[str, Any] = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
_snake_case : Optional[Any] = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase) else object
for name, dtype, feature in zip(schema.names , schema.types , self.config.features.values())
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase)):
_snake_case : str = pd.read_csv(lowerCAmelCase , iterator=lowerCAmelCase , dtype=lowerCAmelCase , **self.config.pd_read_csv_kwargs)
try:
for batch_idx, df in enumerate(lowerCAmelCase):
_snake_case : List[Any] = pa.Table.from_pandas(lowerCAmelCase)
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase)
except ValueError as e:
logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCAmelCase)}: {e}''')
raise
| 317
| 1
|
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
a__ : Any = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
journal = {Transactions of the Association for Computational Linguistics},
volume = {4},
year={2016},
url = {https://www.aclweb.org/anthology/Q16-1029},
pages = {401--415
},
@inproceedings{post-2018-call,
title = "A Call for Clarity in Reporting {BLEU} Scores",
author = "Post, Matt",
booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
month = oct,
year = "2018",
address = "Belgium, Brussels",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-6319",
pages = "186--191",
}
'''
a__ : List[Any] = '''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''
a__ : Tuple = '''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
sources: list of source sentences where each sentence should be a string.
predictions: list of predicted sentences where each sentence should be a string.
references: list of lists of reference sentences where each sentence should be a string.
Returns:
sari: sari score
sacrebleu: sacrebleu score
exact: exact score
Examples:
>>> sources=["About 95 species are currently accepted ."]
>>> predictions=["About 95 you now get in ."]
>>> references=[["About 95 species are currently known ."]]
>>> wiki_split = datasets.load_metric("wiki_split")
>>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
>>> print(results)
{\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
def remove_articles(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = re.compile(R"\b(a|an|the)\b" , re.UNICODE )
return re.sub(lowerCAmelCase_ , " " , lowerCAmelCase_ )
def white_space_fix(lowerCAmelCase_ ):
return " ".join(text.split() )
def remove_punc(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowerCAmelCase_ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase_ ) ) ) )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return int(normalize_answer(lowerCAmelCase_ ) == normalize_answer(lowerCAmelCase_ ) )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [any(compute_exact(lowerCAmelCase_ , lowerCAmelCase_ ) for ref in refs ) for pred, refs in zip(lowerCAmelCase_ , lowerCAmelCase_ )]
return (sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ )) * 100
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = [rgram for rgrams in rgramslist for rgram in rgrams]
__SCREAMING_SNAKE_CASE = Counter(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = Counter(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = Counter()
for sgram, scount in sgramcounter.items():
__SCREAMING_SNAKE_CASE = scount * numref
__SCREAMING_SNAKE_CASE = Counter(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = Counter()
for cgram, ccount in cgramcounter.items():
__SCREAMING_SNAKE_CASE = ccount * numref
# KEEP
__SCREAMING_SNAKE_CASE = sgramcounter_rep & cgramcounter_rep
__SCREAMING_SNAKE_CASE = keepgramcounter_rep & rgramcounter
__SCREAMING_SNAKE_CASE = sgramcounter_rep & rgramcounter
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 1
if len(lowerCAmelCase_ ) > 0:
__SCREAMING_SNAKE_CASE = keeptmpscorea / len(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
__SCREAMING_SNAKE_CASE = keeptmpscorea / sum(keepgramcounterall_rep.values() )
__SCREAMING_SNAKE_CASE = 0
if keepscore_precision > 0 or keepscore_recall > 0:
__SCREAMING_SNAKE_CASE = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
__SCREAMING_SNAKE_CASE = sgramcounter_rep - cgramcounter_rep
__SCREAMING_SNAKE_CASE = delgramcounter_rep - rgramcounter
__SCREAMING_SNAKE_CASE = sgramcounter_rep - rgramcounter
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__SCREAMING_SNAKE_CASE = 1
if len(lowerCAmelCase_ ) > 0:
__SCREAMING_SNAKE_CASE = deltmpscorea / len(lowerCAmelCase_ )
# ADDITION
__SCREAMING_SNAKE_CASE = set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = set(lowerCAmelCase_ ) & set(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = set(lowerCAmelCase_ ) - set(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 1
if len(lowerCAmelCase_ ) > 0:
__SCREAMING_SNAKE_CASE = addtmpscore / len(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
__SCREAMING_SNAKE_CASE = addtmpscore / len(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = 0
if addscore_precision > 0 or addscore_recall > 0:
__SCREAMING_SNAKE_CASE = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = ssent.split(" " )
__SCREAMING_SNAKE_CASE = csent.split(" " )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for rsent in rsents:
__SCREAMING_SNAKE_CASE = rsent.split(" " )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
ragramslist.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
__SCREAMING_SNAKE_CASE = ragrams[i] + " " + ragrams[i + 1]
ragrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
__SCREAMING_SNAKE_CASE = ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
ragrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
__SCREAMING_SNAKE_CASE = ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
ragrams.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
ragramslist.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
__SCREAMING_SNAKE_CASE = sagrams[i] + " " + sagrams[i + 1]
sagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
__SCREAMING_SNAKE_CASE = sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
sagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
__SCREAMING_SNAKE_CASE = sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
sagrams.append(lowerCAmelCase_ )
for i in range(0 , len(lowerCAmelCase_ ) - 1 ):
if i < len(lowerCAmelCase_ ) - 1:
__SCREAMING_SNAKE_CASE = cagrams[i] + " " + cagrams[i + 1]
cagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 2:
__SCREAMING_SNAKE_CASE = cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
cagrams.append(lowerCAmelCase_ )
if i < len(lowerCAmelCase_ ) - 3:
__SCREAMING_SNAKE_CASE = cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
cagrams.append(lowerCAmelCase_ )
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) = SARIngram(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
__SCREAMING_SNAKE_CASE = sum([delascore, delascore, delascore, delascore] ) / 4
__SCREAMING_SNAKE_CASE = sum([addascore, addascore, addascore, addascore] ) / 4
__SCREAMING_SNAKE_CASE = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ = True , lowerCAmelCase_ = "13a" , lowerCAmelCase_ = True ):
'''simple docstring'''
if lowercase:
__SCREAMING_SNAKE_CASE = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
__SCREAMING_SNAKE_CASE = sacrebleu.metrics.bleu._get_tokenizer(lowerCAmelCase_ )()(lowerCAmelCase_ )
else:
__SCREAMING_SNAKE_CASE = sacrebleu.TOKENIZERS[tokenizer]()(lowerCAmelCase_ )
elif tokenizer == "moses":
__SCREAMING_SNAKE_CASE = sacremoses.MosesTokenizer().tokenize(lowerCAmelCase_ , return_str=lowerCAmelCase_ , escape=lowerCAmelCase_ )
elif tokenizer == "penn":
__SCREAMING_SNAKE_CASE = sacremoses.MosesTokenizer().penn_tokenize(lowerCAmelCase_ , return_str=lowerCAmelCase_ )
else:
__SCREAMING_SNAKE_CASE = sentence
if not return_str:
__SCREAMING_SNAKE_CASE = normalized_sent.split()
return normalized_sent
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if not (len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )):
raise ValueError("Sources length must match predictions and references lengths." )
__SCREAMING_SNAKE_CASE = 0
for src, pred, refs in zip(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
sari_score += SARIsent(normalize(lowerCAmelCase_ ) , normalize(lowerCAmelCase_ ) , [normalize(lowerCAmelCase_ ) for sent in refs] )
__SCREAMING_SNAKE_CASE = sari_score / len(lowerCAmelCase_ )
return 100 * sari_score
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_="exp" , lowerCAmelCase_=None , lowerCAmelCase_=False , lowerCAmelCase_=False , lowerCAmelCase_=False , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = len(references[0] )
if any(len(lowerCAmelCase_ ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
__SCREAMING_SNAKE_CASE = [[refs[i] for refs in references] for i in range(lowerCAmelCase_ )]
__SCREAMING_SNAKE_CASE = sacrebleu.corpus_bleu(
lowerCAmelCase_ , lowerCAmelCase_ , smooth_method=lowerCAmelCase_ , smooth_value=lowerCAmelCase_ , force=lowerCAmelCase_ , lowercase=lowerCAmelCase_ , use_effective_order=lowerCAmelCase_ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class UpperCamelCase_ ( datasets.Metric):
"""simple docstring"""
def UpperCAmelCase_ ( self : str ) -> Tuple:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Sequence(datasets.Value("string" , id="sequence" ) , id="references" ),
} ) , codebase_urls=[
"https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
"https://github.com/cocoxu/simplification/blob/master/SARI.py",
"https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
"https://github.com/mjpost/sacreBLEU",
] , reference_urls=[
"https://www.aclweb.org/anthology/Q16-1029.pdf",
"https://github.com/mjpost/sacreBLEU",
"https://en.wikipedia.org/wiki/BLEU",
"https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
] , )
def UpperCAmelCase_ ( self : List[Any] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[str] ) -> str:
__SCREAMING_SNAKE_CASE = {}
result.update({"sari": compute_sari(sources=UpperCAmelCase__ , predictions=UpperCAmelCase__ , references=UpperCAmelCase__ )} )
result.update({"sacrebleu": compute_sacrebleu(predictions=UpperCAmelCase__ , references=UpperCAmelCase__ )} )
result.update({"exact": compute_em(predictions=UpperCAmelCase__ , references=UpperCAmelCase__ )} )
return result
| 195
|
"""simple docstring"""
a__ : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def UpperCAmelCase__ ():
'''simple docstring'''
__SCREAMING_SNAKE_CASE = input("Enter message: " )
__SCREAMING_SNAKE_CASE = input("Enter key [alphanumeric]: " )
__SCREAMING_SNAKE_CASE = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e" ):
__SCREAMING_SNAKE_CASE = "encrypt"
__SCREAMING_SNAKE_CASE = encrypt_message(lowerCAmelCase_ , lowerCAmelCase_ )
elif mode.lower().startswith("d" ):
__SCREAMING_SNAKE_CASE = "decrypt"
__SCREAMING_SNAKE_CASE = decrypt_message(lowerCAmelCase_ , lowerCAmelCase_ )
print(f"""\n{mode.title()}ed message:""" )
print(lowerCAmelCase_ )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return translate_message(lowerCAmelCase_ , lowerCAmelCase_ , "encrypt" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
return translate_message(lowerCAmelCase_ , lowerCAmelCase_ , "decrypt" )
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = key.upper()
for symbol in message:
__SCREAMING_SNAKE_CASE = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(lowerCAmelCase_ )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(lowerCAmelCase_ ):
__SCREAMING_SNAKE_CASE = 0
else:
translated.append(lowerCAmelCase_ )
return "".join(lowerCAmelCase_ )
if __name__ == "__main__":
main()
| 195
| 1
|
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = sorted(string.lower() )
return len(lowerCamelCase_ ) == len(set(lowerCamelCase_ ) )
if __name__ == "__main__":
__UpperCAmelCase = input("""Enter a string """).strip()
__UpperCAmelCase = is_isogram(input_str)
print(f'''{input_str} is {'an' if isogram else 'not an'} isogram.''')
| 323
|
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : Optional[int] , **__lowerCamelCase : Optional[int] ):
requires_backends(self , ["""bs4"""] )
super().__init__(**__lowerCamelCase )
def _A ( self : List[str] , __lowerCamelCase : Any ):
UpperCamelCase :Optional[int] = []
UpperCamelCase :List[str] = []
UpperCamelCase :Union[str, Any] = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
UpperCamelCase :Optional[Any] = parent.find_all(child.name , recursive=__lowerCamelCase )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(__lowerCamelCase ) else next(i for i, s in enumerate(__lowerCamelCase , 1 ) if s is child ) )
UpperCamelCase :Any = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def _A ( self : Any , __lowerCamelCase : Tuple ):
UpperCamelCase :Any = BeautifulSoup(__lowerCamelCase , """html.parser""" )
UpperCamelCase :Union[str, Any] = []
UpperCamelCase :Tuple = []
UpperCamelCase :Tuple = []
for element in html_code.descendants:
if type(__lowerCamelCase ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
UpperCamelCase :Any = html.unescape(__lowerCamelCase ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(__lowerCamelCase )
UpperCamelCase , UpperCamelCase :Optional[Any] = self.xpath_soup(__lowerCamelCase )
stringaxtag_seq.append(__lowerCamelCase )
stringaxsubs_seq.append(__lowerCamelCase )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(__lowerCamelCase ) != len(__lowerCamelCase ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def _A ( self : int , __lowerCamelCase : List[Any] , __lowerCamelCase : List[str] ):
UpperCamelCase :Tuple = """"""
for tagname, subs in zip(__lowerCamelCase , __lowerCamelCase ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self : Any , __lowerCamelCase : Dict ):
UpperCamelCase :Any = False
# Check that strings has a valid type
if isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCamelCase :List[Any] = True
elif isinstance(__lowerCamelCase , (list, tuple) ):
if len(__lowerCamelCase ) == 0 or isinstance(html_strings[0] , __lowerCamelCase ):
UpperCamelCase :Any = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
F"""but is of type {type(__lowerCamelCase )}.""" )
UpperCamelCase :str = bool(isinstance(__lowerCamelCase , (list, tuple) ) and (isinstance(html_strings[0] , __lowerCamelCase )) )
if not is_batched:
UpperCamelCase :Any = [html_strings]
# Get nodes + xpaths
UpperCamelCase :Union[str, Any] = []
UpperCamelCase :str = []
for html_string in html_strings:
UpperCamelCase , UpperCamelCase , UpperCamelCase :int = self.get_three_from_single(__lowerCamelCase )
nodes.append(__lowerCamelCase )
UpperCamelCase :int = []
for node, tag_list, sub_list in zip(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
UpperCamelCase :str = self.construct_xpath(__lowerCamelCase , __lowerCamelCase )
xpath_strings.append(__lowerCamelCase )
xpaths.append(__lowerCamelCase )
# return as Dict
UpperCamelCase :Optional[int] = {"""nodes""": nodes, """xpaths""": xpaths}
UpperCamelCase :Any = BatchFeature(data=__lowerCamelCase , tensor_type=__lowerCamelCase )
return encoded_inputs
| 38
| 0
|
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 (SCREAMING_SNAKE_CASE )-> Optional[Any]:
"""simple docstring"""
return EnvironmentCommand()
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> str:
"""simple docstring"""
return EnvironmentCommand(args.accelerate_config_file )
class lowerCAmelCase_ ( lowerCamelCase__ ):
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( _UpperCAmelCase ):
snake_case_ = parser.add_parser('''env''' )
download_parser.set_defaults(func=_UpperCAmelCase )
download_parser.add_argument(
'''--accelerate-config_file''' , default=_UpperCAmelCase , help='''The accelerate config file to use for the default values in the launching script.''' , )
download_parser.set_defaults(func=_UpperCAmelCase )
def __init__( self , _UpperCAmelCase , *_UpperCAmelCase ):
snake_case_ = accelerate_config_file
def UpperCamelCase__ ( self ):
snake_case_ = '''not installed'''
if is_safetensors_available():
import safetensors
snake_case_ = safetensors.__version__
elif importlib.util.find_spec('''safetensors''' ) is not None:
import safetensors
snake_case_ = F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''
snake_case_ = '''not installed'''
snake_case_ = snake_case_ = '''not found'''
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
snake_case_ = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(_UpperCAmelCase ):
snake_case_ = load_config_from_file(self._accelerate_config_file ).to_dict()
snake_case_ = (
'''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(_UpperCAmelCase , _UpperCAmelCase )
else F'''\t{accelerate_config}'''
)
snake_case_ = '''not installed'''
snake_case_ = '''NA'''
if is_torch_available():
import torch
snake_case_ = torch.__version__
snake_case_ = torch.cuda.is_available()
snake_case_ = '''not installed'''
snake_case_ = '''NA'''
if is_tf_available():
import tensorflow as tf
snake_case_ = tf.__version__
try:
# deprecated in v2.1
snake_case_ = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
snake_case_ = bool(tf.config.list_physical_devices('''GPU''' ) )
snake_case_ = '''not installed'''
snake_case_ = '''not installed'''
snake_case_ = '''not installed'''
snake_case_ = '''NA'''
if is_flax_available():
import flax
import jax
import jaxlib
snake_case_ = flax.__version__
snake_case_ = jax.__version__
snake_case_ = jaxlib.__version__
snake_case_ = jax.lib.xla_bridge.get_backend().platform
snake_case_ = {
'''`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(_UpperCAmelCase ) )
return info
@staticmethod
def UpperCamelCase__ ( _UpperCAmelCase ):
return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
| 358
|
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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 = get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
__snake_case = PegasusTokenizer
__snake_case = PegasusTokenizerFast
__snake_case = True
__snake_case = True
def UpperCamelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ = PegasusTokenizer(_UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCamelCase__ ( self ):
return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )
def UpperCamelCase__ ( self , **_UpperCAmelCase ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def UpperCamelCase__ ( self , _UpperCAmelCase ):
return ("This is a test", "This is a test")
def UpperCamelCase__ ( self ):
snake_case_ = '''</s>'''
snake_case_ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase ) , _UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase ) , _UpperCAmelCase )
def UpperCamelCase__ ( self ):
snake_case_ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''</s>''' )
self.assertEqual(vocab_keys[-1] , '''v''' )
self.assertEqual(len(_UpperCAmelCase ) , 11_03 )
def UpperCamelCase__ ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 11_03 )
def UpperCamelCase__ ( self ):
snake_case_ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
snake_case_ = self.tokenizer_class.from_pretrained(self.tmpdirname )
snake_case_ = (
'''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
''' </s> <pad> <pad> <pad>'''
)
snake_case_ = rust_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
snake_case_ = py_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase__ ( self ):
snake_case_ = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
snake_case_ = '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
snake_case_ = [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
snake_case_ = tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase__ ( self ):
snake_case_ = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_61_03
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_03
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 10_24
snake_case_ = '''To ensure a smooth flow of bank resolutions.'''
snake_case_ = [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
snake_case_ = tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def UpperCamelCase__ ( self ):
snake_case_ = ['''This is going to be way too long.''' * 1_50, '''short example''']
snake_case_ = ['''not super long but more than 5 tokens''', '''tiny''']
snake_case_ = self._large_tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors='''pt''' )
snake_case_ = self._large_tokenizer(
text_target=_UpperCAmelCase , max_length=5 , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 10_24)
assert batch.attention_mask.shape == (2, 10_24)
assert targets["input_ids"].shape == (2, 5)
assert len(_UpperCAmelCase ) == 2 # input_ids, attention_mask.
@slow
def UpperCamelCase__ ( self ):
# fmt: off
snake_case_ = {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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-pegasus-large-arxiv''' , revision='''ba85d0851d708441f91440d509690f1ab6353415''' , )
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_ ( lowerCamelCase__ , unittest.TestCase ):
'''simple docstring'''
__snake_case = PegasusTokenizer
__snake_case = PegasusTokenizerFast
__snake_case = True
__snake_case = True
def UpperCamelCase__ ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
snake_case_ = PegasusTokenizer(_UpperCAmelCase , offset=0 , mask_token_sent=_UpperCAmelCase , mask_token='''[MASK]''' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCamelCase__ ( self ):
return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )
def UpperCamelCase__ ( self , **_UpperCAmelCase ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
def UpperCamelCase__ ( self , _UpperCAmelCase ):
return ("This is a test", "This is a test")
def UpperCamelCase__ ( self ):
snake_case_ = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
snake_case_ = self.tokenizer_class.from_pretrained(self.tmpdirname )
snake_case_ = (
'''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
''' <pad> <pad> <pad>'''
)
snake_case_ = rust_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
snake_case_ = py_tokenizer([raw_input_str] , return_tensors=_UpperCAmelCase , add_special_tokens=_UpperCAmelCase ).input_ids[0]
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
@require_torch
def UpperCamelCase__ ( self ):
snake_case_ = ['''This is going to be way too long.''' * 10_00, '''short example''']
snake_case_ = ['''not super long but more than 5 tokens''', '''tiny''']
snake_case_ = self._large_tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors='''pt''' )
snake_case_ = self._large_tokenizer(
text_target=_UpperCAmelCase , max_length=5 , padding=_UpperCAmelCase , truncation=_UpperCAmelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 40_96)
assert batch.attention_mask.shape == (2, 40_96)
assert targets["input_ids"].shape == (2, 5)
assert len(_UpperCAmelCase ) == 2 # input_ids, attention_mask.
def UpperCamelCase__ ( self ):
snake_case_ = (
'''This is an example string that is used to test the original TF implementation against the HF'''
''' implementation'''
)
snake_case_ = self._large_tokenizer(_UpperCAmelCase ).input_ids
self.assertListEqual(
_UpperCAmelCase , [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1] , )
| 267
| 0
|
'''simple docstring'''
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 ):
lowerCamelCase : Optional[int] = TransfoXLTokenizer
lowerCamelCase : Any = False
lowerCamelCase : Union[str, Any] = False
def UpperCAmelCase__ (self ):
super().setUp()
lowerCamelCase_ : str = [
'''<unk>''',
'''[CLS]''',
'''[SEP]''',
'''want''',
'''unwanted''',
'''wa''',
'''un''',
'''running''',
''',''',
'''low''',
'''l''',
]
lowerCamelCase_ : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
def UpperCAmelCase__ (self , **A ):
lowerCamelCase_ : Optional[int] = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **A )
def UpperCAmelCase__ (self , A ):
lowerCamelCase_ : int = '''<unk> UNwanted , running'''
lowerCamelCase_ : str = '''<unk> unwanted, running'''
return input_text, output_text
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Optional[int] = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=A )
lowerCamelCase_ : Dict = tokenizer.tokenize('''<unk> UNwanted , running''' )
self.assertListEqual(A , ['''<unk>''', '''unwanted''', ''',''', '''running'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , [0, 4, 8, 7] )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Optional[Any] = TransfoXLTokenizer(lower_case=A )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Union[str, Any] = TransfoXLTokenizer(lower_case=A )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo ! how \n Are yoU ? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Optional[int] = TransfoXLTokenizer(lower_case=A )
lowerCamelCase_ : Dict = '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
lowerCamelCase_ : List[Any] = [
'''Hello''',
'''(''',
'''bracket''',
''')''',
'''and''',
'''side''',
'''@-@''',
'''scrolled''',
'''[''',
'''and''',
''']''',
'''Henry''',
'''\'s''',
'''$''',
'''5''',
'''@,@''',
'''000''',
'''with''',
'''3''',
'''@.@''',
'''34''',
'''m''',
'''.''',
'''What''',
'''\'s''',
'''up''',
'''!''',
'''?''',
]
self.assertListEqual(tokenizer.tokenize(A ) , A )
self.assertEqual(tokenizer.convert_tokens_to_string(A ) , A )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : str = self.get_tokenizer()
lowerCamelCase_ : List[str] = len(A )
tokenizer.add_tokens(['''new1''', '''new2'''] )
tokenizer.move_added_token('''new1''' , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(A ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode('''new1''' ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , '''new1''' )
| 318
|
'''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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__lowercase : int = logging.get_logger(__name__)
def lowercase_ ( _lowercase ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ : Optional[int] = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
lowerCamelCase_ : Optional[Any] = [144, 192, 240]
lowerCamelCase_ : Optional[Any] = [16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
lowerCamelCase_ : List[str] = [96, 120, 144]
lowerCamelCase_ : Union[str, Any] = [16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
lowerCamelCase_ : Any = [64, 80, 96]
lowerCamelCase_ : List[str] = [16, 16, 24, 48, 64, 80, 320]
lowerCamelCase_ : Union[str, Any] = 0.05
lowerCamelCase_ : Union[str, Any] = 2.0
if mobilevit_name.startswith('''deeplabv3_''' ):
lowerCamelCase_ : Optional[Any] = 512
lowerCamelCase_ : Dict = 16
lowerCamelCase_ : Dict = 21
lowerCamelCase_ : List[Any] = '''pascal-voc-id2label.json'''
else:
lowerCamelCase_ : Any = 1_000
lowerCamelCase_ : Dict = '''imagenet-1k-id2label.json'''
lowerCamelCase_ : Optional[Any] = '''huggingface/label-files'''
lowerCamelCase_ : int = json.load(open(hf_hub_download(_lowercase , _lowercase , repo_type='''dataset''' ) , '''r''' ) )
lowerCamelCase_ : List[Any] = {int(_lowercase ): v for k, v in idalabel.items()}
lowerCamelCase_ : List[str] = idalabel
lowerCamelCase_ : str = {v: k for k, v in idalabel.items()}
return config
def lowercase_ ( _lowercase , _lowercase=False ) -> List[str]:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
lowerCamelCase_ : Union[str, Any] = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
lowerCamelCase_ : Optional[Any] = name.replace('''conv_1.''' , '''conv_stem.''' )
if ".block." in name:
lowerCamelCase_ : Optional[int] = name.replace('''.block.''' , '''.''' )
if "exp_1x1" in name:
lowerCamelCase_ : Optional[int] = name.replace('''exp_1x1''' , '''expand_1x1''' )
if "red_1x1" in name:
lowerCamelCase_ : int = name.replace('''red_1x1''' , '''reduce_1x1''' )
if ".local_rep.conv_3x3." in name:
lowerCamelCase_ : Dict = name.replace('''.local_rep.conv_3x3.''' , '''.conv_kxk.''' )
if ".local_rep.conv_1x1." in name:
lowerCamelCase_ : Tuple = name.replace('''.local_rep.conv_1x1.''' , '''.conv_1x1.''' )
if ".norm." in name:
lowerCamelCase_ : Dict = name.replace('''.norm.''' , '''.normalization.''' )
if ".conv." in name:
lowerCamelCase_ : Union[str, Any] = name.replace('''.conv.''' , '''.convolution.''' )
if ".conv_proj." in name:
lowerCamelCase_ : List[str] = name.replace('''.conv_proj.''' , '''.conv_projection.''' )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowerCamelCase_ : Dict = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
lowerCamelCase_ : str = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
lowerCamelCase_ : str = name.replace('''expand_1x1''' , '''downsampling_layer.expand_1x1''' )
if "conv_3x3" in name:
lowerCamelCase_ : List[str] = name.replace('''conv_3x3''' , '''downsampling_layer.conv_3x3''' )
if "reduce_1x1" in name:
lowerCamelCase_ : Optional[int] = name.replace('''reduce_1x1''' , '''downsampling_layer.reduce_1x1''' )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
lowerCamelCase_ : Optional[Any] = name.replace(F""".global_rep.{i}.weight""" , '''.layernorm.weight''' )
if F""".global_rep.{i}.bias""" in name:
lowerCamelCase_ : Any = name.replace(F""".global_rep.{i}.bias""" , '''.layernorm.bias''' )
if ".global_rep." in name:
lowerCamelCase_ : List[str] = name.replace('''.global_rep.''' , '''.transformer.''' )
if ".pre_norm_mha.0." in name:
lowerCamelCase_ : List[str] = name.replace('''.pre_norm_mha.0.''' , '''.layernorm_before.''' )
if ".pre_norm_mha.1.out_proj." in name:
lowerCamelCase_ : int = name.replace('''.pre_norm_mha.1.out_proj.''' , '''.attention.output.dense.''' )
if ".pre_norm_ffn.0." in name:
lowerCamelCase_ : Any = name.replace('''.pre_norm_ffn.0.''' , '''.layernorm_after.''' )
if ".pre_norm_ffn.1." in name:
lowerCamelCase_ : str = name.replace('''.pre_norm_ffn.1.''' , '''.intermediate.dense.''' )
if ".pre_norm_ffn.4." in name:
lowerCamelCase_ : str = name.replace('''.pre_norm_ffn.4.''' , '''.output.dense.''' )
if ".transformer." in name:
lowerCamelCase_ : Optional[int] = name.replace('''.transformer.''' , '''.transformer.layer.''' )
if ".aspp_layer." in name:
lowerCamelCase_ : str = name.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in name:
lowerCamelCase_ : Union[str, Any] = name.replace('''.aspp_pool.''' , '''.''' )
if "seg_head." in name:
lowerCamelCase_ : int = name.replace('''seg_head.''' , '''segmentation_head.''' )
if "segmentation_head.classifier.classifier." in name:
lowerCamelCase_ : List[Any] = name.replace('''segmentation_head.classifier.classifier.''' , '''segmentation_head.classifier.''' )
if "classifier.fc." in name:
lowerCamelCase_ : Tuple = name.replace('''classifier.fc.''' , '''classifier.''' )
elif (not base_model) and ("segmentation_head." not in name):
lowerCamelCase_ : Tuple = '''mobilevit.''' + name
return name
def lowercase_ ( _lowercase , _lowercase , _lowercase=False ) -> Tuple:
'''simple docstring'''
if base_model:
lowerCamelCase_ : List[str] = ''''''
else:
lowerCamelCase_ : Any = '''mobilevit.'''
for key in orig_state_dict.copy().keys():
lowerCamelCase_ : Dict = orig_state_dict.pop(_lowercase )
if key[:8] == "encoder.":
lowerCamelCase_ : int = key[8:]
if "qkv" in key:
lowerCamelCase_ : List[Any] = key.split('''.''' )
lowerCamelCase_ : Optional[Any] = int(key_split[0][6:] ) - 1
lowerCamelCase_ : Union[str, Any] = int(key_split[3] )
lowerCamelCase_ : Any = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
lowerCamelCase_ : Dict = layer.transformer.layer[transformer_num].attention.attention.all_head_size
lowerCamelCase_ : Optional[Any] = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
lowerCamelCase_ : List[str] = val[:dim, :]
lowerCamelCase_ : Dict = val[dim : dim * 2, :]
lowerCamelCase_ : Union[str, Any] = val[-dim:, :]
else:
lowerCamelCase_ : List[Any] = val[:dim]
lowerCamelCase_ : Optional[int] = val[dim : dim * 2]
lowerCamelCase_ : int = val[-dim:]
else:
lowerCamelCase_ : int = val
return orig_state_dict
def lowercase_ ( ) -> str:
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCamelCase_ : Optional[Any] = Image.open(requests.get(_lowercase , stream=_lowercase ).raw )
return im
@torch.no_grad()
def lowercase_ ( _lowercase , _lowercase , _lowercase , _lowercase=False ) -> Tuple:
'''simple docstring'''
lowerCamelCase_ : Tuple = get_mobilevit_config(_lowercase )
# load original state_dict
lowerCamelCase_ : int = torch.load(_lowercase , map_location='''cpu''' )
# load 🤗 model
if mobilevit_name.startswith('''deeplabv3_''' ):
lowerCamelCase_ : int = MobileViTForSemanticSegmentation(_lowercase ).eval()
else:
lowerCamelCase_ : int = MobileViTForImageClassification(_lowercase ).eval()
lowerCamelCase_ : Optional[Any] = convert_state_dict(_lowercase , _lowercase )
model.load_state_dict(_lowercase )
# Check outputs on an image, prepared by MobileViTImageProcessor
lowerCamelCase_ : str = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
lowerCamelCase_ : Tuple = image_processor(images=prepare_img() , return_tensors='''pt''' )
lowerCamelCase_ : Optional[int] = model(**_lowercase )
lowerCamelCase_ : List[str] = outputs.logits
if mobilevit_name.startswith('''deeplabv3_''' ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
lowerCamelCase_ : Union[str, Any] = torch.tensor(
[
[[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
[[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
[[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
lowerCamelCase_ : Dict = torch.tensor(
[
[[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
[[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
[[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
lowerCamelCase_ : List[str] = torch.tensor(
[
[[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
[[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
[[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _lowercase , atol=1e-4 )
else:
assert logits.shape == (1, 1_000)
if mobilevit_name == "mobilevit_s":
lowerCamelCase_ : Optional[Any] = torch.tensor([-0.98_66, 0.23_92, -1.12_41] )
elif mobilevit_name == "mobilevit_xs":
lowerCamelCase_ : Tuple = torch.tensor([-2.47_61, -0.93_99, -1.95_87] )
elif mobilevit_name == "mobilevit_xxs":
lowerCamelCase_ : List[Any] = torch.tensor([-1.93_64, -1.23_27, -0.46_53] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _lowercase , atol=1e-4 )
Path(_lowercase ).mkdir(exist_ok=_lowercase )
print(F"""Saving model {mobilevit_name} 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 push_to_hub:
lowerCamelCase_ : str = {
'''mobilevit_s''': '''mobilevit-small''',
'''mobilevit_xs''': '''mobilevit-x-small''',
'''mobilevit_xxs''': '''mobilevit-xx-small''',
'''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
'''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
'''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
}
print('''Pushing to the hub...''' )
lowerCamelCase_ : int = model_mapping[mobilevit_name]
image_processor.push_to_hub(_lowercase , organization='''apple''' )
model.push_to_hub(_lowercase , organization='''apple''' )
if __name__ == "__main__":
__lowercase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
__lowercase : Optional[int] = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 318
| 1
|
_A : Optional[Any] ={
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
}
| 367
|
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_A : List[Any] =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 : Dict =parser.parse_args()
_A : List[str] =UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
_A : Any =CLIPImageProcessor()
_A : Union[str, Any] =CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')
_A : Union[str, Any] =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)
| 129
| 0
|
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowercase_ = 'src/transformers'
# This is to make sure the transformers module imported is the one in the repo.
lowercase_ = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
lowercase_ = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowercase_ = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase_ = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowercase_ = [
('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
(
'zero-shot-object-detection',
'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
'AutoModelForZeroShotObjectDetection',
),
('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
(
'table-question-answering',
'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForTableQuestionAnswering',
),
('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
(
'next-sentence-prediction',
'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
'AutoModelForNextSentencePrediction',
),
(
'audio-frame-classification',
'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForAudioFrameClassification',
),
('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
(
'document-question-answering',
'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForDocumentQuestionAnswering',
),
(
'visual-question-answering',
'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
'AutoModelForVisualQuestionAnswering',
),
('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
(
'zero-shot-image-classification',
'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
'AutoModelForZeroShotImageClassification',
),
('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def lowercase ( lowerCAmelCase__ : Any ) -> Optional[int]:
__a = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowerCAmelCase__ )
return [m.group(0 ) for m in matches]
def lowercase ( ) -> Optional[int]:
__a = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
__a = {
config.replace('''Config''' , '''''' ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
__a = collections.defaultdict(lowerCAmelCase__ )
__a = collections.defaultdict(lowerCAmelCase__ )
__a = collections.defaultdict(lowerCAmelCase__ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(lowerCAmelCase__ ):
__a = None
if _re_tf_models.match(lowerCAmelCase__ ) is not None:
__a = tf_models
__a = _re_tf_models.match(lowerCAmelCase__ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase__ ) is not None:
__a = flax_models
__a = _re_flax_models.match(lowerCAmelCase__ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase__ ) is not None:
__a = pt_models
__a = _re_pt_models.match(lowerCAmelCase__ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase__ ) > 0:
if attr_name in model_prefix_to_model_type:
__a = True
break
# Try again after removing the last word in the name
__a = ''.join(camel_case_split(lowerCAmelCase__ )[:-1] )
__a = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
__a = list(lowerCAmelCase__ )
all_models.sort()
__a = {'model_type': all_models}
__a = [pt_models[t] for t in all_models]
__a = [tf_models[t] for t in all_models]
__a = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
__a = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
__a = 'AutoProcessor'
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
__a = 'AutoTokenizer'
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
__a = 'AutoFeatureExtractor'
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
__a = 'AutoTokenizer'
__a = [processors[t] for t in all_models]
return pd.DataFrame(lowerCAmelCase__ )
def lowercase ( lowerCAmelCase__ : Optional[Any] ) -> List[str]:
__a = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
__a = [model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
__a = [auto_class, f'''TF_{auto_class}''', f'''Flax_{auto_class}''']
# Loop through all three frameworks
for module, cls, mapping in zip(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
# The type of pipeline may not exist in this framework
if not hasattr(lowerCAmelCase__ , lowerCAmelCase__ ):
continue
# First extract all model_names
__a = []
for name in getattr(lowerCAmelCase__ , lowerCAmelCase__ ).values():
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
model_names.append(lowerCAmelCase__ )
else:
model_names.extend(list(lowerCAmelCase__ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int] ) -> List[Any]:
__a = get_frameworks_table()
__a = Dataset.from_pandas(lowerCAmelCase__ )
__a = hf_hub_download(
'''huggingface/transformers-metadata''' , '''pipeline_tags.json''' , repo_type='''dataset''' , token=lowerCAmelCase__ )
__a = Dataset.from_json(lowerCAmelCase__ )
__a = {
tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
for i in range(len(lowerCAmelCase__ ) )
}
__a = update_pipeline_and_auto_class_table(lowerCAmelCase__ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
__a = sorted(table.keys() )
__a = pd.DataFrame(
{
'''model_class''': model_classes,
'''pipeline_tag''': [table[m][0] for m in model_classes],
'''auto_class''': [table[m][1] for m in model_classes],
} )
__a = Dataset.from_pandas(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(lowerCAmelCase__ , '''frameworks.json''' ) )
tags_dataset.to_json(os.path.join(lowerCAmelCase__ , '''pipeline_tags.json''' ) )
if commit_sha is not None:
__a = (
f'''Update with commit {commit_sha}\n\nSee: '''
f'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
)
else:
__a = 'Update'
upload_folder(
repo_id='''huggingface/transformers-metadata''' , folder_path=lowerCAmelCase__ , repo_type='''dataset''' , token=lowerCAmelCase__ , commit_message=lowerCAmelCase__ , )
def lowercase ( ) -> str:
__a = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
__a = transformers_module.pipelines.SUPPORTED_TASKS
__a = []
for key in pipeline_tasks:
if key not in in_table:
__a = pipeline_tasks[key]['pt']
if isinstance(lowerCAmelCase__ , (list, tuple) ):
__a = model[0]
__a = model.__name__
if model not in in_table.values():
missing.append(lowerCAmelCase__ )
if len(lowerCAmelCase__ ) > 0:
__a = ', '.join(lowerCAmelCase__ )
raise ValueError(
'''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
f'''`utils/update_metadata.py`: {msg}. Please add them!''' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
lowercase_ = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 45
|
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : str = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
lowerCAmelCase : Dict = {
'vocab_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
},
'merges_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
},
}
lowerCAmelCase : Dict = {
'allenai/longformer-base-4096': 40_96,
'allenai/longformer-large-4096': 40_96,
'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def A_ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = (
list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) )
)
SCREAMING_SNAKE_CASE_ : List[str] = bs[:]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
for b in range(2**8 ):
if b not in bs:
bs.append(a )
cs.append(2**8 + n )
n += 1
SCREAMING_SNAKE_CASE_ : int = [chr(a ) for n in cs]
return dict(zip(a , a ) )
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = set()
SCREAMING_SNAKE_CASE_ : Optional[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
SCREAMING_SNAKE_CASE_ : Any = char
return pairs
class _A ( __magic_name__):
SCREAMING_SNAKE_CASE : Tuple = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE : Any = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE : int = ['''input_ids''', '''attention_mask''']
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="replace" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="<mask>" , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else bos_token
SCREAMING_SNAKE_CASE_ : Dict = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else eos_token
SCREAMING_SNAKE_CASE_ : int = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else sep_token
SCREAMING_SNAKE_CASE_ : Any = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else cls_token
SCREAMING_SNAKE_CASE_ : Optional[Any] = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else unk_token
SCREAMING_SNAKE_CASE_ : Dict = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE_ : Any = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else mask_token
super().__init__(
errors=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as vocab_handle:
SCREAMING_SNAKE_CASE_ : List[str] = json.load(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Optional[int] = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE_ : int = errors # how to handle errors in decoding
SCREAMING_SNAKE_CASE_ : List[Any] = bytes_to_unicode()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {v: k for k, v in self.byte_encoder.items()}
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as merges_handle:
SCREAMING_SNAKE_CASE_ : Optional[int] = merges_handle.read().split('\n' )[1:-1]
SCREAMING_SNAKE_CASE_ : List[Any] = [tuple(merge.split() ) for merge in bpe_merges]
SCREAMING_SNAKE_CASE_ : Dict = dict(zip(_SCREAMING_SNAKE_CASE , range(len(_SCREAMING_SNAKE_CASE ) ) ) )
SCREAMING_SNAKE_CASE_ : Dict = {}
SCREAMING_SNAKE_CASE_ : List[Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
SCREAMING_SNAKE_CASE_ : Tuple = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' )
@property
def UpperCAmelCase ( self ):
"""simple docstring"""
return len(self.encoder )
def UpperCAmelCase ( self ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if token in self.cache:
return self.cache[token]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Dict = get_pairs(_SCREAMING_SNAKE_CASE )
if not pairs:
return token
while True:
SCREAMING_SNAKE_CASE_ : int = min(_SCREAMING_SNAKE_CASE , key=lambda _SCREAMING_SNAKE_CASE : self.bpe_ranks.get(_SCREAMING_SNAKE_CASE , float('inf' ) ) )
if bigram not in self.bpe_ranks:
break
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Any = bigram
SCREAMING_SNAKE_CASE_ : List[Any] = []
SCREAMING_SNAKE_CASE_ : List[Any] = 0
while i < len(_SCREAMING_SNAKE_CASE ):
try:
SCREAMING_SNAKE_CASE_ : Any = word.index(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
SCREAMING_SNAKE_CASE_ : Tuple = j
if word[i] == first and i < len(_SCREAMING_SNAKE_CASE ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
SCREAMING_SNAKE_CASE_ : str = tuple(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Tuple = new_word
if len(_SCREAMING_SNAKE_CASE ) == 1:
break
else:
SCREAMING_SNAKE_CASE_ : Any = get_pairs(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : List[Any] = ' '.join(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Tuple = word
return word
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = []
for token in re.findall(self.pat , _SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ''.join(
self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE ).split(' ' ) )
return bpe_tokens
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return self.encoder.get(_SCREAMING_SNAKE_CASE , self.encoder.get(self.unk_token ) )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return self.decoder.get(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ''.join(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : str = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors )
return text
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
SCREAMING_SNAKE_CASE_ : Tuple = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE_ : Optional[Any] = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )
with open(_SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_SCREAMING_SNAKE_CASE , ensure_ascii=_SCREAMING_SNAKE_CASE ) + '\n' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
with open(_SCREAMING_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 _SCREAMING_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!' )
SCREAMING_SNAKE_CASE_ : List[Any] = token_index
writer.write(' '.join(_SCREAMING_SNAKE_CASE ) + '\n' )
index += 1
return vocab_file, merge_file
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : Optional[int] = [self.cls_token_id]
SCREAMING_SNAKE_CASE_ : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = kwargs.pop('add_prefix_space' , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_SCREAMING_SNAKE_CASE ) > 0 and not text[0].isspace()):
SCREAMING_SNAKE_CASE_ : List[Any] = ' ' + text
return (text, kwargs)
| 253
| 0
|
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def _a ( SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = []
for line in lines:
__lowerCAmelCase = re.sub(R"#.*" , "" , _a ) # remove comments
if line:
filtered_lines.append(_a )
__lowerCAmelCase = """\n""".join(_a )
# Make a hash from all this code
__lowerCAmelCase = full_str.encode("utf-8" )
return shaaaa(_a ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase__ = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCamelCase__ = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCamelCase__ = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
UpperCamelCase__ = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 365
|
from pathlib import Path
import fire
from tqdm import tqdm
def _a ( SCREAMING_SNAKE_CASE_ : Dict="ro" , SCREAMING_SNAKE_CASE_ : Union[str, Any]="en" , SCREAMING_SNAKE_CASE_ : Optional[Any]="wmt16" , SCREAMING_SNAKE_CASE_ : List[str]=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install datasets" )
__lowerCAmelCase = F"""{src_lang}-{tgt_lang}"""
print(F"""Converting {dataset}-{pair}""" )
__lowerCAmelCase = datasets.load_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if save_dir is None:
__lowerCAmelCase = F"""{dataset}-{pair}"""
__lowerCAmelCase = Path(SCREAMING_SNAKE_CASE_ )
save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
for split in ds.keys():
print(F"""Splitting {split} with {ds[split].num_rows} records""" )
# to save to val.source, val.target like summary datasets
__lowerCAmelCase = "val" if split == "validation" else split
__lowerCAmelCase = save_dir.joinpath(F"""{fn}.source""" )
__lowerCAmelCase = save_dir.joinpath(F"""{fn}.target""" )
__lowerCAmelCase = src_path.open("w+" )
__lowerCAmelCase = tgt_path.open("w+" )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
__lowerCAmelCase = x["translation"]
src_fp.write(ex[src_lang] + "\n" )
tgt_fp.write(ex[tgt_lang] + "\n" )
print(F"""Saved {dataset} dataset to {save_dir}""" )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 102
| 0
|
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase = True , _UpperCamelCase = math.inf , _UpperCamelCase = -math.inf , _UpperCamelCase = math.inf , _UpperCamelCase = -math.inf , _UpperCamelCase = False , _UpperCamelCase = 100 , _UpperCamelCase = 0.01 , _UpperCamelCase = 1 , ) -> Any:
"""simple docstring"""
snake_case_ : Any = False
snake_case_ : Tuple = search_prob
snake_case_ : Any = start_temperate
snake_case_ : Dict = []
snake_case_ : Union[str, Any] = 0
snake_case_ : List[Any] = None
while not search_end:
snake_case_ : str = current_state.score()
if best_state is None or current_score > best_state.score():
snake_case_ : List[str] = current_state
scores.append(_UpperCamelCase )
iterations += 1
snake_case_ : List[str] = None
snake_case_ : int = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
snake_case_ : int = random.randint(0 , len(_UpperCamelCase ) - 1 ) # picking a random neighbor
snake_case_ : str = neighbors.pop(_UpperCamelCase )
snake_case_ : Dict = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
snake_case_ : Any = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
snake_case_ : List[str] = picked_neighbor
else:
snake_case_ : Union[str, Any] = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
snake_case_ : Optional[int] = picked_neighbor
snake_case_ : Optional[Any] = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
snake_case_ : List[Any] = True
else:
snake_case_ : str = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_UpperCamelCase ) , _UpperCamelCase )
plt.xlabel('''Iterations''' )
plt.ylabel('''Function values''' )
plt.show()
return best_state
if __name__ == "__main__":
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
lowerCAmelCase_ = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
lowerCAmelCase_ = simulated_annealing(
prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
'''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
# starting the problem with initial coordinates (12, 47)
lowerCAmelCase_ = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
lowerCAmelCase_ = simulated_annealing(
prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
)
print(
'''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> List[str]:
"""simple docstring"""
return (3 * x**2) - (6 * y)
lowerCAmelCase_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowerCAmelCase_ = simulated_annealing(prob, find_max=False, visualization=True)
print(
'''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
F'''{local_min.score()}'''
)
lowerCAmelCase_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowerCAmelCase_ = simulated_annealing(prob, find_max=True, visualization=True)
print(
'''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
F'''{local_min.score()}'''
)
| 279
|
from math import factorial
lowerCAmelCase_ = {str(digit): factorial(digit) for digit in range(1_0)}
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
raise ValueError('''Parameter number must be greater than or equal to 0''' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(_UpperCamelCase ) )
def lowerCamelCase_ ( _UpperCamelCase = 60 , _UpperCamelCase = 1_000_000 ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''Parameters chain_length and number_limit must be int''' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'''Parameters chain_length and number_limit must be greater than 0''' )
# the counter for the chains with the exact desired length
snake_case_ : Optional[Any] = 0
# the cached sizes of the previous chains
snake_case_ : dict[int, int] = {}
for start_chain_element in range(1 , _UpperCamelCase ):
# The temporary set will contain the elements of the chain
snake_case_ : List[str] = set()
snake_case_ : List[Any] = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
snake_case_ : Any = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(_UpperCamelCase )
chain_set_length += 1
snake_case_ : List[Any] = digit_factorial_sum(_UpperCamelCase )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
snake_case_ : List[str] = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'''{solution()}''')
| 279
| 1
|
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
@property
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
torch.manual_seed(0 )
lowerCamelCase_ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , )
return model
@property
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
torch.manual_seed(0 )
lowerCamelCase_ = 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 , )
return CLIPTextModel(lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = self.dummy_uncond_unet
lowerCamelCase_ = DDIMScheduler()
lowerCamelCase_ = self.dummy_vq_model
lowerCamelCase_ = LDMPipeline(unet=lowercase , vqvae=lowercase , scheduler=lowercase )
ldm.to(lowercase )
ldm.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = ldm(generator=lowercase , num_inference_steps=2 , output_type="numpy" ).images
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = ldm(generator=lowercase , num_inference_steps=2 , output_type="numpy" , return_dict=lowercase )[0]
lowerCamelCase_ = image[0, -3:, -3:, -1]
lowerCamelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase_ = np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2] )
lowerCamelCase_ = 1e-2 if torch_device != "mps" else 3e-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" )
ldm.to(lowercase )
ldm.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = ldm(generator=lowercase , num_inference_steps=5 , output_type="numpy" ).images
lowerCamelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowerCamelCase_ = np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7] )
lowerCamelCase_ = 1e-2 if torch_device != "mps" else 3e-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 47
|
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
lowerCamelCase_ = defaults
cls.build_naming_info()
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase , lowercase ) -> Optional[Any]:
if len(lowercase ) == 0:
return ""
lowerCamelCase_ = 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(lowercase ) + 1 ):
lowerCamelCase_ = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
lowerCamelCase_ = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(lowercase ):
lowerCamelCase_ = ""
while integer != 0:
lowerCamelCase_ = chr(ord("A" ) + integer % 10 ) + s
integer //= 10
return s
lowerCamelCase_ = 0
while True:
lowerCamelCase_ = word + "#" + int_to_alphabetic(lowercase )
if sword in info["reverse_short_word"]:
continue
else:
lowerCamelCase_ = sword
break
lowerCamelCase_ = short_word
lowerCamelCase_ = word
return short_word
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase , lowercase ) -> int:
lowerCamelCase_ = param_name.split("_" )
lowerCamelCase_ = [TrialShortNamer.shortname_for_word(lowercase , lowercase ) 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
lowerCamelCase_ = ["", "_"]
for separator in separators:
lowerCamelCase_ = separator.join(lowercase )
if shortname not in info["reverse_short_param"]:
lowerCamelCase_ = shortname
lowerCamelCase_ = param_name
return shortname
return param_name
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase , lowercase ) -> Optional[Any]:
lowerCamelCase_ = TrialShortNamer.shortname_for_key(lowercase , lowercase )
lowerCamelCase_ = short_name
lowerCamelCase_ = param_name
@classmethod
def SCREAMING_SNAKE_CASE_( cls ) -> Dict:
if cls.NAMING_INFO is not None:
return
lowerCamelCase_ = {
"short_word": {},
"reverse_short_word": {},
"short_param": {},
"reverse_short_param": {},
}
lowerCamelCase_ = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(lowercase , lowercase )
lowerCamelCase_ = info
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase ) -> Optional[int]:
cls.build_naming_info()
assert cls.PREFIX is not None
lowerCamelCase_ = [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
lowerCamelCase_ = cls.NAMING_INFO["short_param"][k]
if isinstance(lowercase , lowercase ):
lowerCamelCase_ = 1 if v else 0
lowerCamelCase_ = "" if isinstance(lowercase , (int, float) ) else "-"
lowerCamelCase_ = f'{key}{sep}{v}'
name.append(lowercase )
return "_".join(lowercase )
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase ) -> List[Any]:
lowerCamelCase_ = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
lowerCamelCase_ = []
else:
lowerCamelCase_ = repr.split("_" )
lowerCamelCase_ = {}
for value in values:
if "-" in value:
lowerCamelCase_ , lowerCamelCase_ = value.split("-" )
else:
lowerCamelCase_ = re.sub("[0-9.]" , "" , lowercase )
lowerCamelCase_ = float(re.sub("[^0-9.]" , "" , lowercase ) )
lowerCamelCase_ = cls.NAMING_INFO["reverse_short_param"][p_k]
lowerCamelCase_ = p_v
for k in cls.DEFAULTS:
if k not in parameters:
lowerCamelCase_ = cls.DEFAULTS[k]
return parameters
| 47
| 1
|
import math
def __UpperCamelCase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
return math.pow(lowerCAmelCase__ , 2 ) - a
def __UpperCamelCase ( lowerCAmelCase__ : float ):
return 2 * x
def __UpperCamelCase ( lowerCAmelCase__ : float ):
__a : Dict = 2.0
while start <= a:
__a : List[Any] = math.pow(lowerCAmelCase__ , 2 )
return start
def __UpperCamelCase ( lowerCAmelCase__ : float , lowerCAmelCase__ : int = 9_9_9_9 , lowerCAmelCase__ : float = 0.00_00_00_00_00_00_01 ):
if a < 0:
raise ValueError('''math domain error''' )
__a : List[Any] = get_initial_point(lowerCAmelCase__ )
for _ in range(lowerCAmelCase__ ):
__a : Optional[Any] = value
__a : str = value - fx(lowerCAmelCase__ , lowerCAmelCase__ ) / fx_derivative(lowerCAmelCase__ )
if abs(prev_value - value ) < tolerance:
return value
return value
if __name__ == "__main__":
from doctest import testmod
testmod()
| 216
|
from __future__ import annotations
def __UpperCamelCase ( lowerCAmelCase__ : list[float] , lowerCAmelCase__ : list[float] ):
__a : Dict = sorted(numsa + numsa )
__a , __a : Optional[Any] = divmod(len(lowerCAmelCase__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase__ =[float(x) for x in input('Enter the elements of first array: ').split()]
lowercase__ =[float(x) for x in input('Enter the elements of second array: ').split()]
print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
| 216
| 1
|
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
'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',
'label_embs_concat': 'label_embeddings_concat',
'mask_emb': 'masked_spec_embed',
'spk_proj': 'speaker_proj',
}
lowerCamelCase__ = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'label_embeddings_concat',
'speaker_proj',
'layer_norm_for_extract',
]
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
for attribute in key.split('.' ):
__lowerCAmelCase : Any = getattr(UpperCamelCase__ , UpperCamelCase__ )
if weight_type is not None:
__lowerCAmelCase : Dict = getattr(UpperCamelCase__ , UpperCamelCase__ ).shape
else:
__lowerCAmelCase : Union[str, Any] = 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 : Optional[Any] = value
elif weight_type == "weight_g":
__lowerCAmelCase : Optional[Any] = value
elif weight_type == "weight_v":
__lowerCAmelCase : Tuple = value
elif weight_type == "bias":
__lowerCAmelCase : List[Any] = value
else:
__lowerCAmelCase : Optional[Any] = value
logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Optional[int] = []
__lowerCAmelCase : List[str] = fairseq_model.state_dict()
__lowerCAmelCase : Optional[Any] = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , hf_model.config.feat_extract_norm == 'group' , )
__lowerCAmelCase : Dict = True
else:
for key, mapped_key in MAPPING.items():
__lowerCAmelCase : List[Any] = 'unispeech_sat.' + 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]:
if "layer_norm_for_extract" in name and (".".join(name.split('.' )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCAmelCase : str = True
if "*" in mapped_key:
__lowerCAmelCase : Dict = name.split(UpperCamelCase__ )[0].split('.' )[-2]
__lowerCAmelCase : Optional[Any] = mapped_key.replace('*' , UpperCamelCase__ )
if "weight_g" in name:
__lowerCAmelCase : str = 'weight_g'
elif "weight_v" in name:
__lowerCAmelCase : int = 'weight_v'
elif "bias" in name:
__lowerCAmelCase : str = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase : List[str] = 'weight'
else:
__lowerCAmelCase : Tuple = 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 __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Union[str, Any] = full_name.split('conv_layers.' )[-1]
__lowerCAmelCase : str = name.split('.' )
__lowerCAmelCase : Optional[Any] = int(items[0] )
__lowerCAmelCase : int = 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 : Tuple = 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 : Tuple = 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[layer_id].layer_norm.bias.data.shape} was found." )
__lowerCAmelCase : Any = 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[layer_id].layer_norm.weight.data.shape} was found." )
__lowerCAmelCase : Union[str, Any] = 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 __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=True ):
if config_path is not None:
__lowerCAmelCase : Union[str, Any] = UniSpeechSatConfig.from_pretrained(UpperCamelCase__ )
else:
__lowerCAmelCase : Any = UniSpeechSatConfig()
__lowerCAmelCase : Any = ''
if is_finetuned:
__lowerCAmelCase : List[str] = UniSpeechSatForCTC(UpperCamelCase__ )
else:
__lowerCAmelCase : Any = UniSpeechSatForPreTraining(UpperCamelCase__ )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : str = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
__lowerCAmelCase : int = model[0].eval()
recursively_load_weights(UpperCamelCase__ , UpperCamelCase__ )
hf_wavavec.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
lowerCamelCase__ = 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"""
)
lowerCamelCase__ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 367
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class A__ ( _lowerCamelCase , unittest.TestCase):
A_ : Optional[int] = ShapEPipeline
A_ : str = ['prompt']
A_ : Any = ['prompt']
A_ : List[Any] = [
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
A_ : Optional[int] = False
@property
def __lowerCamelCase ( self ):
return 32
@property
def __lowerCamelCase ( self ):
return 32
@property
def __lowerCamelCase ( self ):
return self.time_input_dim * 4
@property
def __lowerCamelCase ( self ):
return 8
@property
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , 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 CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE )
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Optional[Any] = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
__lowerCAmelCase : int = PriorTransformer(**_SCREAMING_SNAKE_CASE )
return model
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Dict = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
__lowerCAmelCase : Union[str, Any] = ShapERenderer(**_SCREAMING_SNAKE_CASE )
return model
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = self.dummy_prior
__lowerCAmelCase : str = self.dummy_text_encoder
__lowerCAmelCase : List[Any] = self.dummy_tokenizer
__lowerCAmelCase : str = self.dummy_renderer
__lowerCAmelCase : List[Any] = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=10_24 , prediction_type='sample' , use_karras_sigmas=_SCREAMING_SNAKE_CASE , clip_sample=_SCREAMING_SNAKE_CASE , clip_sample_range=1.0 , )
__lowerCAmelCase : int = {
'prior': prior,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 ):
if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
__lowerCAmelCase : int = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase : Dict = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = {
'prompt': 'horse',
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __lowerCamelCase ( self ):
__lowerCAmelCase : Any = 'cpu'
__lowerCAmelCase : int = self.get_dummy_components()
__lowerCAmelCase : Union[str, Any] = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : Optional[Any] = output.images[0]
__lowerCAmelCase : Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__lowerCAmelCase : str = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCamelCase ( self ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = torch_device == 'cpu'
__lowerCAmelCase : str = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=_SCREAMING_SNAKE_CASE , relax_max_difference=_SCREAMING_SNAKE_CASE , )
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = self.get_dummy_components()
__lowerCAmelCase : List[Any] = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = 1
__lowerCAmelCase : List[Any] = 2
__lowerCAmelCase : int = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
for key in inputs.keys():
if key in self.batch_params:
__lowerCAmelCase : Dict = batch_size * [inputs[key]]
__lowerCAmelCase : Any = pipe(**_SCREAMING_SNAKE_CASE , num_images_per_prompt=_SCREAMING_SNAKE_CASE )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class A__ ( unittest.TestCase):
def __lowerCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_np_out.npy' )
__lowerCAmelCase : Dict = ShapEPipeline.from_pretrained('openai/shap-e' )
__lowerCAmelCase : int = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(0 )
__lowerCAmelCase : List[str] = pipe(
'a shark' , generator=_SCREAMING_SNAKE_CASE , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
| 182
| 0
|
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
def __lt__(self , lowerCamelCase_ ):
"""simple docstring"""
return self[-1] < other[-1]
def __eq__(self , lowerCamelCase_ ):
"""simple docstring"""
return self[-1] == other[-1]
def a( A : list ) -> list:
"""simple docstring"""
a = []
# sort into stacks
for element in collection:
a = Stack([element] )
a = bisect_left(A , A )
if i != len(A ):
stacks[i].append(A )
else:
stacks.append(A )
# use a heap-based merge to merge stack efficiently
a = merge(*(reversed(A ) for stack in stacks) )
return collection
if __name__ == "__main__":
_lowercase: List[Any] = input("Enter numbers separated by a comma:\n").strip()
_lowercase: int = [int(item) for item in user_input.split(",")]
print(patience_sort(unsorted))
| 227
|
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowercase: Union[str, Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
_lowercase: List[str] = json.load(f)
@require_torch
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
return FSMTTokenizer.from_pretrained(lowerCamelCase_ )
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
a = FSMTForConditionalGeneration.from_pretrained(lowerCamelCase_ ).to(lowerCamelCase_ )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["en-ru", 26.0],
["ru-en", 22.0],
["en-de", 22.0],
["de-en", 29.0],
] )
@slow
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
a = F'''facebook/wmt19-{pair}'''
a = self.get_tokenizer(lowerCamelCase_ )
a = self.get_model(lowerCamelCase_ )
a = bleu_data[pair]["src"]
a = bleu_data[pair]["tgt"]
a = tokenizer(lowerCamelCase_ , return_tensors="pt" , truncation=lowerCamelCase_ , padding="longest" ).to(lowerCamelCase_ )
a = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
a = tokenizer.batch_decode(
lowerCamelCase_ , skip_special_tokens=lowerCamelCase_ , clean_up_tokenization_spaces=lowerCamelCase_ )
a = calculate_bleu(lowerCamelCase_ , lowerCamelCase_ )
print(lowerCamelCase_ )
self.assertGreaterEqual(scores["bleu"] , lowerCamelCase_ )
| 227
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : str = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
'TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrajectoryTransformerModel',
'TrajectoryTransformerPreTrainedModel',
'load_tf_weights_in_trajectory_transformer',
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
UpperCAmelCase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 365
|
"""simple docstring"""
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : List[str] = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
UpperCAmelCase : List[str] = {
'b0': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def lowerCamelCase ( _UpperCamelCase : List[Any] ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = EfficientNetConfig()
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""hidden_dim"""]
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""width_coef"""]
__UpperCAmelCase : str = CONFIG_MAP[model_name]["""depth_coef"""]
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""image_size"""]
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""dropout_rate"""]
__UpperCAmelCase : Union[str, Any] = CONFIG_MAP[model_name]["""dw_padding"""]
__UpperCAmelCase : int = """huggingface/label-files"""
__UpperCAmelCase : Optional[int] = """imagenet-1k-id2label.json"""
__UpperCAmelCase : str = 1_0_0_0
__UpperCAmelCase : Dict = json.load(open(hf_hub_download(_UpperCamelCase , _UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
__UpperCAmelCase : int = {int(_UpperCamelCase ): v for k, v in idalabel.items()}
__UpperCAmelCase : Dict = idalabel
__UpperCAmelCase : Tuple = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Dict = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__UpperCAmelCase : Optional[Any] = Image.open(requests.get(_UpperCamelCase , stream=_UpperCamelCase ).raw )
return im
def lowerCamelCase ( _UpperCamelCase : Any ) -> str:
'''simple docstring'''
__UpperCAmelCase : Tuple = CONFIG_MAP[model_name]["""image_size"""]
__UpperCAmelCase : List[str] = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] , do_center_crop=_UpperCamelCase , )
return preprocessor
def lowerCamelCase ( _UpperCamelCase : Dict ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
__UpperCAmelCase : str = sorted(set(_UpperCamelCase ) )
__UpperCAmelCase : Optional[int] = len(_UpperCamelCase )
__UpperCAmelCase : Any = {b: str(_UpperCamelCase ) for b, i in zip(_UpperCamelCase , range(_UpperCamelCase ) )}
__UpperCAmelCase : Any = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
__UpperCAmelCase : List[str] = block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
__UpperCAmelCase : Optional[int] = {}
for item in rename_keys:
if item[0] in original_param_names:
__UpperCAmelCase : Optional[Any] = """efficientnet.""" + item[1]
__UpperCAmelCase : Tuple = """classifier.weight"""
__UpperCAmelCase : Optional[int] = """classifier.bias"""
return key_mapping
def lowerCamelCase ( _UpperCamelCase : Any , _UpperCamelCase : Dict , _UpperCamelCase : int ) -> Tuple:
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
__UpperCAmelCase : List[Any] = key_mapping[key]
if "_conv" in key and "kernel" in key:
__UpperCAmelCase : int = torch.from_numpy(_UpperCamelCase ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__UpperCAmelCase : Optional[Any] = torch.from_numpy(_UpperCamelCase ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__UpperCAmelCase : List[str] = torch.from_numpy(np.transpose(_UpperCamelCase ) )
else:
__UpperCAmelCase : Tuple = torch.from_numpy(_UpperCamelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_UpperCamelCase )
@torch.no_grad()
def lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[str] ) -> Tuple:
'''simple docstring'''
__UpperCAmelCase : int = model_classes[model_name](
include_top=_UpperCamelCase , weights="""imagenet""" , input_tensor=_UpperCamelCase , input_shape=_UpperCamelCase , pooling=_UpperCamelCase , classes=1_0_0_0 , classifier_activation="""softmax""" , )
__UpperCAmelCase : List[str] = original_model.trainable_variables
__UpperCAmelCase : List[Any] = original_model.non_trainable_variables
__UpperCAmelCase : Union[str, Any] = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__UpperCAmelCase : int = param.numpy()
__UpperCAmelCase : Dict = list(tf_params.keys() )
# Load HuggingFace model
__UpperCAmelCase : Optional[Any] = get_efficientnet_config(_UpperCamelCase )
__UpperCAmelCase : Optional[Any] = EfficientNetForImageClassification(_UpperCamelCase ).eval()
__UpperCAmelCase : Any = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
__UpperCAmelCase : Tuple = rename_keys(_UpperCamelCase )
replace_params(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Initialize preprocessor and preprocess input image
__UpperCAmelCase : List[Any] = convert_image_processor(_UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
__UpperCAmelCase : Optional[int] = hf_model(**_UpperCamelCase )
__UpperCAmelCase : Any = outputs.logits.detach().numpy()
# Original model inference
__UpperCAmelCase : Union[str, Any] = False
__UpperCAmelCase : Dict = CONFIG_MAP[model_name]["""image_size"""]
__UpperCAmelCase : str = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__UpperCAmelCase : Optional[Any] = image.img_to_array(_UpperCamelCase )
__UpperCAmelCase : Tuple = np.expand_dims(_UpperCamelCase , axis=0 )
__UpperCAmelCase : str = original_model.predict(_UpperCamelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_UpperCamelCase , _UpperCamelCase , atol=1E-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(_UpperCamelCase ):
os.mkdir(_UpperCamelCase )
# Save converted model and image processor
hf_model.save_pretrained(_UpperCamelCase )
preprocessor.save_pretrained(_UpperCamelCase )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
__UpperCAmelCase : List[str] = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(_UpperCamelCase )
hf_model.push_to_hub(_UpperCamelCase )
if __name__ == "__main__":
UpperCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
UpperCAmelCase : Any = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 320
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|
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
UpperCAmelCase_ : Optional[Any] = """"""
UpperCAmelCase_ : int = """"""
UpperCAmelCase_ : List[str] = """"""
UpperCAmelCase_ : Any = """"""
def _A (__a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = tweepy.OAuthHandler(__a , __a )
auth.set_access_token(__a , __a )
SCREAMING_SNAKE_CASE_ : Dict = tweepy.API(__a )
# initialize a list to hold all the tweepy Tweets
SCREAMING_SNAKE_CASE_ : List[Any] = []
# make initial request for most recent tweets (200 is the maximum allowed count)
SCREAMING_SNAKE_CASE_ : int = api.user_timeline(screen_name=__a , count=2_00 )
# save most recent tweets
alltweets.extend(__a )
# save the id of the oldest tweet less one
SCREAMING_SNAKE_CASE_ : Optional[int] = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__a ) > 0:
print(f'getting tweets before {oldest}' )
# all subsequent requests use the max_id param to prevent duplicates
SCREAMING_SNAKE_CASE_ : Dict = api.user_timeline(
screen_name=__a , count=2_00 , max_id=__a )
# save most recent tweets
alltweets.extend(__a )
# update the id of the oldest tweet less one
SCREAMING_SNAKE_CASE_ : List[str] = alltweets[-1].id - 1
print(f'...{len(__a )} tweets downloaded so far' )
# transform the tweepy tweets into a 2D array that will populate the csv
SCREAMING_SNAKE_CASE_ : List[Any] = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(f'new_{screen_name}_tweets.csv' , '''w''' ) as f:
SCREAMING_SNAKE_CASE_ : List[Any] = csv.writer(__a )
writer.writerow(['''id''', '''created_at''', '''text'''] )
writer.writerows(__a )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets("""FirePing32""")
| 91
|
"""simple docstring"""
import re
def lowercase_ ( _lowerCamelCase: str ) -> bool:
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = re.compile(
r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$" )
return bool(re.search(_lowerCamelCase , _lowerCamelCase ) )
if __name__ == "__main__":
__A = '''0094702343221'''
print(is_sri_lankan_phone_number(phone))
| 135
| 0
|
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : List[str] = '''van'''
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : List[str]=2_2_4 , SCREAMING_SNAKE_CASE__ : Optional[Any]=3 , SCREAMING_SNAKE_CASE__ : Dict=[7, 3, 3, 3] , SCREAMING_SNAKE_CASE__ : Optional[Any]=[4, 2, 2, 2] , SCREAMING_SNAKE_CASE__ : str=[6_4, 1_2_8, 3_2_0, 5_1_2] , SCREAMING_SNAKE_CASE__ : List[Any]=[3, 3, 1_2, 3] , SCREAMING_SNAKE_CASE__ : int=[8, 8, 4, 4] , SCREAMING_SNAKE_CASE__ : List[Any]="gelu" , SCREAMING_SNAKE_CASE__ : List[Any]=0.02 , SCREAMING_SNAKE_CASE__ : List[Any]=1E-6 , SCREAMING_SNAKE_CASE__ : List[str]=1E-2 , SCREAMING_SNAKE_CASE__ : int=0.0 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0 , **SCREAMING_SNAKE_CASE__ : List[str] , ) -> Any:
super().__init__(**SCREAMING_SNAKE_CASE__ )
a_ : int = image_size
a_ : Dict = num_channels
a_ : Optional[Any] = patch_sizes
a_ : List[Any] = strides
a_ : int = hidden_sizes
a_ : List[Any] = depths
a_ : int = mlp_ratios
a_ : str = hidden_act
a_ : str = initializer_range
a_ : Union[str, Any] = layer_norm_eps
a_ : List[Any] = layer_scale_init_value
a_ : List[str] = drop_path_rate
a_ : str = dropout_rate
| 356
|
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
@require_torch
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a_ : str = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
a_ : Any = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
a_ : Dict = '\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
a_ : Dict = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
BertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
pipeline(task='fill-mask' , model=SCREAMING_SNAKE_CASE__ )
# baseline - just load from_pretrained with normal network
a_ : Union[str, Any] = [sys.executable, '-c', '\n'.join([load, run, mock] )]
# should succeed
a_ : List[str] = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a_ : Any = '1'
a_ : Optional[Any] = subprocess.run(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , check=SCREAMING_SNAKE_CASE__ , capture_output=SCREAMING_SNAKE_CASE__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('success' , result.stdout.decode() )
@require_torch
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a_ : Tuple = '\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n '
a_ : str = '\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n '
a_ : Any = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n '
# Force fetching the files so that we can use the cache
a_ : Tuple = 'hf-internal-testing/tiny-random-bert'
BertConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
BertModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__ )
pipeline(task='fill-mask' , model=SCREAMING_SNAKE_CASE__ )
# baseline - just load from_pretrained with normal network
a_ : Optional[Any] = [sys.executable, '-c', '\n'.join([load, run, mock] )]
# should succeed
a_ : Dict = self.get_env()
a_ : Dict = subprocess.run(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , check=SCREAMING_SNAKE_CASE__ , capture_output=SCREAMING_SNAKE_CASE__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('success' , result.stdout.decode() )
@require_torch
def SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]:
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
a_ : Any = '\nfrom transformers import BertConfig, BertModel, BertTokenizer\n '
a_ : Dict = '\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n '
a_ : Any = '\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n '
# baseline - just load from_pretrained with normal network
a_ : Any = [sys.executable, '-c', '\n'.join([load, run] )]
# should succeed
a_ : Tuple = self.get_env()
a_ : int = subprocess.run(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , check=SCREAMING_SNAKE_CASE__ , capture_output=SCREAMING_SNAKE_CASE__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('success' , result.stdout.decode() )
# next emulate no network
a_ : str = [sys.executable, '-c', '\n'.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a_ : Dict = '1'
a_ : int = subprocess.run(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , check=SCREAMING_SNAKE_CASE__ , capture_output=SCREAMING_SNAKE_CASE__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('success' , result.stdout.decode() )
@require_torch
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
a_ : Union[str, Any] = '\nfrom transformers import pipeline\n '
a_ : Optional[Any] = '\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n '
a_ : Any = '\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n '
a_ : Union[str, Any] = self.get_env()
a_ : Optional[Any] = '1'
a_ : int = [sys.executable, '-c', '\n'.join([load, mock, run] )]
a_ : Union[str, Any] = subprocess.run(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , check=SCREAMING_SNAKE_CASE__ , capture_output=SCREAMING_SNAKE_CASE__ )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
'You cannot infer task automatically within `pipeline` when using offline mode' , result.stderr.decode().replace('\n' , '' ) , )
@require_torch
def SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
a_ : Optional[int] = '\nfrom transformers import AutoModel\n '
a_ : Dict = '\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n '
# baseline - just load from_pretrained with normal network
a_ : Tuple = [sys.executable, '-c', '\n'.join([load, run] )]
# should succeed
a_ : Optional[Any] = self.get_env()
a_ : Dict = subprocess.run(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , check=SCREAMING_SNAKE_CASE__ , capture_output=SCREAMING_SNAKE_CASE__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('success' , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
a_ : Optional[int] = '1'
a_ : Optional[Any] = subprocess.run(SCREAMING_SNAKE_CASE__ , env=SCREAMING_SNAKE_CASE__ , check=SCREAMING_SNAKE_CASE__ , capture_output=SCREAMING_SNAKE_CASE__ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('success' , result.stdout.decode() )
| 120
| 0
|
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
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : Union[str, Any] = 'instructblip_vision_model'
def __init__(self : Union[str, Any] , __UpperCAmelCase : Optional[int]=1_4_0_8 , __UpperCAmelCase : List[Any]=6_1_4_4 , __UpperCAmelCase : Optional[int]=3_9 , __UpperCAmelCase : Optional[Any]=1_6 , __UpperCAmelCase : Optional[Any]=2_2_4 , __UpperCAmelCase : Optional[Any]=1_4 , __UpperCAmelCase : Dict="gelu" , __UpperCAmelCase : Tuple=1E-6 , __UpperCAmelCase : str=0.0 , __UpperCAmelCase : Tuple=1E-10 , __UpperCAmelCase : int=True , **__UpperCAmelCase : Union[str, Any] , ) -> Dict:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
UpperCAmelCase__ = hidden_size
UpperCAmelCase__ = intermediate_size
UpperCAmelCase__ = num_hidden_layers
UpperCAmelCase__ = num_attention_heads
UpperCAmelCase__ = patch_size
UpperCAmelCase__ = image_size
UpperCAmelCase__ = initializer_range
UpperCAmelCase__ = attention_dropout
UpperCAmelCase__ = layer_norm_eps
UpperCAmelCase__ = hidden_act
UpperCAmelCase__ = qkv_bias
@classmethod
def lowercase_ (cls : Union[str, Any] , __UpperCAmelCase : Union[str, os.PathLike] , **__UpperCAmelCase : Tuple ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__UpperCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = cls.get_config_dict(__UpperCAmelCase , **__UpperCAmelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
UpperCAmelCase__ = 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 A ( UpperCAmelCase_ ):
__UpperCAmelCase : List[Any] = 'instructblip_qformer'
def __init__(self : str , __UpperCAmelCase : Tuple=3_0_5_2_2 , __UpperCAmelCase : Dict=7_6_8 , __UpperCAmelCase : Any=1_2 , __UpperCAmelCase : List[Any]=1_2 , __UpperCAmelCase : Any=3_0_7_2 , __UpperCAmelCase : List[str]="gelu" , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : Optional[int]=0.1 , __UpperCAmelCase : Optional[int]=5_1_2 , __UpperCAmelCase : List[str]=0.02 , __UpperCAmelCase : List[str]=1E-12 , __UpperCAmelCase : Union[str, Any]=0 , __UpperCAmelCase : Union[str, Any]="absolute" , __UpperCAmelCase : List[Any]=2 , __UpperCAmelCase : Any=1_4_0_8 , **__UpperCAmelCase : List[Any] , ) -> Dict:
"""simple docstring"""
super().__init__(pad_token_id=__UpperCAmelCase , **__UpperCAmelCase )
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__ = initializer_range
UpperCAmelCase__ = layer_norm_eps
UpperCAmelCase__ = position_embedding_type
UpperCAmelCase__ = cross_attention_frequency
UpperCAmelCase__ = encoder_hidden_size
@classmethod
def lowercase_ (cls : Optional[Any] , __UpperCAmelCase : Union[str, os.PathLike] , **__UpperCAmelCase : Dict ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(__UpperCAmelCase )
UpperCAmelCase__ , UpperCAmelCase__ = cls.get_config_dict(__UpperCAmelCase , **__UpperCAmelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get("model_type" ) == "instructblip":
UpperCAmelCase__ = 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(__UpperCAmelCase , **__UpperCAmelCase )
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : Dict = 'instructblip'
__UpperCAmelCase : Optional[int] = True
def __init__(self : Tuple , __UpperCAmelCase : Union[str, Any]=None , __UpperCAmelCase : int=None , __UpperCAmelCase : int=None , __UpperCAmelCase : str=3_2 , **__UpperCAmelCase : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().__init__(**__UpperCAmelCase )
if vision_config is None:
UpperCAmelCase__ = {}
logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )
if qformer_config is None:
UpperCAmelCase__ = {}
logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )
if text_config is None:
UpperCAmelCase__ = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )
UpperCAmelCase__ = InstructBlipVisionConfig(**__UpperCAmelCase )
UpperCAmelCase__ = InstructBlipQFormerConfig(**__UpperCAmelCase )
UpperCAmelCase__ = text_config["model_type"] if "model_type" in text_config else "opt"
UpperCAmelCase__ = CONFIG_MAPPING[text_model_type](**__UpperCAmelCase )
UpperCAmelCase__ = self.text_config.tie_word_embeddings
UpperCAmelCase__ = self.text_config.is_encoder_decoder
UpperCAmelCase__ = num_query_tokens
UpperCAmelCase__ = self.vision_config.hidden_size
UpperCAmelCase__ = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
UpperCAmelCase__ = 1.0
UpperCAmelCase__ = 0.02
@classmethod
def lowercase_ (cls : List[Any] , __UpperCAmelCase : InstructBlipVisionConfig , __UpperCAmelCase : InstructBlipQFormerConfig , __UpperCAmelCase : PretrainedConfig , **__UpperCAmelCase : List[Any] , ) -> Tuple:
"""simple docstring"""
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__UpperCAmelCase , )
def lowercase_ (self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ = copy.deepcopy(self.__dict__ )
UpperCAmelCase__ = self.vision_config.to_dict()
UpperCAmelCase__ = self.qformer_config.to_dict()
UpperCAmelCase__ = self.text_config.to_dict()
UpperCAmelCase__ = self.__class__.model_type
return output
| 65
|
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCamelCase__ : Any = 10
def UpperCamelCase ( _lowerCAmelCase : int, _lowerCAmelCase : int, _lowerCAmelCase : list[int], _lowerCAmelCase : int ) -> int:
for i in range(_lowerCAmelCase, _lowerCAmelCase ):
if array[i] == target:
return i
return -1
def UpperCamelCase ( _lowerCAmelCase : list[int], _lowerCAmelCase : int ) -> int:
_UpperCAmelCase : Optional[Any] = 0
_UpperCAmelCase : Optional[int] = len(_lowerCAmelCase )
while left <= right:
if right - left < precision:
return lin_search(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
_UpperCAmelCase : str = (left + right) // 3 + 1
_UpperCAmelCase : int = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
_UpperCAmelCase : Tuple = one_third - 1
elif array[two_third] < target:
_UpperCAmelCase : Any = two_third + 1
else:
_UpperCAmelCase : Any = one_third + 1
_UpperCAmelCase : Dict = two_third - 1
else:
return -1
def UpperCamelCase ( _lowerCAmelCase : int, _lowerCAmelCase : int, _lowerCAmelCase : list[int], _lowerCAmelCase : int ) -> int:
if left < right:
if right - left < precision:
return lin_search(_lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
_UpperCAmelCase : Optional[Any] = (left + right) // 3 + 1
_UpperCAmelCase : List[Any] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_lowerCAmelCase, one_third - 1, _lowerCAmelCase, _lowerCAmelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1, _lowerCAmelCase, _lowerCAmelCase, _lowerCAmelCase )
else:
return rec_ternary_search(one_third + 1, two_third - 1, _lowerCAmelCase, _lowerCAmelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase__ : Optional[Any] = input('''Enter numbers separated by comma:\n''').strip()
lowerCamelCase__ : Optional[int] = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
lowerCamelCase__ : List[Any] = int(input('''Enter the number to be found in the list:\n''').strip())
lowerCamelCase__ : str = ite_ternary_search(collection, target)
lowerCamelCase__ : List[Any] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F'''Iterative search: {target} found at positions: {resulta}''')
print(F'''Recursive search: {target} found at positions: {resulta}''')
else:
print('''Not found''')
| 246
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : List[str] = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : str = [
'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTMSNModel',
'ViTMSNForImageClassification',
'ViTMSNPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_msn import (
VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMSNForImageClassification,
ViTMSNModel,
ViTMSNPreTrainedModel,
)
else:
import sys
snake_case_ : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 365
|
'''simple docstring'''
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,
)
snake_case_ : Optional[Any] = {
'configuration_clip': [
'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPConfig',
'CLIPOnnxConfig',
'CLIPTextConfig',
'CLIPVisionConfig',
],
'processing_clip': ['CLIPProcessor'],
'tokenization_clip': ['CLIPTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = ['CLIPTokenizerFast']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = ['CLIPFeatureExtractor']
snake_case_ : Dict = ['CLIPImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Tuple = [
'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'CLIPModel',
'CLIPPreTrainedModel',
'CLIPTextModel',
'CLIPTextModelWithProjection',
'CLIPVisionModel',
'CLIPVisionModelWithProjection',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : List[Any] = [
'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFCLIPModel',
'TFCLIPPreTrainedModel',
'TFCLIPTextModel',
'TFCLIPVisionModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = [
'FlaxCLIPModel',
'FlaxCLIPPreTrainedModel',
'FlaxCLIPTextModel',
'FlaxCLIPTextPreTrainedModel',
'FlaxCLIPVisionModel',
'FlaxCLIPVisionPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
snake_case_ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 236
| 0
|
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class snake_case__ :
lowercase__ : Dict = None
def __magic_name__ ( self ) -> int:
__magic_name__ : str = self.feature_extraction_class(**self.feat_extract_dict )
__magic_name__ : List[str] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , _SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ) -> Dict:
__magic_name__ : int = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ : Optional[Any] = os.path.join(_SCREAMING_SNAKE_CASE , """feat_extract.json""" )
feat_extract_first.to_json_file(_SCREAMING_SNAKE_CASE )
__magic_name__ : List[Any] = self.feature_extraction_class.from_json_file(_SCREAMING_SNAKE_CASE )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __magic_name__ ( self ) -> Union[str, Any]:
__magic_name__ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ : Union[str, Any] = feat_extract_first.save_pretrained(_SCREAMING_SNAKE_CASE )[0]
check_json_file_has_correct_format(_SCREAMING_SNAKE_CASE )
__magic_name__ : Union[str, Any] = self.feature_extraction_class.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def __magic_name__ ( self ) -> str:
__magic_name__ : Optional[Any] = self.feature_extraction_class()
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
| 342
|
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
lowerCamelCase = '''platform'''
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def lowerCamelCase_ ( _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , _a=None , ):
"""simple docstring"""
if attention_mask is None:
lowerCAmelCase__ : List[Any] = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
lowerCAmelCase__ : Dict = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
lowerCAmelCase__ : Any = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCAmelCase__ : Optional[int] = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCAmelCase__ : Tuple = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class _a :
def __init__( self : List[str] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int]=13 , _SCREAMING_SNAKE_CASE : List[str]=7 , _SCREAMING_SNAKE_CASE : Optional[Any]=True , _SCREAMING_SNAKE_CASE : int=False , _SCREAMING_SNAKE_CASE : List[Any]=99 , _SCREAMING_SNAKE_CASE : List[Any]=16 , _SCREAMING_SNAKE_CASE : Dict=2 , _SCREAMING_SNAKE_CASE : List[str]=4 , _SCREAMING_SNAKE_CASE : Union[str, Any]=4 , _SCREAMING_SNAKE_CASE : Any="gelu" , _SCREAMING_SNAKE_CASE : str=0.1 , _SCREAMING_SNAKE_CASE : Optional[Any]=0.1 , _SCREAMING_SNAKE_CASE : str=32 , _SCREAMING_SNAKE_CASE : Optional[int]=2 , _SCREAMING_SNAKE_CASE : str=1 , _SCREAMING_SNAKE_CASE : Optional[Any]=0 , _SCREAMING_SNAKE_CASE : List[str]=0.02 , )-> Any:
lowerCAmelCase__ : Any = parent
lowerCAmelCase__ : Dict = batch_size
lowerCAmelCase__ : Any = seq_length
lowerCAmelCase__ : Union[str, Any] = is_training
lowerCAmelCase__ : Optional[Any] = use_labels
lowerCAmelCase__ : List[str] = vocab_size
lowerCAmelCase__ : Optional[int] = hidden_size
lowerCAmelCase__ : List[str] = num_hidden_layers
lowerCAmelCase__ : Optional[int] = num_attention_heads
lowerCAmelCase__ : Optional[Any] = intermediate_size
lowerCAmelCase__ : List[str] = hidden_act
lowerCAmelCase__ : Tuple = hidden_dropout_prob
lowerCAmelCase__ : Any = attention_probs_dropout_prob
lowerCAmelCase__ : Dict = max_position_embeddings
lowerCAmelCase__ : int = eos_token_id
lowerCAmelCase__ : Dict = pad_token_id
lowerCAmelCase__ : Optional[Any] = bos_token_id
lowerCAmelCase__ : str = initializer_range
def UpperCAmelCase__( self : List[str] )-> Any:
lowerCAmelCase__ : Any = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
lowerCAmelCase__ : List[Any] = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
lowerCAmelCase__ : List[str] = shift_tokens_right(_SCREAMING_SNAKE_CASE , 1 , 2 )
lowerCAmelCase__ : List[Any] = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Optional[Any] = prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, inputs_dict
def UpperCAmelCase__( self : List[str] )-> Union[str, Any]:
lowerCAmelCase__ , lowerCAmelCase__ : Tuple = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase__( self : List[Any] , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Optional[Any] )-> str:
lowerCAmelCase__ : str = 20
lowerCAmelCase__ : Dict = model_class_name(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Optional[Any] = model.encode(inputs_dict['''input_ids'''] )
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
lowerCAmelCase__ : str = model.init_cache(decoder_input_ids.shape[0] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : int = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
lowerCAmelCase__ : Any = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowerCAmelCase__ : Dict = model.decode(
decoder_input_ids[:, :-1] , _SCREAMING_SNAKE_CASE , decoder_attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE , decoder_position_ids=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Union[str, Any] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
lowerCAmelCase__ : Tuple = model.decode(
decoder_input_ids[:, -1:] , _SCREAMING_SNAKE_CASE , decoder_attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : str = model.decode(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Union[str, Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'Max diff is {diff}' )
def UpperCAmelCase__( self : List[str] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : int )-> Tuple:
lowerCAmelCase__ : int = 20
lowerCAmelCase__ : Tuple = model_class_name(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Any = model.encode(inputs_dict['''input_ids'''] )
lowerCAmelCase__ , lowerCAmelCase__ : Dict = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
lowerCAmelCase__ : Optional[Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
lowerCAmelCase__ : Tuple = model.init_cache(decoder_input_ids.shape[0] , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : str = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowerCAmelCase__ : Optional[Any] = model.decode(
decoder_input_ids[:, :-1] , _SCREAMING_SNAKE_CASE , decoder_attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE , decoder_position_ids=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Optional[int] = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
lowerCAmelCase__ : str = model.decode(
decoder_input_ids[:, -1:] , _SCREAMING_SNAKE_CASE , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_SCREAMING_SNAKE_CASE , decoder_position_ids=_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Union[str, Any] = model.decode(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , decoder_attention_mask=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F'Max diff is {diff}' )
@require_flax
class _a ( unittest.TestCase):
_a : Optional[int] = 99
def UpperCAmelCase__( self : int )-> Tuple:
lowerCAmelCase__ : Any = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
lowerCAmelCase__ : Optional[Any] = input_ids.shape[0]
lowerCAmelCase__ : Optional[Any] = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def UpperCAmelCase__( self : List[str] )-> Any:
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[str] = self._get_config_and_data()
lowerCAmelCase__ : Dict = FlaxBlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Dict = lm_model(input_ids=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Union[str, Any] = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : str )-> Any:
lowerCAmelCase__ : Union[str, Any] = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
lowerCAmelCase__ : Dict = FlaxBlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Any = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
lowerCAmelCase__ : str = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
lowerCAmelCase__ : str = lm_model(input_ids=_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : int )-> Dict:
lowerCAmelCase__ : Union[str, Any] = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
lowerCAmelCase__ : int = shift_tokens_right(_SCREAMING_SNAKE_CASE , 1 , 2 )
lowerCAmelCase__ : int = np.equal(_SCREAMING_SNAKE_CASE , 1 ).astype(np.floataa ).sum()
lowerCAmelCase__ : int = np.equal(_SCREAMING_SNAKE_CASE , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(_SCREAMING_SNAKE_CASE , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class _a ( _lowercase , unittest.TestCase , _lowercase):
_a : Optional[int] = True
_a : List[str] = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
_a : Dict = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def UpperCAmelCase__( self : Optional[Any] )-> Any:
lowerCAmelCase__ : int = FlaxBlenderbotModelTester(self )
def UpperCAmelCase__( self : int )-> Optional[Any]:
lowerCAmelCase__ , lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : List[Any] )-> Tuple:
lowerCAmelCase__ , lowerCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def UpperCAmelCase__( self : Tuple )-> Any:
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCAmelCase__ : Tuple = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : int = model_class(_SCREAMING_SNAKE_CASE )
@jax.jit
def encode_jitted(_SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[str]=None , **_SCREAMING_SNAKE_CASE : List[Any] ):
return model.encode(input_ids=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
with self.subTest('''JIT Enabled''' ):
lowerCAmelCase__ : str = encode_jitted(**_SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
lowerCAmelCase__ : Any = encode_jitted(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCAmelCase__( self : Optional[Any] )-> Union[str, Any]:
lowerCAmelCase__ , lowerCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCAmelCase__ : Dict = model_class(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Any = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
lowerCAmelCase__ : str = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(_SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Any ):
return model.decode(
decoder_input_ids=_SCREAMING_SNAKE_CASE , decoder_attention_mask=_SCREAMING_SNAKE_CASE , encoder_outputs=_SCREAMING_SNAKE_CASE , )
with self.subTest('''JIT Enabled''' ):
lowerCAmelCase__ : List[Any] = decode_jitted(**_SCREAMING_SNAKE_CASE ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
lowerCAmelCase__ : Optional[Any] = decode_jitted(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) )
for jitted_output, output in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCAmelCase__( self : Dict )-> List[str]:
for model_class_name in self.all_model_classes:
lowerCAmelCase__ : List[Any] = model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
lowerCAmelCase__ : List[str] = np.ones((1, 1) ) * model.config.eos_token_id
lowerCAmelCase__ : Dict = model(_SCREAMING_SNAKE_CASE )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
@unittest.skipUnless(jax_device != '''cpu''' , '''3B test too slow on CPU.''' )
@slow
def UpperCAmelCase__( self : List[Any] )-> Union[str, Any]:
lowerCAmelCase__ : Optional[int] = {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
lowerCAmelCase__ : Tuple = {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}
lowerCAmelCase__ : Optional[int] = FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''' , from_pt=_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )
lowerCAmelCase__ : List[Any] = ['''Sam''']
lowerCAmelCase__ : Dict = tokenizer(_SCREAMING_SNAKE_CASE , return_tensors='''jax''' )
lowerCAmelCase__ : List[str] = model.generate(**_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : Tuple = '''Sam is a great name. It means "sun" in Gaelic.'''
lowerCAmelCase__ : Union[str, Any] = tokenizer.batch_decode(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
assert generated_txt[0].strip() == tgt_text
| 131
| 0
|
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Tuple = logging.get_logger(__name__)
lowercase : Union[str, Any] = ['model.decoder.embed_positions.weights']
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if "emb" in name:
A : Optional[int] = name.replace('''emb''' , '''model.decoder.embed_tokens''' )
if "transformer" in name:
A : List[Any] = name.replace('''transformer''' , '''model.decoder''' )
if "cross_attention" in name:
A : List[Any] = name.replace('''cross_attention''' , '''encoder_attn''' )
if "linear1" in name:
A : Optional[int] = name.replace('''linear1''' , '''fc1''' )
if "linear2" in name:
A : List[Any] = name.replace('''linear2''' , '''fc2''' )
if "norm1" in name:
A : List[Any] = name.replace('''norm1''' , '''self_attn_layer_norm''' )
if "norm_cross" in name:
A : Any = name.replace('''norm_cross''' , '''encoder_attn_layer_norm''' )
if "norm2" in name:
A : Dict = name.replace('''norm2''' , '''final_layer_norm''' )
if "out_norm" in name:
A : Union[str, Any] = name.replace('''out_norm''' , '''model.decoder.layer_norm''' )
if "linears" in name:
A : Dict = name.replace('''linears''' , '''lm_heads''' )
if "condition_provider.conditioners.description.output_proj" in name:
A : List[Any] = name.replace('''condition_provider.conditioners.description.output_proj''' , '''enc_to_dec_proj''' )
return name
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : List[Any] = list(state_dict.keys() )
A : Optional[int] = {}
for key in keys:
A : List[str] = state_dict.pop(snake_case__ )
A : str = rename_keys(snake_case__ )
if "in_proj_weight" in key:
# split fused qkv proj
A : List[Any] = val[:hidden_size, :]
A : Tuple = val[hidden_size : 2 * hidden_size, :]
A : Dict = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
A : int = val
else:
A : Union[str, Any] = val
return state_dict, enc_dec_proj_state_dict
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if checkpoint == "small":
# default config values
A : int = 1024
A : int = 24
A : Dict = 16
elif checkpoint == "medium":
A : Optional[Any] = 1536
A : str = 48
A : List[str] = 24
elif checkpoint == "large":
A : int = 2048
A : int = 48
A : Dict = 32
else:
raise ValueError(F'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' )
A : Optional[int] = MusicgenDecoderConfig(
hidden_size=snake_case__ , ffn_dim=hidden_size * 4 , num_hidden_layers=snake_case__ , num_attention_heads=snake_case__ , )
return config
@torch.no_grad()
def lowerCAmelCase_ ( snake_case__ , snake_case__=None , snake_case__=None , snake_case__="cpu" ):
'''simple docstring'''
A : Optional[Any] = MusicGen.get_pretrained(snake_case__ , device=snake_case__ )
A : int = decoder_config_from_checkpoint(snake_case__ )
A : int = fairseq_model.lm.state_dict()
A : Union[str, Any] = rename_state_dict(
snake_case__ , hidden_size=decoder_config.hidden_size )
A : str = TaEncoderModel.from_pretrained('''t5-base''' )
A : str = EncodecModel.from_pretrained('''facebook/encodec_32khz''' )
A : List[str] = MusicgenForCausalLM(snake_case__ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
A : str = decoder.load_state_dict(snake_case__ , strict=snake_case__ )
for key in missing_keys.copy():
if key.startswith(('''text_encoder''', '''audio_encoder''') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(snake_case__ )
if len(snake_case__ ) > 0:
raise ValueError(F'Missing key(s) in state_dict: {missing_keys}' )
if len(snake_case__ ) > 0:
raise ValueError(F'Unexpected key(s) in state_dict: {unexpected_keys}' )
# init the composite model
A : Union[str, Any] = MusicgenForConditionalGeneration(text_encoder=snake_case__ , audio_encoder=snake_case__ , decoder=snake_case__ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(snake_case__ )
# check we can do a forward pass
A : List[Any] = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
A : Any = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
A : List[Any] = model(input_ids=snake_case__ , decoder_input_ids=snake_case__ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('''Incorrect shape for logits''' )
# now construct the processor
A : Optional[Any] = AutoTokenizer.from_pretrained('''t5-base''' )
A : List[str] = AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' , padding_side='''left''' )
A : List[Any] = MusicgenProcessor(feature_extractor=snake_case__ , tokenizer=snake_case__ )
# set the appropriate bos/pad token ids
A : List[str] = 2048
A : List[str] = 2048
# set other default generation config params
A : Any = int(30 * audio_encoder.config.frame_rate )
A : Optional[Any] = True
A : str = 3.0
if pytorch_dump_folder is not None:
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
logger.info(F'Saving model {checkpoint} to {pytorch_dump_folder}' )
model.save_pretrained(snake_case__ )
processor.save_pretrained(snake_case__ )
if repo_id:
logger.info(F'Pushing model {checkpoint} to {repo_id}' )
model.push_to_hub(snake_case__ )
processor.push_to_hub(snake_case__ )
if __name__ == "__main__":
lowercase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint',
default='small',
type=str,
help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
)
parser.add_argument(
'--pytorch_dump_folder',
required=True,
default=None,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
parser.add_argument(
'--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
)
lowercase : Tuple = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 354
|
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[str] = 2
A : Dict = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(snake_case__ )
if n > 1:
factors.append(snake_case__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311
| 0
|
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 __snake_case ( lowercase_ , unittest.TestCase ):
__lowerCamelCase : List[Any] = """hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
def UpperCAmelCase__ ( self , snake_case__=0 ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =floats_tensor((1, 3, 128, 128) , rng=random.Random(__UpperCamelCase ) )
UpperCAmelCase : Optional[int] =np.random.RandomState(__UpperCamelCase )
UpperCAmelCase : int ={
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''strength''': 0.75,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def UpperCAmelCase__ ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase : List[str] =self.get_dummy_inputs()
UpperCAmelCase : int =pipe(**__UpperCamelCase ).images
UpperCAmelCase : Tuple =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Optional[int] =np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
UpperCAmelCase : List[Any] =PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase : Optional[int] =self.get_dummy_inputs()
UpperCAmelCase : str =pipe(**__UpperCamelCase ).images
UpperCAmelCase : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
UpperCAmelCase : Dict =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
# warmup pass to apply optimizations
UpperCAmelCase : Dict =pipe(**self.get_dummy_inputs() )
UpperCAmelCase : int =self.get_dummy_inputs()
UpperCAmelCase : List[str] =pipe(**__UpperCamelCase ).images
UpperCAmelCase : int =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Optional[int] =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
UpperCAmelCase : List[str] =EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase : List[str] =self.get_dummy_inputs()
UpperCAmelCase : Any =pipe(**__UpperCamelCase ).images
UpperCAmelCase : Union[str, Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Dict =np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
UpperCAmelCase : str =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase : List[str] =self.get_dummy_inputs()
UpperCAmelCase : List[str] =pipe(**__UpperCamelCase ).images
UpperCAmelCase : List[str] =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Union[str, Any] =np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[Any] =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' )
UpperCAmelCase : Optional[Any] =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase : Union[str, Any] =self.get_dummy_inputs()
UpperCAmelCase : int =pipe(**__UpperCamelCase ).images
UpperCAmelCase : str =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
UpperCAmelCase : Any =np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
@property
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[str] =ort.SessionOptions()
UpperCAmelCase : Optional[Any] =False
return options
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
UpperCAmelCase : int =init_image.resize((768, 512) )
# using the PNDM scheduler by default
UpperCAmelCase : Optional[int] =OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''onnx''' , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase : str ='''A fantasy landscape, trending on artstation'''
UpperCAmelCase : Optional[int] =np.random.RandomState(0 )
UpperCAmelCase : Union[str, Any] =pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=10 , generator=__UpperCamelCase , output_type='''np''' , )
UpperCAmelCase : int =output.images
UpperCAmelCase : List[str] =images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
UpperCAmelCase : str =np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def UpperCAmelCase__ ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/img2img/sketch-mountains-input.jpg''' )
UpperCAmelCase : Optional[Any] =init_image.resize((768, 512) )
UpperCAmelCase : List[str] =LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , subfolder='''scheduler''' , revision='''onnx''' )
UpperCAmelCase : Dict =OnnxStableDiffusionImgaImgPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , revision='''onnx''' , scheduler=__UpperCamelCase , safety_checker=__UpperCamelCase , feature_extractor=__UpperCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
UpperCAmelCase : List[str] ='''A fantasy landscape, trending on artstation'''
UpperCAmelCase : List[Any] =np.random.RandomState(0 )
UpperCAmelCase : Optional[int] =pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , strength=0.75 , guidance_scale=7.5 , num_inference_steps=20 , generator=__UpperCamelCase , output_type='''np''' , )
UpperCAmelCase : Tuple =output.images
UpperCAmelCase : str =images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
UpperCAmelCase : List[Any] =np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 348
|
"""simple docstring"""
_snake_case : Optional[int] = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 292
| 0
|
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__A ='''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
'''
__A ='''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''
__A ='''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
predictions: list of predictions to score.
Each translation should be tokenized into a list of tokens.
references: list of lists of references for each translation.
Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
"accuracy": Accuracy
"f1": F1 score
"pearson": Pearson Correlation
"spearmanr": Spearman Correlation
"matthews_correlation": Matthew Correlation
Examples:
>>> glue_metric = datasets.load_metric(\'glue\', \'sst2\') # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0}
>>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\') # \'mrpc\' or \'qqp\'
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'accuracy\': 1.0, \'f1\': 1.0}
>>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
>>> references = [0., 1., 2., 3., 4., 5.]
>>> predictions = [0., 1., 2., 3., 4., 5.]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
{\'pearson\': 1.0, \'spearmanr\': 1.0}
>>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'matthews_correlation\': 1.0}
'''
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
return float((preds == labels).mean() )
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = simple_accuracy(lowerCamelCase__ , lowerCamelCase__ )
lowerCamelCase_ = float(fa_score(y_true=lowerCamelCase__ , y_pred=lowerCamelCase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
lowerCamelCase_ = float(pearsonr(lowerCamelCase__ , lowerCamelCase__ )[0] )
lowerCamelCase_ = float(spearmanr(lowerCamelCase__ , lowerCamelCase__ )[0] )
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _SCREAMING_SNAKE_CASE ( datasets.Metric ):
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
if self.config_name not in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
"\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32" ),
"references": datasets.Value("int64" if self.config_name != "stsb" else "float32" ),
} ) , codebase_urls=[] , reference_urls=[] , format="numpy" , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Dict:
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(lowercase , lowercase )}
elif self.config_name == "stsb":
return pearson_and_spearman(lowercase , lowercase )
elif self.config_name in ["mrpc", "qqp"]:
return acc_and_fa(lowercase , lowercase )
elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
return {"accuracy": simple_accuracy(lowercase , lowercase )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
"\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]" )
| 47
|
import copy
import re
class _SCREAMING_SNAKE_CASE :
lowerCAmelCase__ = 'hp'
lowerCAmelCase__ = {}
lowerCAmelCase__ = None
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = prefix
lowerCamelCase_ = defaults
cls.build_naming_info()
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase , lowercase ) -> Optional[Any]:
if len(lowercase ) == 0:
return ""
lowerCamelCase_ = 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(lowercase ) + 1 ):
lowerCamelCase_ = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
lowerCamelCase_ = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(lowercase ):
lowerCamelCase_ = ""
while integer != 0:
lowerCamelCase_ = chr(ord("A" ) + integer % 10 ) + s
integer //= 10
return s
lowerCamelCase_ = 0
while True:
lowerCamelCase_ = word + "#" + int_to_alphabetic(lowercase )
if sword in info["reverse_short_word"]:
continue
else:
lowerCamelCase_ = sword
break
lowerCamelCase_ = short_word
lowerCamelCase_ = word
return short_word
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase , lowercase ) -> int:
lowerCamelCase_ = param_name.split("_" )
lowerCamelCase_ = [TrialShortNamer.shortname_for_word(lowercase , lowercase ) 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
lowerCamelCase_ = ["", "_"]
for separator in separators:
lowerCamelCase_ = separator.join(lowercase )
if shortname not in info["reverse_short_param"]:
lowerCamelCase_ = shortname
lowerCamelCase_ = param_name
return shortname
return param_name
@staticmethod
def SCREAMING_SNAKE_CASE_( lowercase , lowercase ) -> Optional[Any]:
lowerCamelCase_ = TrialShortNamer.shortname_for_key(lowercase , lowercase )
lowerCamelCase_ = short_name
lowerCamelCase_ = param_name
@classmethod
def SCREAMING_SNAKE_CASE_( cls ) -> Dict:
if cls.NAMING_INFO is not None:
return
lowerCamelCase_ = {
"short_word": {},
"reverse_short_word": {},
"short_param": {},
"reverse_short_param": {},
}
lowerCamelCase_ = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(lowercase , lowercase )
lowerCamelCase_ = info
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase ) -> Optional[int]:
cls.build_naming_info()
assert cls.PREFIX is not None
lowerCamelCase_ = [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
lowerCamelCase_ = cls.NAMING_INFO["short_param"][k]
if isinstance(lowercase , lowercase ):
lowerCamelCase_ = 1 if v else 0
lowerCamelCase_ = "" if isinstance(lowercase , (int, float) ) else "-"
lowerCamelCase_ = f'{key}{sep}{v}'
name.append(lowercase )
return "_".join(lowercase )
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase ) -> List[Any]:
lowerCamelCase_ = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
lowerCamelCase_ = []
else:
lowerCamelCase_ = repr.split("_" )
lowerCamelCase_ = {}
for value in values:
if "-" in value:
lowerCamelCase_ , lowerCamelCase_ = value.split("-" )
else:
lowerCamelCase_ = re.sub("[0-9.]" , "" , lowercase )
lowerCamelCase_ = float(re.sub("[^0-9.]" , "" , lowercase ) )
lowerCamelCase_ = cls.NAMING_INFO["reverse_short_param"][p_k]
lowerCamelCase_ = p_v
for k in cls.DEFAULTS:
if k not in parameters:
lowerCamelCase_ = cls.DEFAULTS[k]
return parameters
| 47
| 1
|
"""simple docstring"""
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
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[int] = ["pixel_values"]
def __init__( self , __A = True , __A = None , __A = PILImageResampling.BICUBIC , __A = True , __A = None , __A = True , __A = 1 / 255 , __A = True , __A = IMAGENET_DEFAULT_MEAN , __A = IMAGENET_DEFAULT_STD , **__A , ) -> None:
super().__init__(**__A )
lowerCAmelCase_ :Optional[int] = size if size is not None else {"""shortest_edge""": 224}
lowerCAmelCase_ :Optional[Any] = get_size_dict(__A , default_to_square=__A )
lowerCAmelCase_ :int = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowerCAmelCase_ :Dict = get_size_dict(__A , param_name="""crop_size""" )
lowerCAmelCase_ :int = do_resize
lowerCAmelCase_ :Optional[Any] = size
lowerCAmelCase_ :str = resample
lowerCAmelCase_ :str = do_center_crop
lowerCAmelCase_ :List[Any] = crop_size
lowerCAmelCase_ :Optional[Any] = do_rescale
lowerCAmelCase_ :Optional[int] = rescale_factor
lowerCAmelCase_ :Any = do_normalize
lowerCAmelCase_ :List[str] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowerCAmelCase_ :Union[str, Any] = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def __lowerCAmelCase ( self , __A , __A , __A = PILImageResampling.BICUBIC , __A = None , **__A , ) -> np.ndarray:
lowerCAmelCase_ :Any = get_size_dict(__A , default_to_square=__A )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
lowerCAmelCase_ :Optional[Any] = int((256 / 224) * size["""shortest_edge"""] )
lowerCAmelCase_ :Tuple = get_resize_output_image_size(__A , size=__A , default_to_square=__A )
lowerCAmelCase_ :str = {"""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(
__A , size=(size_dict["""height"""], size_dict["""width"""]) , resample=__A , data_format=__A , **__A )
def __lowerCAmelCase ( self , __A , __A , __A = None , **__A , ) -> np.ndarray:
lowerCAmelCase_ :Any = get_size_dict(__A )
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(__A , size=(size["""height"""], size["""width"""]) , data_format=__A , **__A )
def __lowerCAmelCase ( self , __A , __A , __A = None , **__A , ) -> np.ndarray:
return rescale(__A , scale=__A , data_format=__A , **__A )
def __lowerCAmelCase ( self , __A , __A , __A , __A = None , **__A , ) -> np.ndarray:
return normalize(__A , mean=__A , std=__A , data_format=__A , **__A )
def __lowerCAmelCase ( self , __A , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = None , __A = ChannelDimension.FIRST , **__A , ) -> BatchFeature:
lowerCAmelCase_ :Union[str, Any] = do_resize if do_resize is not None else self.do_resize
lowerCAmelCase_ :Union[str, Any] = resample if resample is not None else self.resample
lowerCAmelCase_ :Optional[int] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCAmelCase_ :Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
lowerCAmelCase_ :str = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCAmelCase_ :str = do_normalize if do_normalize is not None else self.do_normalize
lowerCAmelCase_ :Any = image_mean if image_mean is not None else self.image_mean
lowerCAmelCase_ :List[Any] = image_std if image_std is not None else self.image_std
lowerCAmelCase_ :List[Any] = size if size is not None else self.size
lowerCAmelCase_ :str = get_size_dict(__A , default_to_square=__A )
lowerCAmelCase_ :Optional[int] = crop_size if crop_size is not None else self.crop_size
lowerCAmelCase_ :int = get_size_dict(__A , param_name="""crop_size""" )
lowerCAmelCase_ :Optional[Any] = make_list_of_images(__A )
if not valid_images(__A ):
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.
lowerCAmelCase_ :Any = [to_numpy_array(__A ) for image in images]
if do_resize:
lowerCAmelCase_ :Tuple = [self.resize(__A , __A , __A ) for image in images]
if do_center_crop:
lowerCAmelCase_ :int = [self.center_crop(__A , __A ) for image in images]
if do_rescale:
lowerCAmelCase_ :Optional[int] = [self.rescale(__A , __A ) for image in images]
if do_normalize:
lowerCAmelCase_ :List[str] = [self.normalize(__A , __A , __A ) for image in images]
lowerCAmelCase_ :Tuple = [to_channel_dimension_format(__A , __A ) for image in images]
lowerCAmelCase_ :List[str] = {"""pixel_values""": images}
return BatchFeature(data=__A , tensor_type=__A )
| 84
|
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
lowerCAmelCase_ :Union[str, Any] = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
lowerCAmelCase_ :Any = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCAmelCase_ :List[str] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowerCAmelCase_ :Tuple = key.replace(f"""patch_embed{idx}""" , f"""patch_embeddings.{int(lowercase__ )-1}""" )
if "norm" in key:
lowerCAmelCase_ :Dict = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCAmelCase_ :str = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
lowerCAmelCase_ :str = key.replace(f"""layer_norm{idx}""" , f"""layer_norm.{int(lowercase__ )-1}""" )
if "layer_norm1" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowerCAmelCase_ :str = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowerCAmelCase_ :List[str] = key[key.find("""block""" ) + len("""block""" )]
lowerCAmelCase_ :int = key.replace(f"""block{idx}""" , f"""block.{int(lowercase__ )-1}""" )
if "attn.q" in key:
lowerCAmelCase_ :Tuple = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowerCAmelCase_ :Optional[int] = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowerCAmelCase_ :str = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowerCAmelCase_ :List[Any] = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowerCAmelCase_ :List[str] = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowerCAmelCase_ :str = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowerCAmelCase_ :Any = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCAmelCase_ :str = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowerCAmelCase_ :Optional[int] = key.replace(f"""linear_c{idx}""" , f"""linear_c.{int(lowercase__ )-1}""" )
if "bot_conv" in key:
lowerCAmelCase_ :Union[str, Any] = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
lowerCAmelCase_ :int = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
lowerCAmelCase_ :str = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
lowerCAmelCase_ :Any = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
lowerCAmelCase_ :List[str] = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
lowerCAmelCase_ :Dict = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
lowerCAmelCase_ :Any = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
lowerCAmelCase_ :Tuple = key.replace("""module.last_layer_depth""" , """head.head""" )
lowerCAmelCase_ :List[Any] = value
return new_state_dict
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
lowerCAmelCase_ :Optional[Any] = kv_weight[
: config.hidden_sizes[i], :
]
lowerCAmelCase_ :Union[str, Any] = kv_bias[: config.hidden_sizes[i]]
lowerCAmelCase_ :List[Any] = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCAmelCase_ :int = kv_bias[config.hidden_sizes[i] :]
def _snake_case ( ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ :Optional[Any] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return image
@torch.no_grad()
def _snake_case ( lowercase__ : List[Any] , lowercase__ : str , lowercase__ : Dict=False , lowercase__ : List[Any]=None ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] , decoder_hidden_size=6_4 , depths=[3, 8, 2_7, 3] )
# load image processor (only resize + rescale)
lowerCAmelCase_ :Union[str, Any] = GLPNImageProcessor()
# prepare image
lowerCAmelCase_ :List[Any] = prepare_img()
lowerCAmelCase_ :int = image_processor(images=lowercase__ , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
lowerCAmelCase_ :Tuple = torch.load(lowercase__ , map_location=torch.device("""cpu""" ) )
# rename keys
lowerCAmelCase_ :Union[str, Any] = rename_keys(lowercase__ )
# key and value matrices need special treatment
read_in_k_v(lowercase__ , lowercase__ )
# create HuggingFace model and load state dict
lowerCAmelCase_ :List[Any] = GLPNForDepthEstimation(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
# forward pass
lowerCAmelCase_ :Dict = model(lowercase__ )
lowerCAmelCase_ :Tuple = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowerCAmelCase_ :Optional[Any] = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
lowerCAmelCase_ :Any = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
lowerCAmelCase_ :Union[str, Any] = torch.Size([1, 4_8_0, 6_4_0] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , lowercase__ , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowercase__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowercase__ , )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path 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.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__UpperCAmelCase = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 84
| 1
|
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
lowercase : Dict = imread(r"digital_image_processing/image_data/lena_small.jpg")
lowercase : Optional[int] = cvtColor(img, COLOR_BGR2GRAY)
def SCREAMING_SNAKE_CASE__ ( ) -> Union[str, Any]:
_snake_case = cn.convert_to_negative(_UpperCamelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
with Image.open('digital_image_processing/image_data/lena_small.jpg' ) as img:
# Work around assertion for response
assert str(cc.change_contrast(_UpperCamelCase , 110 ) ).startswith(
'<PIL.Image.Image image mode=RGB size=100x100 at' )
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
_snake_case = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def SCREAMING_SNAKE_CASE__ ( ) -> Any:
_snake_case = imread('digital_image_processing/image_data/lena_small.jpg' , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
_snake_case = canny.canny(_UpperCamelCase )
# assert canny array for at least one True
assert canny_array.any()
def SCREAMING_SNAKE_CASE__ ( ) -> Any:
assert gg.gaussian_filter(_UpperCamelCase , 5 , sigma=0.9 ).all()
def SCREAMING_SNAKE_CASE__ ( ) -> int:
_snake_case = array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]] )
_snake_case = conv.img_convolve(_UpperCamelCase , _UpperCamelCase ).astype(_UpperCamelCase )
assert res.any()
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
assert med.median_filter(_UpperCamelCase , 3 ).any()
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[Any]:
_snake_case , _snake_case = sob.sobel_filter(_UpperCamelCase )
assert grad.any() and theta.any()
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
_snake_case = sp.make_sepia(_UpperCamelCase , 20 )
assert sepia.all()
def SCREAMING_SNAKE_CASE__ ( __A = "digital_image_processing/image_data/lena_small.jpg" ) -> str:
_snake_case = bs.Burkes(imread(_UpperCamelCase , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def SCREAMING_SNAKE_CASE__ ( __A = "digital_image_processing/image_data/lena_small.jpg" , ) -> Dict:
_snake_case = rs.NearestNeighbour(imread(_UpperCamelCase , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def SCREAMING_SNAKE_CASE__ ( ) -> Optional[Any]:
_snake_case = 'digital_image_processing/image_data/lena.jpg'
# Reading the image and converting it to grayscale.
_snake_case = imread(_UpperCamelCase , 0 )
# Test for get_neighbors_pixel function() return not None
_snake_case = 0
_snake_case = 0
_snake_case = image[x_coordinate][y_coordinate]
_snake_case = lbp.get_neighbors_pixel(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
_snake_case = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
_snake_case = lbp.local_binary_value(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
assert lbp_image.any()
| 359
|
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class __UpperCAmelCase :
@staticmethod
def lowerCamelCase ( *lowerCAmelCase_ , **lowerCAmelCase_ ):
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def SCREAMING_SNAKE_CASE__ ( __A ) -> Dict:
_snake_case = np.array(__A )
_snake_case = npimg.shape
return {"hash": hashimage(__A ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
__lowercase = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
__lowercase = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = MaskGenerationPipeline(model=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
pass
@require_tf
@unittest.skip('Image segmentation not implemented in TF' )
def lowerCamelCase ( self ):
"""simple docstring"""
pass
@slow
@require_torch
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = pipeline('mask-generation' , model='facebook/sam-vit-huge' )
_snake_case = image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' , points_per_batch=2_56 )
# Shortening by hashing
_snake_case = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCAmelCase_ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (4_80, 6_40)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.021},
{'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0053},
{'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_80, 6_40)}, 'scores': 0.9967},
{'mask': {'hash': '453c7844bd', 'shape': (4_80, 6_40)}, 'scores': 0.993},
{'mask': {'hash': '3d44f2926d', 'shape': (4_80, 6_40)}, 'scores': 0.9909},
{'mask': {'hash': '64033ddc3f', 'shape': (4_80, 6_40)}, 'scores': 0.9879},
{'mask': {'hash': '801064ff79', 'shape': (4_80, 6_40)}, 'scores': 0.9834},
{'mask': {'hash': '6172f276ef', 'shape': (4_80, 6_40)}, 'scores': 0.9716},
{'mask': {'hash': 'b49e60e084', 'shape': (4_80, 6_40)}, 'scores': 0.9612},
{'mask': {'hash': 'a811e775fd', 'shape': (4_80, 6_40)}, 'scores': 0.9599},
{'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_80, 6_40)}, 'scores': 0.9552},
{'mask': {'hash': '9d8257e080', 'shape': (4_80, 6_40)}, 'scores': 0.9532},
{'mask': {'hash': '32de6454a8', 'shape': (4_80, 6_40)}, 'scores': 0.9516},
{'mask': {'hash': 'af3d4af2c8', 'shape': (4_80, 6_40)}, 'scores': 0.9499},
{'mask': {'hash': '3c6db475fb', 'shape': (4_80, 6_40)}, 'scores': 0.9483},
{'mask': {'hash': 'c290813fb9', 'shape': (4_80, 6_40)}, 'scores': 0.9464},
{'mask': {'hash': 'b6f0b8f606', 'shape': (4_80, 6_40)}, 'scores': 0.943},
{'mask': {'hash': '92ce16bfdf', 'shape': (4_80, 6_40)}, 'scores': 0.943},
{'mask': {'hash': 'c749b25868', 'shape': (4_80, 6_40)}, 'scores': 0.9408},
{'mask': {'hash': 'efb6cab859', 'shape': (4_80, 6_40)}, 'scores': 0.9335},
{'mask': {'hash': '1ff2eafb30', 'shape': (4_80, 6_40)}, 'scores': 0.9326},
{'mask': {'hash': '788b798e24', 'shape': (4_80, 6_40)}, 'scores': 0.9262},
{'mask': {'hash': 'abea804f0e', 'shape': (4_80, 6_40)}, 'scores': 0.8999},
{'mask': {'hash': '7b9e8ddb73', 'shape': (4_80, 6_40)}, 'scores': 0.8986},
{'mask': {'hash': 'cd24047c8a', 'shape': (4_80, 6_40)}, 'scores': 0.8984},
{'mask': {'hash': '6943e6bcbd', 'shape': (4_80, 6_40)}, 'scores': 0.8873},
{'mask': {'hash': 'b5f47c9191', 'shape': (4_80, 6_40)}, 'scores': 0.8871}
] , )
# fmt: on
@require_torch
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = 'facebook/sam-vit-huge'
_snake_case = pipeline('mask-generation' , model=lowerCAmelCase_ )
_snake_case = image_segmenter(
'http://images.cocodataset.org/val2017/000000039769.jpg' , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
_snake_case = []
for i, o in enumerate(outputs['masks'] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCAmelCase_ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(lowerCAmelCase_ , decimals=4 ) , [
{'mask': {'hash': '115ad19f5f', 'shape': (4_80, 6_40)}, 'scores': 1.0444},
{'mask': {'hash': '6affa964c6', 'shape': (4_80, 6_40)}, 'scores': 1.0210},
{'mask': {'hash': 'dfe28a0388', 'shape': (4_80, 6_40)}, 'scores': 1.0167},
{'mask': {'hash': 'c0a5f4a318', 'shape': (4_80, 6_40)}, 'scores': 1.0132},
{'mask': {'hash': 'fe8065c197', 'shape': (4_80, 6_40)}, 'scores': 1.0053},
] , )
| 160
| 0
|
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowerCamelCase : int = False
class A__ ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class A__ ( unittest.TestCase ):
def A ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A ( self : List[str] ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_SCREAMING_SNAKE_CASE =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
_SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE =pipe.dual_guided(
prompt='first prompt' , image=_a , text_to_image_strength=0.75 , generator=_a , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_a )
_SCREAMING_SNAKE_CASE =VersatileDiffusionPipeline.from_pretrained(_a , torch_dtype=torch.floataa )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_SCREAMING_SNAKE_CASE =generator.manual_seed(0 )
_SCREAMING_SNAKE_CASE =pipe.dual_guided(
prompt='first prompt' , image=_a , text_to_image_strength=0.75 , generator=_a , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def A ( self : int ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_SCREAMING_SNAKE_CASE ='cyberpunk 2077'
_SCREAMING_SNAKE_CASE =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
_SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE =pipe.dual_guided(
prompt=_a , image=_a , text_to_image_strength=0.75 , generator=_a , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' , ).images
_SCREAMING_SNAKE_CASE =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_SCREAMING_SNAKE_CASE =np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
_SCREAMING_SNAKE_CASE ='A painting of a squirrel eating a burger '
_SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE =pipe.text_to_image(
prompt=_a , generator=_a , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' ).images
_SCREAMING_SNAKE_CASE =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_SCREAMING_SNAKE_CASE =np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
_SCREAMING_SNAKE_CASE =pipe.image_variation(_a , generator=_a , output_type='numpy' ).images
_SCREAMING_SNAKE_CASE =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_SCREAMING_SNAKE_CASE =np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 47
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCamelCase : Optional[int] = False
class A__ ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def A ( self : Tuple ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion' )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_SCREAMING_SNAKE_CASE =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
_SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE =pipe(
image=_a , generator=_a , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' , ).images
_SCREAMING_SNAKE_CASE =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_SCREAMING_SNAKE_CASE =np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 47
| 1
|
"""simple docstring"""
def lowercase ( a__ : list[list[int]] , a__ : int , a__ : int , a__ : list[int] ) -> bool:
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def lowercase ( a__ : list[list[int]] , a__ : list[int] , a__ : int ) -> bool:
# Base Case
if curr_ind == len(a__ ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(a__ ) ):
if valid_connection(a__ , a__ , a__ , a__ ):
# Insert current vertex into path as next transition
_UpperCamelCase = next_ver
# Validate created path
if util_hamilton_cycle(a__ , a__ , curr_ind + 1 ):
return True
# Backtrack
_UpperCamelCase = -1
return False
def lowercase ( a__ : list[list[int]] , a__ : int = 0 ) -> list[int]:
_UpperCamelCase = [-1] * (len(a__ ) + 1)
# initialize start and end of path with starting index
_UpperCamelCase = _UpperCamelCase = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(a__ , a__ , 1 ) else []
| 368
|
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( _lowercase):
snake_case__ = ['''image_processor''', '''tokenizer''']
snake_case__ = '''BlipImageProcessor'''
snake_case__ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[Any] , __UpperCamelCase : List[str] , __UpperCamelCase : Optional[int] ) -> int:
_UpperCamelCase = False
super().__init__(__UpperCamelCase , __UpperCamelCase )
_UpperCamelCase = self.image_processor
def __call__( self : Any , __UpperCamelCase : ImageInput = None , __UpperCamelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __UpperCamelCase : bool = True , __UpperCamelCase : Union[bool, str, PaddingStrategy] = False , __UpperCamelCase : Union[bool, str, TruncationStrategy] = None , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : int = 0 , __UpperCamelCase : Optional[int] = None , __UpperCamelCase : Optional[bool] = None , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : bool = False , __UpperCamelCase : bool = True , __UpperCamelCase : Optional[Union[str, TensorType]] = None , **__UpperCamelCase : List[str] , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError('''You have to specify either images or text.''' )
# Get only text
if images is None:
_UpperCamelCase = self.tokenizer
_UpperCamelCase = self.tokenizer(
text=__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , stride=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_attention_mask=__UpperCamelCase , return_overflowing_tokens=__UpperCamelCase , return_special_tokens_mask=__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , return_token_type_ids=__UpperCamelCase , return_length=__UpperCamelCase , verbose=__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase , )
return text_encoding
# add pixel_values
_UpperCamelCase = self.image_processor(__UpperCamelCase , return_tensors=__UpperCamelCase )
if text is not None:
_UpperCamelCase = self.tokenizer(
text=__UpperCamelCase , add_special_tokens=__UpperCamelCase , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=__UpperCamelCase , stride=__UpperCamelCase , pad_to_multiple_of=__UpperCamelCase , return_attention_mask=__UpperCamelCase , return_overflowing_tokens=__UpperCamelCase , return_special_tokens_mask=__UpperCamelCase , return_offsets_mapping=__UpperCamelCase , return_token_type_ids=__UpperCamelCase , return_length=__UpperCamelCase , verbose=__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase , )
else:
_UpperCamelCase = None
if text_encoding is not None:
encoding_image_processor.update(__UpperCamelCase )
return encoding_image_processor
def _UpperCamelCase ( self : Union[str, Any] , *__UpperCamelCase : str , **__UpperCamelCase : Any ) -> List[Any]:
return self.tokenizer.batch_decode(*__UpperCamelCase , **__UpperCamelCase )
def _UpperCamelCase ( self : Optional[int] , *__UpperCamelCase : List[Any] , **__UpperCamelCase : str ) -> str:
return self.tokenizer.decode(*__UpperCamelCase , **__UpperCamelCase )
@property
def _UpperCamelCase ( self : List[str] ) -> Dict:
_UpperCamelCase = self.tokenizer.model_input_names
_UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 54
| 0
|
'''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 lowercase_ ( a__ , unittest.TestCase ):
__UpperCAmelCase = CLIPTokenizer
__UpperCAmelCase = CLIPTokenizerFast
__UpperCAmelCase = True
__UpperCAmelCase = {}
__UpperCAmelCase = False
def __a ( self ):
super().setUp()
# fmt: off
UpperCamelCase__ = ["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
UpperCamelCase__ = dict(zip(a , range(len(a ) ) ) )
UpperCamelCase__ = ["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
UpperCamelCase__ = {"unk_token": "<unk>"}
UpperCamelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCamelCase__ = 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(a ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(a ) )
def __a ( self , **a ):
kwargs.update(self.special_tokens_map )
return CLIPTokenizer.from_pretrained(self.tmpdirname , **a )
def __a ( self , **a ):
kwargs.update(self.special_tokens_map )
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **a )
def __a ( self , a ):
UpperCamelCase__ = "lower newer"
UpperCamelCase__ = "lower newer"
return input_text, output_text
def __a ( self ):
UpperCamelCase__ = CLIPTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCamelCase__ = "lower newer"
UpperCamelCase__ = ["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
UpperCamelCase__ = tokenizer.tokenize(a )
self.assertListEqual(a , a )
UpperCamelCase__ = tokens + [tokenizer.unk_token]
UpperCamelCase__ = [10, 2, 16, 9, 3, 2, 16, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(a ) , a )
@require_ftfy
def __a ( self ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
UpperCamelCase__ = self.tokenizer_class.from_pretrained(a , **a )
UpperCamelCase__ = self.rust_tokenizer_class.from_pretrained(a , **a )
UpperCamelCase__ = "A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
UpperCamelCase__ = tokenizer_s.tokenize(a )
UpperCamelCase__ = tokenizer_r.tokenize(a )
self.assertListEqual(a , a )
# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
# with Tilde) encoded in 2 different ways
UpperCamelCase__ = "xa\u0303y" + " " + "x\xe3y"
UpperCamelCase__ = tokenizer_s.tokenize(a )
UpperCamelCase__ = tokenizer_r.tokenize(a )
self.assertListEqual(a , a )
# Test that the tokenization is identical on unicode of space type
UpperCamelCase__ = [
"\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:
UpperCamelCase__ = tokenizer_s.tokenize(a )
UpperCamelCase__ = tokenizer_r.tokenize(a )
self.assertListEqual(a , a )
# Test that the tokenization is identical on unicode of line break type
UpperCamelCase__ = [
"\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:
UpperCamelCase__ = tokenizer_s.tokenize(a )
UpperCamelCase__ = tokenizer_r.tokenize(a )
self.assertListEqual(a , a )
def __a ( self ):
# 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})''' ):
UpperCamelCase__ = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
UpperCamelCase__ = f'''{text_of_1_token} {text_of_1_token}'''
UpperCamelCase__ = self.rust_tokenizer_class.from_pretrained(
a , use_fast=a , )
UpperCamelCase__ = tokenizer_r(a , return_offsets_mapping=a , add_special_tokens=a )
self.assertEqual(encoding.offset_mapping[0] , (0, len(a )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(a ) + 1, len(a ) + 1 + len(a )) , )
UpperCamelCase__ = f''' {text}'''
UpperCamelCase__ = self.rust_tokenizer_class.from_pretrained(
a , use_fast=a , )
UpperCamelCase__ = tokenizer_r(a , return_offsets_mapping=a , add_special_tokens=a )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(a )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(a ) + 1, 1 + len(a ) + 1 + len(a )) , )
def __a ( self ):
# 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(a ) 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 __a ( self ):
super().test_tokenization_python_rust_equals()
def __a ( self ):
# CLIP always lower cases letters
pass
| 80
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def __lowerCamelCase ( _lowercase ) -> Tuple:
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Any = create_tensor(_lowercase )
UpperCAmelCase : Union[str, Any] = gather(_lowercase )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
UpperCAmelCase : Any = [state.process_index]
UpperCAmelCase : Union[str, Any] = gather_object(_lowercase )
assert len(_lowercase ) == state.num_processes, F'''{gathered_obj}, {len(_lowercase )} != {state.num_processes}'''
assert gathered_obj == list(range(state.num_processes ) ), F'''{gathered_obj} != {list(range(state.num_processes ) )}'''
def __lowerCamelCase ( _lowercase ) -> List[Any]:
UpperCAmelCase : Optional[int] = create_tensor(_lowercase )
UpperCAmelCase : List[str] = broadcast(_lowercase )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def __lowerCamelCase ( _lowercase ) -> Tuple:
# We need to pad the tensor with one more element if we are the main process
# to ensure that we can pad
if state.is_main_process:
UpperCAmelCase : Optional[Any] = torch.arange(state.num_processes + 1 ).to(state.device )
else:
UpperCAmelCase : Tuple = torch.arange(state.num_processes ).to(state.device )
UpperCAmelCase : Optional[Any] = pad_across_processes(_lowercase )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def __lowerCamelCase ( _lowercase ) -> Dict:
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCAmelCase : Optional[Any] = create_tensor(_lowercase )
UpperCAmelCase : Optional[Any] = reduce(_lowercase , """sum""" )
UpperCAmelCase : Optional[Any] = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(_lowercase , _lowercase ), F'''{reduced_tensor} != {truth_tensor}'''
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
# For now runs on only two processes
if state.num_processes != 2:
return
UpperCAmelCase : Tuple = create_tensor(_lowercase )
UpperCAmelCase : Optional[int] = reduce(_lowercase , """mean""" )
UpperCAmelCase : str = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(_lowercase , _lowercase ), F'''{reduced_tensor} != {truth_tensor}'''
def __lowerCamelCase ( _lowercase ) -> Optional[int]:
# For xla_spawn (TPUs)
main()
def __lowerCamelCase ( ) -> int:
UpperCAmelCase : List[Any] = PartialState()
state.print(F'''State: {state}''' )
state.print("""testing gather""" )
test_gather(_lowercase )
state.print("""testing gather_object""" )
test_gather_object(_lowercase )
state.print("""testing broadcast""" )
test_broadcast(_lowercase )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(_lowercase )
state.print("""testing reduce_sum""" )
test_reduce_sum(_lowercase )
state.print("""testing reduce_mean""" )
test_reduce_mean(_lowercase )
if __name__ == "__main__":
main()
| 265
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase: Optional[int] = logging.get_logger(__name__)
UpperCAmelCase: str = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
),
}
class UpperCamelCase ( snake_case ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = "lilt"
def __init__( self ,UpperCAmelCase_=3_05_22 ,UpperCAmelCase_=7_68 ,UpperCAmelCase_=12 ,UpperCAmelCase_=12 ,UpperCAmelCase_=30_72 ,UpperCAmelCase_="gelu" ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=0.1 ,UpperCAmelCase_=5_12 ,UpperCAmelCase_=2 ,UpperCAmelCase_=0.02 ,UpperCAmelCase_=1E-12 ,UpperCAmelCase_=0 ,UpperCAmelCase_="absolute" ,UpperCAmelCase_=None ,UpperCAmelCase_=4 ,UpperCAmelCase_=10_24 ,**UpperCAmelCase_ ,):
super().__init__(pad_token_id=UpperCAmelCase_ ,**UpperCAmelCase_ )
_lowercase : Dict = vocab_size
_lowercase : Tuple = hidden_size
_lowercase : Optional[Any] = num_hidden_layers
_lowercase : Union[str, Any] = num_attention_heads
_lowercase : Any = hidden_act
_lowercase : Dict = intermediate_size
_lowercase : Optional[Any] = hidden_dropout_prob
_lowercase : Any = attention_probs_dropout_prob
_lowercase : str = max_position_embeddings
_lowercase : Tuple = type_vocab_size
_lowercase : Tuple = initializer_range
_lowercase : Tuple = layer_norm_eps
_lowercase : List[Any] = position_embedding_type
_lowercase : int = classifier_dropout
_lowercase : str = channel_shrink_ratio
_lowercase : List[str] = max_ad_position_embeddings
| 367
|
"""simple docstring"""
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
UpperCAmelCase: str = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class UpperCamelCase ( datasets.BuilderConfig ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[datasets.Features] = None
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , ):
import pyspark
def generate_fn():
_lowercase : List[Any] = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
_lowercase : Optional[int] = df_with_partition_id.select("""*""" ).where(F"""part_id = {partition_id}""" ).drop("""part_id""" )
_lowercase : int = partition_df.collect()
_lowercase : Dict = 0
for row in rows:
yield F"""{partition_id}_{row_id}""", row.asDict()
row_id += 1
return generate_fn
class UpperCamelCase ( _BaseExamplesIterable ):
"""simple docstring"""
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_=None ,):
_lowercase : Union[str, Any] = df
_lowercase : List[str] = partition_order or range(self.df.rdd.getNumPartitions() )
_lowercase : Tuple = _generate_iterable_examples(self.df ,self.partition_order )
def __iter__( self ):
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
_lowercase : List[str] = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ):
_lowercase : Union[str, Any] = self.split_shard_indices_by_worker(UpperCAmelCase_ ,UpperCAmelCase_ )
return SparkExamplesIterable(self.df ,partition_order=UpperCAmelCase_ )
@property
def lowerCamelCase__ ( self ):
return len(self.partition_order )
class UpperCamelCase ( datasets.DatasetBuilder ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = SparkConfig
def __init__( self ,UpperCAmelCase_ ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
import pyspark
_lowercase : List[Any] = pyspark.sql.SparkSession.builder.getOrCreate()
_lowercase : List[Any] = df
_lowercase : int = working_dir
super().__init__(
cache_dir=UpperCAmelCase_ ,config_name=str(self.df.semanticHash() ) ,**UpperCAmelCase_ ,)
def lowerCamelCase__ ( self ):
# Returns the path of the created file.
def create_cache_and_write_probe(UpperCAmelCase_ ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir ,exist_ok=UpperCAmelCase_ )
_lowercase : Union[str, Any] = os.path.join(self._cache_dir ,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(UpperCAmelCase_ ,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""" ,"""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
_lowercase : List[str] = (
self._spark.sparkContext.parallelize(range(1 ) ,1 ).mapPartitions(UpperCAmelCase_ ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self ,UpperCAmelCase_ ):
import pyspark
def get_arrow_batch_size(UpperCAmelCase_ ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
_lowercase : List[str] = self.df.count()
_lowercase : List[str] = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
_lowercase : Union[str, Any] = (
self.df.limit(UpperCAmelCase_ )
.repartition(1 )
.mapInArrow(UpperCAmelCase_ ,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
_lowercase : List[Any] = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
_lowercase : int = min(UpperCAmelCase_ ,int(approx_total_size / max_shard_size ) )
_lowercase : List[Any] = self.df.repartition(UpperCAmelCase_ )
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
import pyspark
_lowercase : Union[str, Any] = ParquetWriter if file_format == """parquet""" else ArrowWriter
_lowercase : List[Any] = os.path.join(self._working_dir ,os.path.basename(UpperCAmelCase_ ) ) if self._working_dir else fpath
_lowercase : Any = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
_lowercase : Union[str, Any] = self.config.features
_lowercase : Optional[int] = self._writer_batch_size
_lowercase : Optional[Any] = self._fs.storage_options
def write_arrow(UpperCAmelCase_ ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
_lowercase : Any = pyspark.TaskContext().taskAttemptId()
_lowercase : List[str] = next(UpperCAmelCase_ ,UpperCAmelCase_ )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
_lowercase : List[Any] = 0
_lowercase : int = writer_class(
features=UpperCAmelCase_ ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Optional[int] = pa.Table.from_batches([first_batch] )
writer.write_table(UpperCAmelCase_ )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
_lowercase , _lowercase : Optional[Any] = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
shard_id += 1
_lowercase : Union[str, Any] = writer_class(
features=writer._features ,path=working_fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,writer_batch_size=UpperCAmelCase_ ,storage_options=UpperCAmelCase_ ,embed_local_files=UpperCAmelCase_ ,)
_lowercase : Dict = pa.Table.from_batches([batch] )
writer.write_table(UpperCAmelCase_ )
if writer._num_bytes > 0:
_lowercase , _lowercase : Dict = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]] ,names=["""task_id""", """num_examples""", """num_bytes"""] ,)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(UpperCAmelCase_ ) ):
_lowercase : Dict = os.path.join(os.path.dirname(UpperCAmelCase_ ) ,os.path.basename(UpperCAmelCase_ ) )
shutil.move(UpperCAmelCase_ ,UpperCAmelCase_ )
_lowercase : List[str] = (
self.df.mapInArrow(UpperCAmelCase_ ,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ) ,pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ) ,pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ) ,pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ) ,)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,UpperCAmelCase_ = "arrow" ,UpperCAmelCase_ = None ,UpperCAmelCase_ = None ,**UpperCAmelCase_ ,):
self._validate_cache_dir()
_lowercase : Tuple = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(UpperCAmelCase_ )
_lowercase : Optional[int] = not is_remote_filesystem(self._fs )
_lowercase : Dict = os.path.join if is_local else posixpath.join
_lowercase : int = """-TTTTT-SSSSS-of-NNNNN"""
_lowercase : Optional[Any] = f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
_lowercase : Dict = path_join(self._output_dir ,UpperCAmelCase_ )
_lowercase : List[Any] = 0
_lowercase : Optional[Any] = 0
_lowercase : int = 0
_lowercase : Any = []
_lowercase : Any = []
for task_id, content in self._prepare_split_single(UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : Tuple = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(UpperCAmelCase_ )
_lowercase : Optional[int] = total_num_examples
_lowercase : List[Any] = total_num_bytes
# should rename everything at the end
logger.debug(f"""Renaming {total_shards} shards.""" )
if total_shards > 1:
_lowercase : List[Any] = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
_lowercase : Union[str, Any] = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
UpperCAmelCase_ ,UpperCAmelCase_ ,UpperCAmelCase_ ,):
rename(
UpperCAmelCase_ ,fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace("""TTTTT-SSSSS""" ,f"""{global_shard_id:05d}""" ).replace("""NNNNN""" ,f"""{total_shards:05d}""" ) ,)
_lowercase : Optional[Any] = []
_lowercase : List[str] = 0
for i in range(len(UpperCAmelCase_ ) ):
_lowercase , _lowercase : List[str] = task_id_and_num_shards[i]
for shard_id in range(UpperCAmelCase_ ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(UpperCAmelCase_ ,len(UpperCAmelCase_ ) ).map(lambda UpperCAmelCase_ : _rename_shard(*UpperCAmelCase_ ) ).collect()
else:
# don't use any pattern
_lowercase : Tuple = 0
_lowercase : Optional[Any] = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""" ,f"""{shard_id:05d}""" ).replace("""TTTTT""" ,f"""{task_id:05d}""" ) ,fpath.replace(UpperCAmelCase_ ,"""""" ) ,)
def lowerCamelCase__ ( self ,UpperCAmelCase_ ,):
return SparkExamplesIterable(self.df )
| 336
| 0
|
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """M-CLIP"""
def __init__( self : Optional[Any] , UpperCAmelCase : Union[str, Any]=1024 , UpperCAmelCase : Tuple=768 , **UpperCAmelCase : Optional[int] ) -> Dict:
lowerCamelCase__ : Optional[int] = transformerDimSize
lowerCamelCase__ : Optional[Any] = imageDimSize
super().__init__(**UpperCAmelCase )
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = MCLIPConfig
def __init__( self : List[Any] , UpperCAmelCase : Dict , *UpperCAmelCase : Any , **UpperCAmelCase : Dict ) -> Dict:
super().__init__(UpperCAmelCase , *UpperCAmelCase , **UpperCAmelCase )
lowerCamelCase__ : Tuple = XLMRobertaModel(UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def A_ ( self : Optional[int] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Dict ) -> Tuple:
lowerCamelCase__ : Any = self.transformer(input_ids=UpperCAmelCase , attention_mask=UpperCAmelCase )[0]
lowerCamelCase__ : int = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(UpperCAmelCase ), embs
| 50
|
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger()
@dataclass
class a_ :
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = field(default_factory=snake_case_ )
UpperCamelCase = field(default_factory=snake_case_ )
def snake_case_( self , A , A , A ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = len(list(m.modules() ) ) == 1 or isinstance(A , nn.Convad ) or isinstance(A , nn.BatchNormad )
if has_not_submodules:
self.traced.append(A )
def __call__( self , A ) -> str:
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(A )
[x.remove() for x in self.handles]
return self
@property
def snake_case_( self ) -> str:
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda A : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class a_ :
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 0
UpperCamelCase = field(default_factory=snake_case_ )
UpperCamelCase = field(default_factory=snake_case_ )
def __call__( self , A ) -> List[str]:
_SCREAMING_SNAKE_CASE = Tracker(self.dest )(A ).parametrized
_SCREAMING_SNAKE_CASE = Tracker(self.src )(A ).parametrized
_SCREAMING_SNAKE_CASE = list(filter(lambda A : type(A ) not in self.src_skip , A ) )
_SCREAMING_SNAKE_CASE = list(filter(lambda A : type(A ) not in self.dest_skip , A ) )
if len(A ) != len(A ):
raise Exception(
f'Numbers of operations are different. Source module has {len(A )} operations while'
f' destination module has {len(A )}.' )
for dest_m, src_m in zip(A , A ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f'Transfered from={src_m} to={dest_m}' )
def lowerCamelCase ( __lowerCamelCase : str , __lowerCamelCase : ResNetConfig , __lowerCamelCase : Path , __lowerCamelCase : bool = True ) ->int:
print(F'Converting {name}...' )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = timm.create_model(__lowerCamelCase , pretrained=__lowerCamelCase ).eval()
_SCREAMING_SNAKE_CASE = ResNetForImageClassification(__lowerCamelCase ).eval()
_SCREAMING_SNAKE_CASE = ModuleTransfer(src=__lowerCamelCase , dest=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = torch.randn((1, 3, 224, 224) )
module_transfer(__lowerCamelCase )
assert torch.allclose(from_model(__lowerCamelCase ) , our_model(__lowerCamelCase ).logits ), "The model logits don't match the original one."
_SCREAMING_SNAKE_CASE = F'resnet{"-".join(name.split("resnet" ) )}'
print(__lowerCamelCase )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add model""" , use_temp_dir=__lowerCamelCase , )
# we can use the convnext one
_SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="""Add image processor""" , use_temp_dir=__lowerCamelCase , )
print(F'Pushed {checkpoint_name}' )
def lowerCamelCase ( __lowerCamelCase : Path , __lowerCamelCase : str = None , __lowerCamelCase : bool = True ) ->Any:
_SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
_SCREAMING_SNAKE_CASE = 1000
_SCREAMING_SNAKE_CASE = (1, num_labels)
_SCREAMING_SNAKE_CASE = """huggingface/label-files"""
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(__lowerCamelCase , __lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
_SCREAMING_SNAKE_CASE = {int(__lowerCamelCase ): 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 = partial(__lowerCamelCase , num_labels=__lowerCamelCase , idalabel=__lowerCamelCase , labelaid=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = {
"""resnet18""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type="""basic""" ),
"""resnet26""": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
"""resnet34""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type="""basic""" ),
"""resnet50""": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
"""resnet101""": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
"""resnet152""": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1024, 2048] , layer_type="""bottleneck""" ),
}
if model_name:
convert_weight_and_push(__lowerCamelCase , names_to_config[model_name] , __lowerCamelCase , __lowerCamelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
return config, expected_shape
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
lowercase_ = parser.parse_args()
lowercase_ = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 58
| 0
|
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : list , lowerCamelCase_ : list , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
if index == number_of_items:
return 0
UpperCAmelCase_ : int = 0
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[Any] = knapsack(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , index + 1 )
if weights[index] <= max_weight:
UpperCAmelCase_ : List[str] = values[index] + knapsack(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , max_weight - weights[index] , index + 1 )
return max(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352
|
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
snake_case__ : Optional[int] = '''1'''
snake_case__ : str = '''0'''
snake_case__ : List[str] = '''1'''
snake_case__ : List[str] = ort.SessionOptions()
snake_case__ : str = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__ : Dict = ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__ : Dict = ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__ : str = ort.RunOptions()
snake_case__ : List[Any] = 128
snake_case__ : Union[str, Any] = 1
snake_case__ : Tuple = np.ones((batch, sequence), dtype=np.intaa)
snake_case__ : Tuple = np.ones((batch, sequence), dtype=np.intaa)
snake_case__ : Union[str, Any] = 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...''')
snake_case__ : Union[str, Any] = time.time()
snake_case__ : str = 2000
snake_case__ : Tuple = {}
for iter in range(max_iters):
snake_case__ : str = 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) * 1000 / max_iters))
| 274
| 0
|
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')
# TF training parameters
UpperCAmelCase__ : int = False
UpperCAmelCase__ : List[Any] = False
def lowerCamelCase__ ( a ) -> List[Any]:
return TrainCommand(a )
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@staticmethod
def __magic_name__ ( lowerCAmelCase_ : ArgumentParser ):
"""simple docstring"""
_A: str = parser.add_parser('''train''' , help='''CLI tool to train a model on a task.''' )
train_parser.add_argument(
'''--train_data''' , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.''' , )
train_parser.add_argument(
'''--column_label''' , type=lowerCAmelCase_ , default=0 , help='''Column of the dataset csv file with example labels.''' )
train_parser.add_argument(
'''--column_text''' , type=lowerCAmelCase_ , default=1 , help='''Column of the dataset csv file with example texts.''' )
train_parser.add_argument(
'''--column_id''' , type=lowerCAmelCase_ , default=2 , help='''Column of the dataset csv file with example ids.''' )
train_parser.add_argument(
'''--skip_first_row''' , action='''store_true''' , help='''Skip the first row of the csv file (headers).''' )
train_parser.add_argument('''--validation_data''' , type=lowerCAmelCase_ , default='''''' , help='''path to validation dataset.''' )
train_parser.add_argument(
'''--validation_split''' , type=lowerCAmelCase_ , default=0.1 , help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.''' , )
train_parser.add_argument('''--output''' , type=lowerCAmelCase_ , default='''./''' , help='''path to saved the trained model.''' )
train_parser.add_argument(
'''--task''' , type=lowerCAmelCase_ , default='''text_classification''' , help='''Task to train the model on.''' )
train_parser.add_argument(
'''--model''' , type=lowerCAmelCase_ , default='''bert-base-uncased''' , help='''Model\'s name or path to stored model.''' )
train_parser.add_argument('''--train_batch_size''' , type=lowerCAmelCase_ , default=3_2 , help='''Batch size for training.''' )
train_parser.add_argument('''--valid_batch_size''' , type=lowerCAmelCase_ , default=6_4 , help='''Batch size for validation.''' )
train_parser.add_argument('''--learning_rate''' , type=lowerCAmelCase_ , default=3e-5 , help='''Learning rate.''' )
train_parser.add_argument('''--adam_epsilon''' , type=lowerCAmelCase_ , default=1e-08 , help='''Epsilon for Adam optimizer.''' )
train_parser.set_defaults(func=lowerCAmelCase_ )
def __init__( self : Union[str, Any] , lowerCAmelCase_ : Namespace ):
"""simple docstring"""
_A: Optional[Any] = logging.get_logger('''transformers-cli/training''' )
_A: Tuple = '''tf''' if is_tf_available() else '''torch'''
os.makedirs(args.output , exist_ok=lowerCAmelCase_ )
_A: Tuple = args.output
_A: str = args.column_label
_A: Optional[int] = args.column_text
_A: Any = args.column_id
self.logger.info(F"""Loading {args.task} pipeline for {args.model}""" )
if args.task == "text_classification":
_A: Optional[int] = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(F"""Loading dataset from {args.train_data}""" )
_A: Optional[Any] = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_A: int = None
if args.validation_data:
self.logger.info(F"""Loading validation dataset from {args.validation_data}""" )
_A: List[str] = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
_A: str = args.validation_split
_A: Optional[Any] = args.train_batch_size
_A: Tuple = args.valid_batch_size
_A: List[Any] = args.learning_rate
_A: str = args.adam_epsilon
def __magic_name__ ( self : Dict ):
"""simple docstring"""
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
raise NotImplementedError
def __magic_name__ ( self : int ):
"""simple docstring"""
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 121
|
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowerCamelCase__ ( a = True , *a , **a ) -> Optional[Any]:
if not is_tqdm_available():
raise ImportError('''Accelerate\'s `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`.''' )
_A: Optional[Any] = False
if main_process_only:
_A: Union[str, Any] = PartialState().local_process_index == 0
return _tqdm(*a , **a , disable=a )
| 121
| 1
|
"""simple docstring"""
def __lowerCamelCase ( a_ : int ) -> int:
if not isinstance(a_ , a_ ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''' )
__SCREAMING_SNAKE_CASE :Tuple = 0
__SCREAMING_SNAKE_CASE :Union[str, Any] = str(a_ )
while len(a_ ) != 1:
__SCREAMING_SNAKE_CASE :Tuple = [int(a_ ) for i in num_string]
__SCREAMING_SNAKE_CASE :List[Any] = 1
for i in range(0 , len(a_ ) ):
total *= numbers[i]
__SCREAMING_SNAKE_CASE :List[Any] = str(a_ )
steps += 1
return steps
def __lowerCamelCase ( a_ : int ) -> int:
if not isinstance(a_ , a_ ):
raise ValueError('''additive_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''additive_persistence() does not accept negative values''' )
__SCREAMING_SNAKE_CASE :int = 0
__SCREAMING_SNAKE_CASE :Tuple = str(a_ )
while len(a_ ) != 1:
__SCREAMING_SNAKE_CASE :Any = [int(a_ ) for i in num_string]
__SCREAMING_SNAKE_CASE :int = 0
for i in range(0 , len(a_ ) ):
total += numbers[i]
__SCREAMING_SNAKE_CASE :Union[str, Any] = str(a_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 239
|
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class _SCREAMING_SNAKE_CASE( A ):
def __init__( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,) -> Optional[int]:
"""simple docstring"""
super().__init__()
__SCREAMING_SNAKE_CASE :Any = value_function
__SCREAMING_SNAKE_CASE :List[str] = unet
__SCREAMING_SNAKE_CASE :int = scheduler
__SCREAMING_SNAKE_CASE :Optional[int] = env
__SCREAMING_SNAKE_CASE :Optional[int] = env.get_dataset()
__SCREAMING_SNAKE_CASE :str = {}
for key in self.data.keys():
try:
__SCREAMING_SNAKE_CASE :Optional[Any] = self.data[key].mean()
except: # noqa: E722
pass
__SCREAMING_SNAKE_CASE :Dict = {}
for key in self.data.keys():
try:
__SCREAMING_SNAKE_CASE :str = self.data[key].std()
except: # noqa: E722
pass
__SCREAMING_SNAKE_CASE :Optional[int] = env.observation_space.shape[0]
__SCREAMING_SNAKE_CASE :int = env.action_space.shape[0]
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
return (x_in - self.means[key]) / self.stds[key]
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Dict:
"""simple docstring"""
return x_in * self.stds[key] + self.means[key]
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
if type(SCREAMING_SNAKE_CASE__ ) is dict:
return {k: self.to_torch(SCREAMING_SNAKE_CASE__ ) for k, v in x_in.items()}
elif torch.is_tensor(SCREAMING_SNAKE_CASE__ ):
return x_in.to(self.unet.device )
return torch.tensor(SCREAMING_SNAKE_CASE__ ,device=self.unet.device )
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
for key, val in cond.items():
__SCREAMING_SNAKE_CASE :Dict = val.clone()
return x_in
def _UpperCamelCase ( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Union[str, Any] = x.shape[0]
__SCREAMING_SNAKE_CASE :List[Any] = None
for i in tqdm.tqdm(self.scheduler.timesteps ):
# create batch of timesteps to pass into model
__SCREAMING_SNAKE_CASE :Tuple = torch.full((batch_size,) ,SCREAMING_SNAKE_CASE__ ,device=self.unet.device ,dtype=torch.long )
for _ in range(SCREAMING_SNAKE_CASE__ ):
with torch.enable_grad():
x.requires_grad_()
# permute to match dimension for pre-trained models
__SCREAMING_SNAKE_CASE :str = self.value_function(x.permute(0 ,2 ,1 ) ,SCREAMING_SNAKE_CASE__ ).sample
__SCREAMING_SNAKE_CASE :Union[str, Any] = torch.autograd.grad([y.sum()] ,[x] )[0]
__SCREAMING_SNAKE_CASE :int = self.scheduler._get_variance(SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :Optional[Any] = torch.exp(0.5 * posterior_variance )
__SCREAMING_SNAKE_CASE :List[str] = model_std * grad
__SCREAMING_SNAKE_CASE :Dict = 0
__SCREAMING_SNAKE_CASE :List[Any] = x.detach()
__SCREAMING_SNAKE_CASE :Union[str, Any] = x + scale * grad
__SCREAMING_SNAKE_CASE :Any = self.reset_xa(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.action_dim )
__SCREAMING_SNAKE_CASE :Optional[int] = self.unet(x.permute(0 ,2 ,1 ) ,SCREAMING_SNAKE_CASE__ ).sample.permute(0 ,2 ,1 )
# TODO: verify deprecation of this kwarg
__SCREAMING_SNAKE_CASE :Optional[int] = self.scheduler.step(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,predict_epsilon=SCREAMING_SNAKE_CASE__ )['''prev_sample''']
# apply conditions to the trajectory (set the initial state)
__SCREAMING_SNAKE_CASE :List[str] = self.reset_xa(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.action_dim )
__SCREAMING_SNAKE_CASE :Dict = self.to_torch(SCREAMING_SNAKE_CASE__ )
return x, y
def __call__( self ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__=64 ,SCREAMING_SNAKE_CASE__=32 ,SCREAMING_SNAKE_CASE__=2 ,SCREAMING_SNAKE_CASE__=0.1 ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Tuple = self.normalize(SCREAMING_SNAKE_CASE__ ,'''observations''' )
__SCREAMING_SNAKE_CASE :List[Any] = obs[None].repeat(SCREAMING_SNAKE_CASE__ ,axis=0 )
__SCREAMING_SNAKE_CASE :str = {0: self.to_torch(SCREAMING_SNAKE_CASE__ )}
__SCREAMING_SNAKE_CASE :Optional[Any] = (batch_size, planning_horizon, self.state_dim + self.action_dim)
# generate initial noise and apply our conditions (to make the trajectories start at current state)
__SCREAMING_SNAKE_CASE :Optional[int] = randn_tensor(SCREAMING_SNAKE_CASE__ ,device=self.unet.device )
__SCREAMING_SNAKE_CASE :Tuple = self.reset_xa(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,self.action_dim )
__SCREAMING_SNAKE_CASE :Any = self.to_torch(SCREAMING_SNAKE_CASE__ )
# run the diffusion process
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Tuple = self.run_diffusion(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
# sort output trajectories by value
__SCREAMING_SNAKE_CASE :Any = y.argsort(0 ,descending=SCREAMING_SNAKE_CASE__ ).squeeze()
__SCREAMING_SNAKE_CASE :Any = x[sorted_idx]
__SCREAMING_SNAKE_CASE :str = sorted_values[:, :, : self.action_dim]
__SCREAMING_SNAKE_CASE :Union[str, Any] = actions.detach().cpu().numpy()
__SCREAMING_SNAKE_CASE :Optional[int] = self.de_normalize(SCREAMING_SNAKE_CASE__ ,key='''actions''' )
# select the action with the highest value
if y is not None:
__SCREAMING_SNAKE_CASE :Optional[int] = 0
else:
# if we didn't run value guiding, select a random action
__SCREAMING_SNAKE_CASE :Any = np.random.randint(0 ,SCREAMING_SNAKE_CASE__ )
__SCREAMING_SNAKE_CASE :int = denorm_actions[selected_index, 0]
return denorm_actions
| 239
| 1
|
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
a_ = [
('bert.bert', 'visual_bert'),
('bert.cls', 'cls'),
('bert.classifier', 'cls'),
('token_type_embeddings_visual', 'visual_token_type_embeddings'),
('position_embeddings_visual', 'visual_position_embeddings'),
('projection', 'visual_projection'),
]
a_ = [
'nlvr2_coco_pre_trained.th',
'nlvr2_fine_tuned.th',
'nlvr2_pre_trained.th',
'vcr_coco_pre_train.th',
'vcr_fine_tune.th',
'vcr_pre_train.th',
'vqa_coco_pre_trained.th',
'vqa_fine_tuned.th',
'vqa_pre_trained.th',
]
def __lowercase ( lowerCamelCase : List[Any] ):
UpperCamelCase_ : List[str] = torch.load(lowerCamelCase , map_location='cpu' )
return sd
def __lowercase ( lowerCamelCase : Tuple , lowerCamelCase : Optional[int] , lowerCamelCase : str=rename_keys_prefix ):
UpperCamelCase_ : Any = OrderedDict()
UpperCamelCase_ : Union[str, Any] = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
UpperCamelCase_ : str = key
for name_pair in rename_keys_prefix:
UpperCamelCase_ : List[str] = new_key.replace(name_pair[0] , name_pair[1] )
UpperCamelCase_ : Optional[Any] = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
UpperCamelCase_ : Dict = new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def __lowercase ( lowerCamelCase : Union[str, Any] , lowerCamelCase : str ):
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."
# Get Config
if "pre" in checkpoint_path:
UpperCamelCase_ : str = 'pretraining'
if "vcr" in checkpoint_path:
UpperCamelCase_ : List[Any] = {'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
UpperCamelCase_ : List[str] = {'visual_embedding_dim': 2048}
elif "vqa" in checkpoint_path:
UpperCamelCase_ : List[Any] = {'visual_embedding_dim': 2048}
elif "nlvr" in checkpoint_path:
UpperCamelCase_ : str = {'visual_embedding_dim': 1024}
else:
raise NotImplementedError(F"No implementation found for `{checkpoint_path}`." )
else:
if "vcr" in checkpoint_path:
UpperCamelCase_ : Any = {'visual_embedding_dim': 512}
UpperCamelCase_ : List[str] = 'multichoice'
elif "vqa_advanced" in checkpoint_path:
UpperCamelCase_ : List[Any] = {'visual_embedding_dim': 2048}
UpperCamelCase_ : Tuple = 'vqa_advanced'
elif "vqa" in checkpoint_path:
UpperCamelCase_ : Dict = {'visual_embedding_dim': 2048, 'num_labels': 3129}
UpperCamelCase_ : Any = 'vqa'
elif "nlvr" in checkpoint_path:
UpperCamelCase_ : Optional[int] = {
'visual_embedding_dim': 1024,
'num_labels': 2,
}
UpperCamelCase_ : Dict = 'nlvr'
UpperCamelCase_ : Tuple = VisualBertConfig(**lowerCamelCase )
# Load State Dict
UpperCamelCase_ : Dict = load_state_dict(lowerCamelCase )
UpperCamelCase_ : Optional[int] = get_new_dict(lowerCamelCase , lowerCamelCase )
if model_type == "pretraining":
UpperCamelCase_ : Optional[Any] = VisualBertForPreTraining(lowerCamelCase )
elif model_type == "vqa":
UpperCamelCase_ : List[str] = VisualBertForQuestionAnswering(lowerCamelCase )
elif model_type == "nlvr":
UpperCamelCase_ : List[Any] = VisualBertForVisualReasoning(lowerCamelCase )
elif model_type == "multichoice":
UpperCamelCase_ : List[str] = VisualBertForMultipleChoice(lowerCamelCase )
model.load_state_dict(lowerCamelCase )
# Save Checkpoints
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
a_ = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 175
|
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def __lowercase ( lowerCamelCase : Any ):
UpperCamelCase_ : Union[str, Any] = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
F"{test_file} instead." )
UpperCamelCase_ : str = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead." )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead." )
UpperCamelCase_ : Union[str, Any] = components[:-1] + [test_fn.replace('.py' , '' )]
UpperCamelCase_ : List[Any] = '.'.join(lowerCamelCase )
return test_module_path
def __lowercase ( lowerCamelCase : Optional[Any] ):
UpperCamelCase_ : List[Any] = get_module_path(lowerCamelCase )
UpperCamelCase_ : Union[str, Any] = importlib.import_module(lowerCamelCase )
return test_module
def __lowercase ( lowerCamelCase : List[str] ):
UpperCamelCase_ : int = []
UpperCamelCase_ : Tuple = get_test_module(lowerCamelCase )
for attr in dir(lowerCamelCase ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(lowerCamelCase , lowerCamelCase ) )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : List[str] = []
UpperCamelCase_ : Union[str, Any] = get_test_module(lowerCamelCase )
for attr in dir(lowerCamelCase ):
UpperCamelCase_ : Dict = getattr(lowerCamelCase , lowerCamelCase )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
UpperCamelCase_ : Optional[int] = getattr(lowerCamelCase , 'all_model_classes' , [] )
if len(lowerCamelCase ) > 0:
test_classes.append(lowerCamelCase )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : Dict ):
UpperCamelCase_ : int = get_test_classes(lowerCamelCase )
UpperCamelCase_ : List[Any] = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : Tuple ):
UpperCamelCase_ : int = test_class()
if hasattr(lowerCamelCase , 'setUp' ):
test.setUp()
UpperCamelCase_ : List[Any] = None
if hasattr(lowerCamelCase , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
UpperCamelCase_ : Optional[Any] = test.model_tester.__class__
return model_tester
def __lowercase ( lowerCamelCase : Tuple , lowerCamelCase : Dict ):
UpperCamelCase_ : Optional[Any] = get_test_classes(lowerCamelCase )
UpperCamelCase_ : Tuple = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(lowerCamelCase )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : Tuple ):
UpperCamelCase_ : List[Any] = get_test_classes_for_model(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : int = []
for test_class in test_classes:
UpperCamelCase_ : Tuple = get_model_tester_from_test_class(lowerCamelCase )
if tester_class is not None:
tester_classes.append(lowerCamelCase )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : Tuple = get_test_classes(lowerCamelCase )
UpperCamelCase_ : Tuple = {test_class: get_model_tester_from_test_class(lowerCamelCase ) for test_class in test_classes}
return test_tester_mapping
def __lowercase ( lowerCamelCase : Any ):
UpperCamelCase_ : List[str] = get_model_classes(lowerCamelCase )
UpperCamelCase_ : int = {
model_class: get_test_classes_for_model(lowerCamelCase , lowerCamelCase ) for model_class in model_classes
}
return model_test_mapping
def __lowercase ( lowerCamelCase : Tuple ):
UpperCamelCase_ : Tuple = get_model_classes(lowerCamelCase )
UpperCamelCase_ : Optional[Any] = {
model_class: get_tester_classes_for_model(lowerCamelCase , lowerCamelCase ) for model_class in model_classes
}
return model_to_tester_mapping
def __lowercase ( lowerCamelCase : Any ):
if isinstance(lowerCamelCase , lowerCamelCase ):
return o
elif isinstance(lowerCamelCase , lowerCamelCase ):
return o.__name__
elif isinstance(lowerCamelCase , (list, tuple) ):
return [to_json(lowerCamelCase ) for x in o]
elif isinstance(lowerCamelCase , lowerCamelCase ):
return {to_json(lowerCamelCase ): to_json(lowerCamelCase ) for k, v in o.items()}
else:
return o
| 175
| 1
|
"""simple docstring"""
import functools
from typing import Any
def _A ( UpperCamelCase_ : str, UpperCamelCase_ : list[str]) -> bool:
'''simple docstring'''
if not isinstance(UpperCamelCase_, UpperCamelCase_) or len(UpperCamelCase_) == 0:
raise ValueError("the string should be not empty string")
if not isinstance(UpperCamelCase_, UpperCamelCase_) or not all(
isinstance(UpperCamelCase_, UpperCamelCase_) and len(UpperCamelCase_) > 0 for item in words):
raise ValueError("the words should be a list of non-empty strings")
# Build trie
__lowercase = {}
__lowercase = "WORD_KEEPER"
for word in words:
__lowercase = trie
for c in word:
if c not in trie_node:
__lowercase = {}
__lowercase = trie_node[c]
__lowercase = True
__lowercase = len(UpperCamelCase_)
# Dynamic programming method
@functools.cache
def is_breakable(UpperCamelCase_ : int) -> bool:
if index == len_string:
return True
__lowercase = trie
for i in range(UpperCamelCase_, UpperCamelCase_):
__lowercase = trie_node.get(string[i], UpperCamelCase_)
if trie_node is None:
return False
if trie_node.get(UpperCamelCase_, UpperCamelCase_) and is_breakable(i + 1):
return True
return False
return is_breakable(0)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 144
|
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_a = True
except (ImportError, ModuleNotFoundError):
_a = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _A ( UpperCamelCase_ : str) -> str:
'''simple docstring'''
re.sub("<n>", "", UpperCamelCase_) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase_))
| 144
| 1
|
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
A__ : Dict = '''CompVis/stable-diffusion-v1-1'''
A__ : int = '''CompVis/stable-diffusion-v1-2'''
A__ : List[Any] = '''CompVis/stable-diffusion-v1-3'''
A__ : List[Any] = '''CompVis/stable-diffusion-v1-4'''
class __snake_case ( UpperCamelCase_ ):
def __init__( self : List[str] , A_ : AutoencoderKL , A_ : CLIPTextModel , A_ : CLIPTokenizer , A_ : UNetaDConditionModel , A_ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , A_ : StableDiffusionSafetyChecker , A_ : CLIPImageProcessor , A_ : bool = True , ):
super()._init_()
lowerCAmelCase_ : Dict = StableDiffusionPipeline.from_pretrained(A_)
lowerCAmelCase_ : Union[str, Any] = StableDiffusionPipeline.from_pretrained(A_)
lowerCAmelCase_ : List[str] = StableDiffusionPipeline.from_pretrained(A_)
lowerCAmelCase_ : Union[str, Any] = StableDiffusionPipeline(
vae=A_ , text_encoder=A_ , tokenizer=A_ , unet=A_ , scheduler=A_ , safety_checker=A_ , feature_extractor=A_ , requires_safety_checker=A_ , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea)
@property
def UpperCAmelCase__ ( self : int):
return {k: getattr(self , A_) for k in self.config.keys() if not k.startswith('''_''')}
def UpperCAmelCase__ ( self : List[Any] , A_ : Optional[Union[str, int]] = "auto"):
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowerCAmelCase_ : List[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(A_)
def UpperCAmelCase__ ( self : Optional[int]):
self.enable_attention_slicing(A_)
@torch.no_grad()
def UpperCAmelCase__ ( self : List[str] , A_ : Union[str, List[str]] , A_ : int = 5_1_2 , A_ : int = 5_1_2 , A_ : int = 5_0 , A_ : float = 7.5 , A_ : Optional[Union[str, List[str]]] = None , A_ : Optional[int] = 1 , A_ : float = 0.0 , A_ : Optional[torch.Generator] = None , A_ : Optional[torch.FloatTensor] = None , A_ : Optional[str] = "pil" , A_ : bool = True , A_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A_ : int = 1 , **A_ : str , ):
return self.pipea(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
@torch.no_grad()
def UpperCAmelCase__ ( self : Dict , A_ : Union[str, List[str]] , A_ : int = 5_1_2 , A_ : int = 5_1_2 , A_ : int = 5_0 , A_ : float = 7.5 , A_ : Optional[Union[str, List[str]]] = None , A_ : Optional[int] = 1 , A_ : float = 0.0 , A_ : Optional[torch.Generator] = None , A_ : Optional[torch.FloatTensor] = None , A_ : Optional[str] = "pil" , A_ : bool = True , A_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A_ : int = 1 , **A_ : Union[str, Any] , ):
return self.pipea(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
@torch.no_grad()
def UpperCAmelCase__ ( self : List[str] , A_ : Union[str, List[str]] , A_ : int = 5_1_2 , A_ : int = 5_1_2 , A_ : int = 5_0 , A_ : float = 7.5 , A_ : Optional[Union[str, List[str]]] = None , A_ : Optional[int] = 1 , A_ : float = 0.0 , A_ : Optional[torch.Generator] = None , A_ : Optional[torch.FloatTensor] = None , A_ : Optional[str] = "pil" , A_ : bool = True , A_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A_ : int = 1 , **A_ : Tuple , ):
return self.pipea(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
@torch.no_grad()
def UpperCAmelCase__ ( self : Union[str, Any] , A_ : Union[str, List[str]] , A_ : int = 5_1_2 , A_ : int = 5_1_2 , A_ : int = 5_0 , A_ : float = 7.5 , A_ : Optional[Union[str, List[str]]] = None , A_ : Optional[int] = 1 , A_ : float = 0.0 , A_ : Optional[torch.Generator] = None , A_ : Optional[torch.FloatTensor] = None , A_ : Optional[str] = "pil" , A_ : bool = True , A_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A_ : int = 1 , **A_ : int , ):
return self.pipea(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
@torch.no_grad()
def UpperCAmelCase__ ( self : int , A_ : Union[str, List[str]] , A_ : int = 5_1_2 , A_ : int = 5_1_2 , A_ : int = 5_0 , A_ : float = 7.5 , A_ : Optional[Union[str, List[str]]] = None , A_ : Optional[int] = 1 , A_ : float = 0.0 , A_ : Optional[torch.Generator] = None , A_ : Optional[torch.FloatTensor] = None , A_ : Optional[str] = "pil" , A_ : bool = True , A_ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , A_ : int = 1 , **A_ : List[str] , ):
lowerCAmelCase_ : List[Any] = '''cuda''' if torch.cuda.is_available() else '''cpu'''
self.to(A_)
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}.""")
# Get first result from Stable Diffusion Checkpoint v1.1
lowerCAmelCase_ : Optional[Any] = self.textaimg_sda_a(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
# Get first result from Stable Diffusion Checkpoint v1.2
lowerCAmelCase_ : str = self.textaimg_sda_a(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
# Get first result from Stable Diffusion Checkpoint v1.3
lowerCAmelCase_ : List[str] = self.textaimg_sda_a(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
# Get first result from Stable Diffusion Checkpoint v1.4
lowerCAmelCase_ : List[Any] = self.textaimg_sda_a(
prompt=A_ , height=A_ , width=A_ , num_inference_steps=A_ , guidance_scale=A_ , negative_prompt=A_ , num_images_per_prompt=A_ , eta=A_ , generator=A_ , latents=A_ , output_type=A_ , return_dict=A_ , callback=A_ , callback_steps=A_ , **A_ , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]])
| 103
|
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 = logging.get_logger(__name__)
_a = {
"hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """yolos"""
def __init__( self , __lowerCAmelCase=7_6_8 , __lowerCAmelCase=1_2 , __lowerCAmelCase=1_2 , __lowerCAmelCase=3_0_7_2 , __lowerCAmelCase="gelu" , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.02 , __lowerCAmelCase=1E-12 , __lowerCAmelCase=[5_1_2, 8_6_4] , __lowerCAmelCase=1_6 , __lowerCAmelCase=3 , __lowerCAmelCase=True , __lowerCAmelCase=1_0_0 , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase=1 , __lowerCAmelCase=5 , __lowerCAmelCase=2 , __lowerCAmelCase=5 , __lowerCAmelCase=2 , __lowerCAmelCase=0.1 , **__lowerCAmelCase , ):
'''simple docstring'''
super().__init__(**__lowerCAmelCase )
lowerCamelCase__ = hidden_size
lowerCamelCase__ = num_hidden_layers
lowerCamelCase__ = num_attention_heads
lowerCamelCase__ = intermediate_size
lowerCamelCase__ = hidden_act
lowerCamelCase__ = hidden_dropout_prob
lowerCamelCase__ = attention_probs_dropout_prob
lowerCamelCase__ = initializer_range
lowerCamelCase__ = layer_norm_eps
lowerCamelCase__ = image_size
lowerCamelCase__ = patch_size
lowerCamelCase__ = num_channels
lowerCamelCase__ = qkv_bias
lowerCamelCase__ = num_detection_tokens
lowerCamelCase__ = use_mid_position_embeddings
lowerCamelCase__ = auxiliary_loss
# Hungarian matcher
lowerCamelCase__ = class_cost
lowerCamelCase__ = bbox_cost
lowerCamelCase__ = giou_cost
# Loss coefficients
lowerCamelCase__ = bbox_loss_coefficient
lowerCamelCase__ = giou_loss_coefficient
lowerCamelCase__ = eos_coefficient
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = version.parse("""1.11""" )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return 1E-4
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return 1_2
| 209
| 0
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
snake_case : str = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
snake_case : Dict = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
model.to(UpperCamelCase__ )
from datasets import load_dataset
snake_case : Union[str, Any] = load_dataset("nielsr/rvlcdip-demo" )
snake_case : Any = dataset["train"][0]["image"].convert("RGB" )
snake_case : Union[str, Any] = image_processor(UpperCamelCase__ , return_tensors="pt" ).to(UpperCamelCase__ )
# forward pass
with torch.no_grad():
snake_case : int = model(**UpperCamelCase__ )
snake_case : int = outputs.logits
snake_case : int = torch.Size((1, 16) )
self.assertEqual(logits.shape , UpperCamelCase__ )
snake_case : int = torch.tensor(
[-0.4158, -0.4092, -0.4347] , device=UpperCamelCase__ , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , UpperCamelCase__ , atol=1e-4 ) )
| 112
|
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
__snake_case = {
"""/attention/""": """/0/SelfAttention/""",
"""/self_attention/""": """/0/SelfAttention/""",
"""/encoder_decoder_attention/""": """/1/EncDecAttention/""",
"""value""": """v""",
"""query""": """q""",
"""key""": """k""",
"""out""": """o""",
"""pre_self_attention_layer_norm""": """0/layer_norm""",
"""pre_cross_attention_layer_norm""": """1/layer_norm""",
"""pre_attention_layer_norm""": """0/layer_norm""", # previously 1, but seems wrong
"""token_embedder""": """shared""",
"""encoder_norm""": """final_layer_norm""",
"""decoder_norm""": """final_layer_norm""",
"""relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
"""router/router_weights/w/""": """router/classifier/""",
"""roer/roer_weights/w/""": """router/classifier/""",
"""logits_dense""": """lm_head""",
}
def __lowerCAmelCase ( lowercase : Optional[int] ) -> List[str]:
"""simple docstring"""
snake_case : Optional[Any] = list(s_dict.keys() )
for key in keys:
snake_case : Any = R".*/layers_(\d+)"
snake_case : Tuple = key
if re.match(lowercase , lowercase ):
snake_case : List[str] = re.sub(R"layers_(\d+)" , R"block/\1/layer" , lowercase )
snake_case : Union[str, Any] = R"(encoder|decoder)\/"
if re.match(lowercase , lowercase ):
snake_case : Any = re.match(lowercase , lowercase ).groups()
if groups[0] == "encoder":
snake_case : Union[str, Any] = re.sub(R"/mlp/" , R"/1/mlp/" , lowercase )
snake_case : int = re.sub(R"/pre_mlp_layer_norm/" , R"/1/layer_norm/" , lowercase )
elif groups[0] == "decoder":
snake_case : str = re.sub(R"/mlp/" , R"/2/mlp/" , lowercase )
snake_case : List[str] = re.sub(R"/pre_mlp_layer_norm/" , R"/2/layer_norm/" , lowercase )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
snake_case : int = new_key.replace(lowercase , lowercase )
print(F'{key} -> {new_key}' )
snake_case : Optional[Any] = s_dict.pop(lowercase )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
snake_case : int = s_dict[
"encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
snake_case : Optional[Any] = s_dict[
"decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
snake_case : Tuple = s_dict[key].shape[0]
snake_case : int = s_dict[key]
for idx in range(lowercase ):
snake_case : List[str] = expert_weihts[idx]
print(F'{key} -> {key.replace("expert/" , "nested fstring" )}' )
s_dict.pop(lowercase )
return s_dict
__snake_case = {
"""NUM_ENCODER_LAYERS""": """num_layers""",
"""NUM_DECODER_LAYERS""": """num_decoder_layers""",
"""NUM_HEADS""": """num_heads""",
"""HEAD_DIM""": """d_kv""",
"""EMBED_DIM""": """d_model""",
"""MLP_DIM""": """d_ff""",
"""NUM_SELECTED_EXPERTS""": """num_selected_experts""",
"""NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
"""NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
"""dense.MlpBlock.activations""": """feed_forward_proj""",
}
def __lowerCAmelCase ( lowercase : Dict , lowercase : Optional[Any] ) -> int:
"""simple docstring"""
import regex as re
with open(lowercase , "r" ) as f:
snake_case : List[str] = f.read()
snake_case : Tuple = re.findall(R"(.*) = ([0-9.]*)" , lowercase )
snake_case : Any = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
snake_case : Tuple = float(lowercase ) if "." in value else int(lowercase )
snake_case : List[str] = re.findall(R"(.*activations) = \(\'(.*)\',\)" , lowercase )[0]
snake_case : List[Any] = str(activation[1] )
snake_case : Optional[Any] = num_experts
snake_case : List[Any] = SwitchTransformersConfig(**lowercase )
return config
def __lowerCAmelCase ( lowercase : Tuple , lowercase : Tuple , lowercase : Union[str, Any]=None , lowercase : Any="./" , lowercase : int=8 ) -> Dict:
"""simple docstring"""
print(F'Loading flax weights from : {flax_checkpoint_path}' )
snake_case : Union[str, Any] = checkpoints.load_tax_checkpoint(lowercase )
if gin_file is not None:
snake_case : List[str] = convert_gin_to_config(lowercase , lowercase )
else:
snake_case : str = SwitchTransformersConfig.from_pretrained(lowercase )
snake_case : Any = SwitchTransformersForConditionalGeneration(lowercase )
snake_case : Optional[Any] = flax_params["target"]
snake_case : Optional[int] = flatten_dict(lowercase , sep="/" )
snake_case : Optional[Any] = rename_keys(lowercase )
snake_case : List[str] = unflatten_dict(lowercase , sep="/" )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(lowercase , lowercase )
print(F'Save PyTorch model to {pytorch_dump_path}' )
pt_model.save_pretrained(lowercase )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
""" model architecture. If not provided, a `gin_file` has to be provided."""
),
)
parser.add_argument(
"""--gin_file""",
default=None,
type=str,
required=False,
help="""Path to the gin config file. If not provided, a `config_file` has to be passed """,
)
parser.add_argument(
"""--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
)
parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
__snake_case = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 112
| 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
lowerCamelCase : List[Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class A__ ( A__ ):
A__ = ['pixel_values']
def __init__( self : int , _a : bool = True , _a : Dict[str, int] = None , _a : PILImageResampling = PILImageResampling.BICUBIC , _a : bool = True , _a : Dict[str, int] = None , _a : bool = True , _a : Union[int, float] = 1 / 255 , _a : bool = True , _a : Optional[Union[float, List[float]]] = None , _a : Optional[Union[float, List[float]]] = None , _a : bool = True , **_a : Any , ) -> None:
'''simple docstring'''
super().__init__(**_a )
_SCREAMING_SNAKE_CASE =size if size is not None else {'shortest_edge': 224}
_SCREAMING_SNAKE_CASE =get_size_dict(_a , default_to_square=_a )
_SCREAMING_SNAKE_CASE =crop_size if crop_size is not None else {'height': 224, 'width': 224}
_SCREAMING_SNAKE_CASE =get_size_dict(_a , default_to_square=_a , param_name='crop_size' )
_SCREAMING_SNAKE_CASE =do_resize
_SCREAMING_SNAKE_CASE =size
_SCREAMING_SNAKE_CASE =resample
_SCREAMING_SNAKE_CASE =do_center_crop
_SCREAMING_SNAKE_CASE =crop_size
_SCREAMING_SNAKE_CASE =do_rescale
_SCREAMING_SNAKE_CASE =rescale_factor
_SCREAMING_SNAKE_CASE =do_normalize
_SCREAMING_SNAKE_CASE =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_SCREAMING_SNAKE_CASE =image_std if image_std is not None else OPENAI_CLIP_STD
_SCREAMING_SNAKE_CASE =do_convert_rgb
def A ( self : int , _a : np.ndarray , _a : Dict[str, int] , _a : PILImageResampling = PILImageResampling.BICUBIC , _a : Optional[Union[str, ChannelDimension]] = None , **_a : str , ) -> np.ndarray:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =get_size_dict(_a , default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
_SCREAMING_SNAKE_CASE =get_resize_output_image_size(_a , size=size['shortest_edge'] , default_to_square=_a )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def A ( self : Optional[Any] , _a : np.ndarray , _a : Dict[str, int] , _a : Optional[Union[str, ChannelDimension]] = None , **_a : Optional[Any] , ) -> np.ndarray:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
return center_crop(_a , size=(size['height'], size['width']) , data_format=_a , **_a )
def A ( self : List[Any] , _a : np.ndarray , _a : Union[int, float] , _a : Optional[Union[str, ChannelDimension]] = None , **_a : str , ) -> str:
'''simple docstring'''
return rescale(_a , scale=_a , data_format=_a , **_a )
def A ( self : List[Any] , _a : np.ndarray , _a : Union[float, List[float]] , _a : Union[float, List[float]] , _a : Optional[Union[str, ChannelDimension]] = None , **_a : Optional[Any] , ) -> np.ndarray:
'''simple docstring'''
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def A ( self : Tuple , _a : ImageInput , _a : bool = None , _a : Dict[str, int] = None , _a : PILImageResampling = None , _a : bool = None , _a : int = None , _a : bool = None , _a : float = None , _a : bool = None , _a : Optional[Union[float, List[float]]] = None , _a : Optional[Union[float, List[float]]] = None , _a : bool = None , _a : Optional[Union[str, TensorType]] = None , _a : Optional[ChannelDimension] = ChannelDimension.FIRST , **_a : Optional[Any] , ) -> PIL.Image.Image:
'''simple docstring'''
_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(_a , param_name='size' , default_to_square=_a )
_SCREAMING_SNAKE_CASE =resample if resample is not None else self.resample
_SCREAMING_SNAKE_CASE =do_center_crop if do_center_crop is not None else self.do_center_crop
_SCREAMING_SNAKE_CASE =crop_size if crop_size is not None else self.crop_size
_SCREAMING_SNAKE_CASE =get_size_dict(_a , param_name='crop_size' , default_to_square=_a )
_SCREAMING_SNAKE_CASE =do_rescale if do_rescale is not None else self.do_rescale
_SCREAMING_SNAKE_CASE =rescale_factor if rescale_factor is not None else self.rescale_factor
_SCREAMING_SNAKE_CASE =do_normalize if do_normalize is not None else self.do_normalize
_SCREAMING_SNAKE_CASE =image_mean if image_mean is not None else self.image_mean
_SCREAMING_SNAKE_CASE =image_std if image_std is not None else self.image_std
_SCREAMING_SNAKE_CASE =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_SCREAMING_SNAKE_CASE =make_list_of_images(_a )
if not valid_images(_a ):
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.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
_SCREAMING_SNAKE_CASE =[convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
_SCREAMING_SNAKE_CASE =[to_numpy_array(_a ) for image in images]
if do_resize:
_SCREAMING_SNAKE_CASE =[self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_center_crop:
_SCREAMING_SNAKE_CASE =[self.center_crop(image=_a , size=_a ) for image in images]
if do_rescale:
_SCREAMING_SNAKE_CASE =[self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
_SCREAMING_SNAKE_CASE =[self.normalize(image=_a , mean=_a , std=_a ) for image in images]
_SCREAMING_SNAKE_CASE =[to_channel_dimension_format(_a , _a ) for image in images]
_SCREAMING_SNAKE_CASE ={'pixel_values': images}
return BatchFeature(data=_a , tensor_type=_a )
| 47
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCamelCase : Optional[int] = False
class A__ ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def A ( self : Tuple ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion' )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_SCREAMING_SNAKE_CASE =load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
_SCREAMING_SNAKE_CASE =torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE =pipe(
image=_a , generator=_a , guidance_scale=7.5 , num_inference_steps=50 , output_type='numpy' , ).images
_SCREAMING_SNAKE_CASE =image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_SCREAMING_SNAKE_CASE =np.array([0.04_41, 0.04_69, 0.05_07, 0.05_75, 0.06_32, 0.06_50, 0.08_65, 0.09_09, 0.09_45] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 47
| 1
|
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCamelCase (a_ :Optional[Any]) -> Union[str, Any]:
lowercase :str = int(a_)
lowercase , lowercase , lowercase :Tuple = t // 3600, (t // 60) % 60, t % 60
return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""
def lowerCamelCase (a_ :Optional[Any] , a_ :str , a_ :Optional[int] , a_ :str , a_ :Any=300) -> Optional[int]:
# docstyle-ignore
return F"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def lowerCamelCase (a_ :Tuple) -> Optional[Any]:
lowercase :Any = '''<table border="1" class="dataframe">\n'''
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F""" <th>{i}</th>\n"""
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
lowercase :List[Any] = F"""{elt:.6f}""" if isinstance(a_ , a_) else str(a_)
html_code += F""" <td>{elt}</td>\n"""
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __magic_name__ :
__A : Optional[Any] = 5
__A : int = 0.2
def __init__( self : Union[str, Any] , snake_case__ : int , snake_case__ : Optional[str] = None , snake_case__ : bool = True , snake_case__ : Optional["NotebookTrainingTracker"] = None , snake_case__ : int = 3_0_0 , ):
'''simple docstring'''
lowercase :Tuple = total
lowercase :List[Any] = '''''' if prefix is None else prefix
lowercase :Optional[Any] = leave
lowercase :List[str] = parent
lowercase :Tuple = width
lowercase :Optional[Any] = None
lowercase :int = None
lowercase :Optional[Any] = None
def __snake_case ( self : Optional[int] , snake_case__ : int , snake_case__ : bool = False , snake_case__ : str = None ):
'''simple docstring'''
lowercase :Optional[Any] = value
if comment is not None:
lowercase :Optional[int] = comment
if self.last_value is None:
lowercase :Dict = time.time()
lowercase :Dict = value
lowercase :Optional[Any] = None
lowercase :List[Any] = self.warmup
lowercase :str = 1
self.update_bar(snake_case__ )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
lowercase :Optional[int] = time.time()
lowercase :Optional[int] = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
lowercase :Dict = self.elapsed_time / (value - self.start_value)
else:
lowercase :Optional[Any] = None
if value >= self.total:
lowercase :str = self.total
lowercase :Union[str, Any] = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
lowercase :int = self.average_time_per_item * (self.total - value)
self.update_bar(snake_case__ )
lowercase :Tuple = value
lowercase :List[str] = current_time
if self.average_time_per_item is None:
lowercase :Tuple = 1
else:
lowercase :Any = max(int(self.update_every / self.average_time_per_item ) , 1 )
def __snake_case ( self : List[Any] , snake_case__ : int , snake_case__ : int=None ):
'''simple docstring'''
lowercase :str = ''' ''' * (len(str(self.total ) ) - len(str(snake_case__ ) )) + str(snake_case__ )
if self.elapsed_time is None:
lowercase :Tuple = f"""[{spaced_value}/{self.total} : < :"""
elif self.predicted_remaining is None:
lowercase :Optional[Any] = f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"""
else:
lowercase :int = (
f"""[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"""
f""" {format_time(self.predicted_remaining )}"""
)
self.label += f""", {1/self.average_time_per_item:.2f} it/s"""
self.label += "]" if self.comment is None or len(self.comment ) == 0 else f""", {self.comment}]"""
self.display()
def __snake_case ( self : Tuple ):
'''simple docstring'''
lowercase :Union[str, Any] = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
lowercase :List[str] = disp.display(disp.HTML(self.html_code ) , display_id=snake_case__ )
else:
self.output.update(disp.HTML(self.html_code ) )
def __snake_case ( self : Tuple ):
'''simple docstring'''
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('''''' ) )
class __magic_name__ ( __UpperCAmelCase ):
def __init__( self : Any , snake_case__ : str , snake_case__ : Optional[Any]=None ):
'''simple docstring'''
super().__init__(snake_case__ )
lowercase :int = None if column_names is None else [column_names]
lowercase :List[str] = None
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
lowercase :Optional[int] = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
lowercase :List[Any] = disp.display(disp.HTML(self.html_code ) , display_id=snake_case__ )
else:
self.output.update(disp.HTML(self.html_code ) )
def __snake_case ( self : Any , snake_case__ : int ):
'''simple docstring'''
if self.inner_table is None:
lowercase :List[Any] = [list(values.keys() ), list(values.values() )]
else:
lowercase :Optional[Any] = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(snake_case__ )
lowercase :List[Any] = columns
self.inner_table.append([values[c] for c in columns] )
def __snake_case ( self : Tuple , snake_case__ : Union[str, Any] , snake_case__ : Union[str, Any]=None , snake_case__ : Dict=3_0_0 ):
'''simple docstring'''
lowercase :Union[str, Any] = NotebookProgressBar(snake_case__ , prefix=snake_case__ , parent=self , width=snake_case__ )
return self.child_bar
def __snake_case ( self : Tuple ):
'''simple docstring'''
lowercase :Dict = None
self.display()
class __magic_name__ ( __UpperCAmelCase ):
def __init__( self : List[str] ):
'''simple docstring'''
lowercase :List[str] = None
lowercase :Union[str, Any] = None
lowercase :List[str] = False
def __snake_case ( self : Dict , snake_case__ : Any , snake_case__ : Any , snake_case__ : List[str] , **snake_case__ : int ):
'''simple docstring'''
lowercase :Tuple = '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
lowercase :Any = 0
lowercase :int = 0
lowercase :List[str] = [self.first_column] + ['''Training Loss''']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('''Validation Loss''' )
lowercase :str = NotebookTrainingTracker(state.max_steps , snake_case__ )
def __snake_case ( self : Any , snake_case__ : Optional[Any] , snake_case__ : Optional[Any] , snake_case__ : int , **snake_case__ : Dict ):
'''simple docstring'''
lowercase :Dict = int(state.epoch ) if int(state.epoch ) == state.epoch else f"""{state.epoch:.2f}"""
self.training_tracker.update(
state.global_step + 1 , comment=f"""Epoch {epoch}/{state.num_train_epochs}""" , force_update=self._force_next_update , )
lowercase :int = False
def __snake_case ( self : List[Any] , snake_case__ : List[Any] , snake_case__ : List[str] , snake_case__ : Dict , snake_case__ : Optional[Any]=None , **snake_case__ : int ):
'''simple docstring'''
if not has_length(snake_case__ ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
lowercase :Tuple = self.training_tracker.add_child(len(snake_case__ ) )
else:
lowercase :Optional[int] = NotebookProgressBar(len(snake_case__ ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def __snake_case ( self : Tuple , snake_case__ : List[str] , snake_case__ : Optional[Any] , snake_case__ : Dict , **snake_case__ : str ):
'''simple docstring'''
if self.prediction_bar is not None:
self.prediction_bar.close()
lowercase :Any = None
def __snake_case ( self : Dict , snake_case__ : Dict , snake_case__ : Any , snake_case__ : Optional[Any] , snake_case__ : Any=None , **snake_case__ : str ):
'''simple docstring'''
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
lowercase :Optional[int] = {'''Training Loss''': logs['''loss''']}
# First column is necessarily Step sine we're not in epoch eval strategy
lowercase :Dict = state.global_step
self.training_tracker.write_line(snake_case__ )
def __snake_case ( self : List[Any] , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : Optional[Any] , snake_case__ : List[Any]=None , **snake_case__ : Optional[Any] ):
'''simple docstring'''
if self.training_tracker is not None:
lowercase :int = {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
for log in reversed(state.log_history ):
if "loss" in log:
lowercase :Tuple = log['''loss''']
break
if self.first_column == "Epoch":
lowercase :Union[str, Any] = int(state.epoch )
else:
lowercase :Any = state.global_step
lowercase :List[str] = '''eval'''
for k in metrics:
if k.endswith('''_loss''' ):
lowercase :Tuple = re.sub(r'''\_loss$''' , '''''' , snake_case__ )
lowercase :Optional[Any] = metrics.pop('''total_flos''' , snake_case__ )
lowercase :Union[str, Any] = metrics.pop('''epoch''' , snake_case__ )
lowercase :Any = metrics.pop(f"""{metric_key_prefix}_runtime""" , snake_case__ )
lowercase :Any = metrics.pop(f"""{metric_key_prefix}_samples_per_second""" , snake_case__ )
lowercase :List[Any] = metrics.pop(f"""{metric_key_prefix}_steps_per_second""" , snake_case__ )
lowercase :List[str] = metrics.pop(f"""{metric_key_prefix}_jit_compilation_time""" , snake_case__ )
for k, v in metrics.items():
if k == f"""{metric_key_prefix}_loss""":
lowercase :Optional[Any] = v
else:
lowercase :Optional[Any] = k.split('''_''' )
lowercase :List[str] = ''' '''.join([part.capitalize() for part in splits[1:]] )
lowercase :Union[str, Any] = v
self.training_tracker.write_line(snake_case__ )
self.training_tracker.remove_child()
lowercase :Any = None
# Evaluation takes a long time so we should force the next update.
lowercase :Any = True
def __snake_case ( self : Optional[int] , snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : Optional[Any] , **snake_case__ : Any ):
'''simple docstring'''
self.training_tracker.update(
state.global_step , comment=f"""Epoch {int(state.epoch )}/{state.num_train_epochs}""" , force_update=snake_case__ )
lowercase :Optional[int] = None
| 172
|
"""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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase = logging.get_logger(__name__)
def lowerCamelCase (a_ :str) -> YolosConfig:
lowercase :Union[str, Any] = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
lowercase :List[str] = 192
lowercase :List[str] = 768
lowercase :int = 12
lowercase :str = 3
lowercase :List[Any] = [800, 1333]
lowercase :Any = False
elif yolos_name == "yolos_s_dWr":
lowercase :List[str] = 330
lowercase :List[Any] = 14
lowercase :int = 6
lowercase :List[Any] = 1320
elif "yolos_s" in yolos_name:
lowercase :int = 384
lowercase :Union[str, Any] = 1536
lowercase :int = 12
lowercase :str = 6
elif "yolos_b" in yolos_name:
lowercase :Dict = [800, 1344]
lowercase :List[str] = 91
lowercase :List[Any] = '''huggingface/label-files'''
lowercase :Union[str, Any] = '''coco-detection-id2label.json'''
lowercase :int = json.load(open(hf_hub_download(a_ , a_ , repo_type='''dataset''') , '''r'''))
lowercase :List[Any] = {int(a_): v for k, v in idalabel.items()}
lowercase :Dict = idalabel
lowercase :Tuple = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase (a_ :dict , a_ :YolosConfig , a_ :bool = False) -> Optional[int]:
for i in range(config.num_hidden_layers):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase :Dict = state_dict.pop(F"""blocks.{i}.attn.qkv.weight""")
lowercase :List[Any] = state_dict.pop(F"""blocks.{i}.attn.qkv.bias""")
# next, add query, keys and values (in that order) to the state dict
lowercase :int = in_proj_weight[: config.hidden_size, :]
lowercase :List[str] = in_proj_bias[: config.hidden_size]
lowercase :Optional[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase :int = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase :Any = in_proj_weight[-config.hidden_size :, :]
lowercase :Union[str, Any] = in_proj_bias[-config.hidden_size :]
def lowerCamelCase (a_ :str) -> str:
if "backbone" in name:
lowercase :Optional[int] = name.replace('''backbone''' , '''vit''')
if "cls_token" in name:
lowercase :List[Any] = name.replace('''cls_token''' , '''embeddings.cls_token''')
if "det_token" in name:
lowercase :int = name.replace('''det_token''' , '''embeddings.detection_tokens''')
if "mid_pos_embed" in name:
lowercase :List[Any] = name.replace('''mid_pos_embed''' , '''encoder.mid_position_embeddings''')
if "pos_embed" in name:
lowercase :List[str] = name.replace('''pos_embed''' , '''embeddings.position_embeddings''')
if "patch_embed.proj" in name:
lowercase :Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''')
if "blocks" in name:
lowercase :Any = name.replace('''blocks''' , '''encoder.layer''')
if "attn.proj" in name:
lowercase :Dict = name.replace('''attn.proj''' , '''attention.output.dense''')
if "attn" in name:
lowercase :Tuple = name.replace('''attn''' , '''attention.self''')
if "norm1" in name:
lowercase :List[Any] = name.replace('''norm1''' , '''layernorm_before''')
if "norm2" in name:
lowercase :List[Any] = name.replace('''norm2''' , '''layernorm_after''')
if "mlp.fc1" in name:
lowercase :Union[str, Any] = name.replace('''mlp.fc1''' , '''intermediate.dense''')
if "mlp.fc2" in name:
lowercase :Dict = name.replace('''mlp.fc2''' , '''output.dense''')
if "class_embed" in name:
lowercase :Dict = name.replace('''class_embed''' , '''class_labels_classifier''')
if "bbox_embed" in name:
lowercase :Dict = name.replace('''bbox_embed''' , '''bbox_predictor''')
if "vit.norm" in name:
lowercase :Dict = name.replace('''vit.norm''' , '''vit.layernorm''')
return name
def lowerCamelCase (a_ :dict , a_ :YolosForObjectDetection) -> dict:
for key in orig_state_dict.copy().keys():
lowercase :List[Any] = orig_state_dict.pop(a_)
if "qkv" in key:
lowercase :str = key.split('''.''')
lowercase :List[str] = int(key_split[2])
lowercase :List[str] = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
lowercase :List[Any] = val[:dim, :]
lowercase :Optional[int] = val[
dim : dim * 2, :
]
lowercase :Any = val[-dim:, :]
else:
lowercase :List[str] = val[:dim]
lowercase :Union[str, Any] = val[dim : dim * 2]
lowercase :List[Any] = val[-dim:]
else:
lowercase :List[str] = val
return orig_state_dict
def lowerCamelCase () -> torch.Tensor:
lowercase :Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase :Dict = Image.open(requests.get(a_ , stream=a_).raw)
return im
@torch.no_grad()
def lowerCamelCase (a_ :str , a_ :str , a_ :str , a_ :bool = False) -> List[Any]:
lowercase :Union[str, Any] = get_yolos_config(a_)
# load original state_dict
lowercase :List[str] = torch.load(a_ , map_location='''cpu''')['''model''']
# load 🤗 model
lowercase :Tuple = YolosForObjectDetection(a_)
model.eval()
lowercase :Dict = convert_state_dict(a_ , a_)
model.load_state_dict(a_)
# Check outputs on an image, prepared by YolosImageProcessor
lowercase :Tuple = 800 if yolos_name != '''yolos_ti''' else 512
lowercase :Dict = YolosImageProcessor(format='''coco_detection''' , size=a_)
lowercase :Optional[int] = image_processor(images=prepare_img() , return_tensors='''pt''')
lowercase :List[Any] = model(**a_)
lowercase , lowercase :Dict = outputs.logits, outputs.pred_boxes
lowercase , lowercase :int = None, None
if yolos_name == "yolos_ti":
lowercase :Dict = torch.tensor(
[[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]])
lowercase :Dict = torch.tensor(
[[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]])
elif yolos_name == "yolos_s_200_pre":
lowercase :Union[str, Any] = torch.tensor(
[[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]])
lowercase :List[str] = torch.tensor(
[[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]])
elif yolos_name == "yolos_s_300_pre":
lowercase :int = torch.tensor(
[[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]])
lowercase :Optional[Any] = torch.tensor(
[[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]])
elif yolos_name == "yolos_s_dWr":
lowercase :int = torch.tensor(
[[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]])
lowercase :Dict = torch.tensor(
[[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]])
elif yolos_name == "yolos_base":
lowercase :Dict = torch.tensor(
[[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]])
lowercase :Tuple = torch.tensor(
[[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]])
else:
raise ValueError(F"""Unknown yolos_name: {yolos_name}""")
assert torch.allclose(logits[0, :3, :3] , a_ , atol=1E-4)
assert torch.allclose(pred_boxes[0, :3, :3] , a_ , atol=1E-4)
Path(a_).mkdir(exist_ok=a_)
print(F"""Saving model {yolos_name} to {pytorch_dump_folder_path}""")
model.save_pretrained(a_)
print(F"""Saving image processor to {pytorch_dump_folder_path}""")
image_processor.save_pretrained(a_)
if push_to_hub:
lowercase :Optional[int] = {
'''yolos_ti''': '''yolos-tiny''',
'''yolos_s_200_pre''': '''yolos-small''',
'''yolos_s_300_pre''': '''yolos-small-300''',
'''yolos_s_dWr''': '''yolos-small-dwr''',
'''yolos_base''': '''yolos-base''',
}
print('''Pushing to the hub...''')
lowercase :Optional[Any] = model_mapping[yolos_name]
image_processor.push_to_hub(a_ , organization='''hustvl''')
model.push_to_hub(a_ , organization='''hustvl''')
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--yolos_name''',
default='''yolos_s_200_pre''',
type=str,
help=(
'''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
UpperCAmelCase = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 172
| 1
|
'''simple docstring'''
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class lowerCamelCase_ :
def __init__( self : Dict , _A : List[str] , _A : Dict=100 , _A : Tuple=13 , _A : str=30 , _A : Optional[Any]=2 , _A : List[str]=3 , _A : str=True , _A : Tuple=True , _A : Any=32 , _A : Dict=4 , _A : Union[str, Any]=4 , _A : Dict=37 , _A : int="gelu" , _A : List[Any]=0.1 , _A : List[Any]=0.1 , _A : Union[str, Any]=10 , _A : int=0.0_2 , _A : Dict=3 , _A : Optional[Any]=None , _A : Dict=[0, 1, 2, 3] , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = parent
UpperCAmelCase__ : List[str] = 100
UpperCAmelCase__ : Union[str, Any] = batch_size
UpperCAmelCase__ : int = image_size
UpperCAmelCase__ : str = patch_size
UpperCAmelCase__ : Optional[int] = num_channels
UpperCAmelCase__ : List[str] = is_training
UpperCAmelCase__ : str = use_labels
UpperCAmelCase__ : List[str] = hidden_size
UpperCAmelCase__ : List[str] = num_hidden_layers
UpperCAmelCase__ : List[str] = num_attention_heads
UpperCAmelCase__ : List[str] = intermediate_size
UpperCAmelCase__ : Optional[Any] = hidden_act
UpperCAmelCase__ : Optional[Any] = hidden_dropout_prob
UpperCAmelCase__ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase__ : List[str] = type_sequence_label_size
UpperCAmelCase__ : List[str] = initializer_range
UpperCAmelCase__ : List[Any] = scope
UpperCAmelCase__ : int = out_indices
UpperCAmelCase__ : Any = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase__ : Tuple = (image_size // patch_size) ** 2
UpperCAmelCase__ : Optional[int] = num_patches + 1
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : Optional[int] = None
if self.use_labels:
UpperCAmelCase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ : Optional[Any] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
UpperCAmelCase__ : int = self.get_config()
return config, pixel_values, labels, pixel_labels
def lowercase_ ( self : str ):
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , 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=_A , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def lowercase_ ( self : Tuple , _A : Any , _A : Dict , _A : Tuple , _A : Any ):
'''simple docstring'''
UpperCAmelCase__ : str = BeitModel(config=_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Dict = model(_A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ ( self : str , _A : Optional[int] , _A : str , _A : Optional[Any] , _A : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : str = BeitForMaskedImageModeling(config=_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def lowercase_ ( self : List[str] , _A : Tuple , _A : Any , _A : Tuple , _A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = self.type_sequence_label_size
UpperCAmelCase__ : Any = BeitForImageClassification(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Union[str, Any] = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
UpperCAmelCase__ : str = 1
UpperCAmelCase__ : Dict = BeitForImageClassification(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Any = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
UpperCAmelCase__ : Union[str, Any] = model(_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowercase_ ( self : Any , _A : Union[str, Any] , _A : Optional[int] , _A : Dict , _A : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.num_labels
UpperCAmelCase__ : Any = BeitForSemanticSegmentation(_A )
model.to(_A )
model.eval()
UpperCAmelCase__ : Optional[Any] = model(_A )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
UpperCAmelCase__ : Dict = model(_A , labels=_A )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = config_and_inputs
UpperCAmelCase__ : int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( __a , __a , unittest.TestCase ):
lowerCAmelCase__ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
{
'feature-extraction': BeitModel,
'image-classification': BeitForImageClassification,
'image-segmentation': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowercase_ ( self : List[str] ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = BeitModelTester(self )
UpperCAmelCase__ : Union[str, Any] = ConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=37 )
def lowercase_ ( self : str ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''BEiT does not use inputs_embeds''' )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
pass
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : List[Any] = model_class(_A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase__ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_A , nn.Linear ) )
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Any = model_class(_A )
UpperCAmelCase__ : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : Any = [*signature.parameters.keys()]
UpperCAmelCase__ : List[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _A )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_A )
def lowercase_ ( self : Dict ):
'''simple docstring'''
if not self.model_tester.is_training:
return
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : List[Any] = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(_A ), BeitForMaskedImageModeling]:
continue
UpperCAmelCase__ : List[Any] = model_class(_A )
model.to(_A )
model.train()
UpperCAmelCase__ : List[str] = self._prepare_for_class(_A , _A , return_labels=_A )
UpperCAmelCase__ : int = model(**_A ).loss
loss.backward()
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
UpperCAmelCase__ : List[str] = False
UpperCAmelCase__ : List[Any] = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(_A ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
UpperCAmelCase__ : Tuple = model_class(_A )
model.gradient_checkpointing_enable()
model.to(_A )
model.train()
UpperCAmelCase__ : str = self._prepare_for_class(_A , _A , return_labels=_A )
UpperCAmelCase__ : str = model(**_A ).loss
loss.backward()
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ : Any = _config_zero_init(_A )
for model_class in self.all_model_classes:
UpperCAmelCase__ : Optional[int] = model_class(config=_A )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if 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""" , )
@slow
def lowercase_ ( self : List[str] ):
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Dict = BeitModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def a__ ( ) -> int:
UpperCAmelCase__ : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''' ) if is_vision_available() else None
@slow
def lowercase_ ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' ).to(_A )
UpperCAmelCase__ : Tuple = self.default_image_processor
UpperCAmelCase__ : List[Any] = prepare_img()
UpperCAmelCase__ : Union[str, Any] = image_processor(images=_A , return_tensors='''pt''' ).pixel_values.to(_A )
# prepare bool_masked_pos
UpperCAmelCase__ : Optional[int] = torch.ones((1, 196) , dtype=torch.bool ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(pixel_values=_A , bool_masked_pos=_A )
UpperCAmelCase__ : Optional[Any] = outputs.logits
# verify the logits
UpperCAmelCase__ : List[Any] = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , _A )
UpperCAmelCase__ : int = torch.tensor(
[[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]] ).to(_A )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , _A , atol=1e-2 ) )
@slow
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' ).to(_A )
UpperCAmelCase__ : str = self.default_image_processor
UpperCAmelCase__ : Tuple = prepare_img()
UpperCAmelCase__ : List[str] = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : List[str] = model(**_A )
UpperCAmelCase__ : List[str] = outputs.logits
# verify the logits
UpperCAmelCase__ : Tuple = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , _A )
UpperCAmelCase__ : Any = torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8] ).to(_A )
self.assertTrue(torch.allclose(logits[0, :3] , _A , atol=1e-4 ) )
UpperCAmelCase__ : Any = 281
self.assertEqual(logits.argmax(-1 ).item() , _A )
@slow
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' ).to(
_A )
UpperCAmelCase__ : Optional[Any] = self.default_image_processor
UpperCAmelCase__ : Tuple = prepare_img()
UpperCAmelCase__ : Optional[Any] = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Union[str, Any] = model(**_A )
UpperCAmelCase__ : Any = outputs.logits
# verify the logits
UpperCAmelCase__ : int = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , _A )
UpperCAmelCase__ : Tuple = torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1] ).to(_A )
self.assertTrue(torch.allclose(logits[0, :3] , _A , atol=1e-4 ) )
UpperCAmelCase__ : Any = 2_396
self.assertEqual(logits.argmax(-1 ).item() , _A )
@slow
def lowercase_ ( self : int ):
'''simple docstring'''
UpperCAmelCase__ : str = BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
UpperCAmelCase__ : int = model.to(_A )
UpperCAmelCase__ : List[str] = BeitImageProcessor(do_resize=_A , size=640 , do_center_crop=_A )
UpperCAmelCase__ : Union[str, Any] = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
UpperCAmelCase__ : Optional[int] = Image.open(ds[0]['''file'''] )
UpperCAmelCase__ : Dict = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Optional[Any] = model(**_A )
UpperCAmelCase__ : Union[str, Any] = outputs.logits
# verify the logits
UpperCAmelCase__ : Union[str, Any] = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , _A )
UpperCAmelCase__ : Tuple = version.parse(PIL.__version__ ) < version.parse('''9.0.0''' )
if is_pillow_less_than_a:
UpperCAmelCase__ : List[Any] = torch.tensor(
[
[[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
[[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
[[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
] , device=_A , )
else:
UpperCAmelCase__ : List[Any] = torch.tensor(
[
[[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
[[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
[[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
] , device=_A , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _A , atol=1e-4 ) )
@slow
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
UpperCAmelCase__ : Optional[Any] = model.to(_A )
UpperCAmelCase__ : Dict = BeitImageProcessor(do_resize=_A , size=640 , do_center_crop=_A )
UpperCAmelCase__ : List[Any] = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
UpperCAmelCase__ : str = Image.open(ds[0]['''file'''] )
UpperCAmelCase__ : str = image_processor(images=_A , return_tensors='''pt''' ).to(_A )
# forward pass
with torch.no_grad():
UpperCAmelCase__ : Any = model(**_A )
UpperCAmelCase__ : Any = outputs.logits.detach().cpu()
UpperCAmelCase__ : int = image_processor.post_process_semantic_segmentation(outputs=_A , target_sizes=[(500, 300)] )
UpperCAmelCase__ : Union[str, Any] = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , _A )
UpperCAmelCase__ : List[str] = image_processor.post_process_semantic_segmentation(outputs=_A )
UpperCAmelCase__ : Any = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , _A )
| 181
|
'''simple docstring'''
import pprint
import requests
UpperCamelCase__ = '''https://zenquotes.io/api'''
def a__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def a__ ( ) -> list:
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
UpperCamelCase__ = random_quotes()
pprint.pprint(response)
| 181
| 1
|
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
if len(_lowercase ) != len(_lowercase ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zero." )
if any(p < 0 for p in profit ):
raise ValueError("Profit can not be negative." )
if any(w < 0 for w in weight ):
raise ValueError("Weight can not be negative." )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
UpperCamelCase_ = [p / w for p, w in zip(_lowercase , _lowercase )]
# Creating a copy of the list and sorting profit/weight in ascending order
UpperCamelCase_ = sorted(_lowercase )
# declaring useful variables
UpperCamelCase_ = len(_lowercase )
UpperCamelCase_ = 0
UpperCamelCase_ = 0
UpperCamelCase_ = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
UpperCamelCase_ = sorted_profit_by_weight[length - i - 1]
UpperCamelCase_ = profit_by_weight.index(_lowercase )
UpperCamelCase_ = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
'Input profits, weights, and then max_weight (all positive ints) separated by '
'spaces.'
)
_UpperCAmelCase = [int(x) for x in input('Input profits separated by spaces: ').split()]
_UpperCAmelCase = [int(x) for x in input('Input weights separated by spaces: ').split()]
_UpperCAmelCase = int(input('Max weight allowed: '))
# Function Call
calc_profit(profit, weight, max_weight)
| 359
|
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCase_ = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
UpperCamelCase_ = "mps"
if device == "mps":
print(
"WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"
" errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"
" with generations." )
return device
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
UpperCamelCase_ = plt.imshow(UpperCamelCase_ )
fig.axes.get_xaxis().set_visible(UpperCamelCase_ )
fig.axes.get_yaxis().set_visible(UpperCamelCase_ )
plt.show()
def lowerCAmelCase_ ( ) -> List[str]:
UpperCamelCase_ = datetime.now()
UpperCamelCase_ = current_time.strftime("%H:%M:%S" )
return timestamp
| 328
| 0
|
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase ( A__ ):
"""simple docstring"""
_a = ["image_processor", "tokenizer"]
_a = "OwlViTImageProcessor"
_a = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , **UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , UpperCAmelCase__ , )
UpperCamelCase__ :List[Any] = kwargs.pop('''feature_extractor''' )
UpperCamelCase__ :List[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__(UpperCAmelCase__ , UpperCAmelCase__ )
def __call__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_="max_length" , UpperCamelCase_="np" , **UpperCamelCase_ ):
'''simple docstring'''
if text is None and query_images is None and images is None:
raise ValueError(
'''You have to specify at least one text or query image or image. All three cannot be none.''' )
if text is not None:
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or (isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and not isinstance(text[0] , UpperCAmelCase__ )):
UpperCamelCase__ :Optional[Any] = [self.tokenizer(UpperCAmelCase__ , padding=UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )]
elif isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and isinstance(text[0] , UpperCAmelCase__ ):
UpperCamelCase__ :Any = []
# Maximum number of queries across batch
UpperCamelCase__ :List[str] = max([len(UpperCAmelCase__ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(UpperCAmelCase__ ) != max_num_queries:
UpperCamelCase__ :Dict = t + [''' '''] * (max_num_queries - len(UpperCAmelCase__ ))
UpperCamelCase__ :Optional[Any] = self.tokenizer(UpperCAmelCase__ , padding=UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
encodings.append(UpperCAmelCase__ )
else:
raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )
if return_tensors == "np":
UpperCamelCase__ :Union[str, Any] = np.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
UpperCamelCase__ :List[Any] = np.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
UpperCamelCase__ :Any = jnp.concatenate([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
UpperCamelCase__ :int = jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
UpperCamelCase__ :Dict = torch.cat([encoding['''input_ids'''] for encoding in encodings] , dim=0 )
UpperCamelCase__ :Union[str, Any] = torch.cat([encoding['''attention_mask'''] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
UpperCamelCase__ :Dict = tf.stack([encoding['''input_ids'''] for encoding in encodings] , axis=0 )
UpperCamelCase__ :List[str] = tf.stack([encoding['''attention_mask'''] for encoding in encodings] , axis=0 )
else:
raise ValueError('''Target return tensor type could not be returned''' )
UpperCamelCase__ :Optional[int] = BatchEncoding()
UpperCamelCase__ :Optional[Any] = input_ids
UpperCamelCase__ :Dict = attention_mask
if query_images is not None:
UpperCamelCase__ :int = BatchEncoding()
UpperCamelCase__ :Optional[Any] = self.image_processor(
UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ ).pixel_values
UpperCamelCase__ :Tuple = query_pixel_values
if images is not None:
UpperCamelCase__ :List[Any] = self.image_processor(UpperCAmelCase__ , return_tensors=UpperCAmelCase__ , **UpperCAmelCase__ )
if text is not None and images is not None:
UpperCamelCase__ :List[str] = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
UpperCamelCase__ :Any = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCAmelCase__ ) , tensor_type=UpperCAmelCase__ )
def lowerCAmelCase__ ( self , *UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
return self.image_processor.post_process(*UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self , *UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
return self.image_processor.post_process_object_detection(*UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self , *UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
return self.image_processor.post_process_image_guided_detection(*UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self , *UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCAmelCase__ ( self , *UpperCamelCase_ , **UpperCamelCase_ ):
'''simple docstring'''
return self.tokenizer.decode(*UpperCAmelCase__ , **UpperCAmelCase__ )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , UpperCAmelCase__ , )
return self.image_processor_class
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , UpperCAmelCase__ , )
return self.image_processor
| 97
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : str = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
'''tokenization_roformer''': ['''RoFormerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = ['''RoFormerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : int = [
'''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoFormerForCausalLM''',
'''RoFormerForMaskedLM''',
'''RoFormerForMultipleChoice''',
'''RoFormerForQuestionAnswering''',
'''RoFormerForSequenceClassification''',
'''RoFormerForTokenClassification''',
'''RoFormerLayer''',
'''RoFormerModel''',
'''RoFormerPreTrainedModel''',
'''load_tf_weights_in_roformer''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[Any] = [
'''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRoFormerForCausalLM''',
'''TFRoFormerForMaskedLM''',
'''TFRoFormerForMultipleChoice''',
'''TFRoFormerForQuestionAnswering''',
'''TFRoFormerForSequenceClassification''',
'''TFRoFormerForTokenClassification''',
'''TFRoFormerLayer''',
'''TFRoFormerModel''',
'''TFRoFormerPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Tuple = [
'''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxRoFormerForMaskedLM''',
'''FlaxRoFormerForMultipleChoice''',
'''FlaxRoFormerForQuestionAnswering''',
'''FlaxRoFormerForSequenceClassification''',
'''FlaxRoFormerForTokenClassification''',
'''FlaxRoFormerModel''',
'''FlaxRoFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
from .tokenization_roformer import RoFormerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roformer_fast import RoFormerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerLayer,
RoFormerModel,
RoFormerPreTrainedModel,
load_tf_weights_in_roformer,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roformer import (
TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerLayer,
TFRoFormerModel,
TFRoFormerPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roformer import (
FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
FlaxRoFormerPreTrainedModel,
)
else:
import sys
a__ : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 54
| 0
|
"""simple docstring"""
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy
__A : str = logging.get_logger(__name__)
__A : int = {
'artists_file': 'artists.json',
'lyrics_file': 'lyrics.json',
'genres_file': 'genres.json',
}
__A : Tuple = {
'artists_file': {
'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
},
'genres_file': {
'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
},
'lyrics_file': {
'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
},
}
__A : int = {
'jukebox': 5_12,
}
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
_UpperCamelCase:Any = VOCAB_FILES_NAMES
_UpperCamelCase:int = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase:Optional[int] = PRETRAINED_LYRIC_TOKENS_SIZES
_UpperCamelCase:Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=["v3", "v2", "v2"] , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE="<|endoftext|>" , **_SCREAMING_SNAKE_CASE , )-> Optional[int]:
lowerCamelCase_ =AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else unk_token
super().__init__(
unk_token=_SCREAMING_SNAKE_CASE , n_genres=_SCREAMING_SNAKE_CASE , version=_SCREAMING_SNAKE_CASE , max_n_lyric_tokens=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
lowerCamelCase_ =version
lowerCamelCase_ =max_n_lyric_tokens
lowerCamelCase_ =n_genres
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as vocab_handle:
lowerCamelCase_ =json.load(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as vocab_handle:
lowerCamelCase_ =json.load(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as vocab_handle:
lowerCamelCase_ =json.load(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =R"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder ) == 79:
lowerCamelCase_ =oov.replace(R"""\-'""" , R"""\-+'""" )
lowerCamelCase_ =regex.compile(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ ={v: k for k, v in self.artists_encoder.items()}
lowerCamelCase_ ={v: k for k, v in self.genres_encoder.items()}
lowerCamelCase_ ={v: k for k, v in self.lyrics_encoder.items()}
@property
def _snake_case ( self )-> Union[str, Any]:
return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )
def _snake_case ( self )-> int:
return dict(self.artists_encoder , self.genres_encoder , self.lyrics_encoder )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )-> Tuple:
lowerCamelCase_ =[self.artists_encoder.get(_SCREAMING_SNAKE_CASE , 0 ) for artist in list_artists]
for genres in range(len(_SCREAMING_SNAKE_CASE ) ):
lowerCamelCase_ =[self.genres_encoder.get(_SCREAMING_SNAKE_CASE , 0 ) for genre in list_genres[genres]]
lowerCamelCase_ =list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))
lowerCamelCase_ =[[self.lyrics_encoder.get(_SCREAMING_SNAKE_CASE , 0 ) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> List[Any]:
return list(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )-> Tuple:
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ =self.prepare_for_tokenization(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ =self._tokenize(_SCREAMING_SNAKE_CASE )
return artist, genre, lyrics
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False )-> Tuple[str, str, str, Dict[str, Any]]:
for idx in range(len(self.version ) ):
if self.version[idx] == "v3":
lowerCamelCase_ =artists[idx].lower()
lowerCamelCase_ =[genres[idx].lower()]
else:
lowerCamelCase_ =self._normalize(artists[idx] ) + """.v2"""
lowerCamelCase_ =[
self._normalize(_SCREAMING_SNAKE_CASE ) + """.v2""" for genre in genres[idx].split("""_""" )
] # split is for the full dictionary with combined genres
if self.version[0] == "v2":
lowerCamelCase_ =regex.compile(R"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+""" )
lowerCamelCase_ ="""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
lowerCamelCase_ ={vocab[index]: index + 1 for index in range(len(_SCREAMING_SNAKE_CASE ) )}
lowerCamelCase_ =0
lowerCamelCase_ =len(_SCREAMING_SNAKE_CASE ) + 1
lowerCamelCase_ =self.vocab
lowerCamelCase_ ={v: k for k, v in self.vocab.items()}
lowerCamelCase_ =""""""
else:
lowerCamelCase_ =regex.compile(R"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+""" )
lowerCamelCase_ =self._run_strip_accents(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =lyrics.replace("""\\""" , """\n""" )
lowerCamelCase_ =self.out_of_vocab.sub("""""" , _SCREAMING_SNAKE_CASE ), [], []
return artists, genres, lyrics
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> Tuple:
lowerCamelCase_ =unicodedata.normalize("""NFD""" , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ =[]
for char in text:
lowerCamelCase_ =unicodedata.category(_SCREAMING_SNAKE_CASE )
if cat == "Mn":
continue
output.append(_SCREAMING_SNAKE_CASE )
return "".join(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> str:
lowerCamelCase_ =(
[chr(_SCREAMING_SNAKE_CASE ) for i in range(ord("""a""" ) , ord("""z""" ) + 1 )]
+ [chr(_SCREAMING_SNAKE_CASE ) for i in range(ord("""A""" ) , ord("""Z""" ) + 1 )]
+ [chr(_SCREAMING_SNAKE_CASE ) for i in range(ord("""0""" ) , ord("""9""" ) + 1 )]
+ ["""."""]
)
lowerCamelCase_ =frozenset(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =re.compile(R"""_+""" )
lowerCamelCase_ ="""""".join([c if c in accepted else """_""" for c in text.lower()] )
lowerCamelCase_ =pattern.sub("""_""" , _SCREAMING_SNAKE_CASE ).strip("""_""" )
return text
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> str:
return " ".join(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False )-> Union[str, Any]:
# Convert to TensorType
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
lowerCamelCase_ =TensorType(_SCREAMING_SNAKE_CASE )
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
"""Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.""" )
import tensorflow as tf
lowerCamelCase_ =tf.constant
lowerCamelCase_ =tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError("""Unable to convert output to PyTorch tensors format, PyTorch is not installed.""" )
import torch
lowerCamelCase_ =torch.tensor
lowerCamelCase_ =torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError("""Unable to convert output to JAX tensors format, JAX is not installed.""" )
import jax.numpy as jnp # noqa: F811
lowerCamelCase_ =jnp.array
lowerCamelCase_ =_is_jax
else:
lowerCamelCase_ =np.asarray
lowerCamelCase_ =_is_numpy
# Do the tensor conversion in batch
try:
if prepend_batch_axis:
lowerCamelCase_ =[inputs]
if not is_tensor(_SCREAMING_SNAKE_CASE ):
lowerCamelCase_ =as_tensor(_SCREAMING_SNAKE_CASE )
except: # noqa E722
raise ValueError(
"""Unable to create tensor, you should probably activate truncation and/or padding """
"""with 'padding=True' 'truncation=True' to have batched tensors with the same length.""" )
return inputs
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="" , _SCREAMING_SNAKE_CASE="pt" )-> BatchEncoding:
lowerCamelCase_ =[0, 0, 0]
lowerCamelCase_ =[artist] * len(self.version )
lowerCamelCase_ =[genres] * len(self.version )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ =self.tokenize(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ =self._convert_token_to_id(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ =[-INFINITY] * len(full_tokens[-1] )
lowerCamelCase_ =[
self.convert_to_tensors(
[input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] , tensor_type=_SCREAMING_SNAKE_CASE )
for i in range(len(self.version ) )
]
return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks} )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None )-> Tuple[str]:
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCamelCase_ =os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""artists_file"""] )
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.artists_encoder , ensure_ascii=_SCREAMING_SNAKE_CASE ) )
lowerCamelCase_ =os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""genres_file"""] )
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.genres_encoder , ensure_ascii=_SCREAMING_SNAKE_CASE ) )
lowerCamelCase_ =os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""lyrics_file"""] )
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.lyrics_encoder , ensure_ascii=_SCREAMING_SNAKE_CASE ) )
return (artists_file, genres_file, lyrics_file)
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )-> List[str]:
lowerCamelCase_ =self.artists_decoder.get(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =[self.genres_decoder.get(_SCREAMING_SNAKE_CASE ) for genre in genres_index]
lowerCamelCase_ =[self.lyrics_decoder.get(_SCREAMING_SNAKE_CASE ) for character in lyric_index]
return artist, genres, lyrics
| 351
|
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
__A : int = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n'
__A : Any = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'
__A : Union[str, Any] = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _SCREAMING_SNAKE_CASE ( datasets.Metric):
def _snake_case ( self )-> Any:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/krishnap25/mauve""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/krishnap25/mauve"""] , reference_urls=[
"""https://arxiv.org/abs/2102.01454""",
"""https://github.com/krishnap25/mauve""",
] , )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="auto" , _SCREAMING_SNAKE_CASE=-1 , _SCREAMING_SNAKE_CASE=0.9 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=500 , _SCREAMING_SNAKE_CASE="gpt2-large" , _SCREAMING_SNAKE_CASE=-1 , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=25 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=25 , )-> List[str]:
lowerCamelCase_ =compute_mauve(
p_text=_SCREAMING_SNAKE_CASE , q_text=_SCREAMING_SNAKE_CASE , p_features=_SCREAMING_SNAKE_CASE , q_features=_SCREAMING_SNAKE_CASE , p_tokens=_SCREAMING_SNAKE_CASE , q_tokens=_SCREAMING_SNAKE_CASE , num_buckets=_SCREAMING_SNAKE_CASE , pca_max_data=_SCREAMING_SNAKE_CASE , kmeans_explained_var=_SCREAMING_SNAKE_CASE , kmeans_num_redo=_SCREAMING_SNAKE_CASE , kmeans_max_iter=_SCREAMING_SNAKE_CASE , featurize_model_name=_SCREAMING_SNAKE_CASE , device_id=_SCREAMING_SNAKE_CASE , max_text_length=_SCREAMING_SNAKE_CASE , divergence_curve_discretization_size=_SCREAMING_SNAKE_CASE , mauve_scaling_factor=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , seed=_SCREAMING_SNAKE_CASE , )
return out
| 49
| 0
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( _lowercase , unittest.TestCase ):
lowerCamelCase : str = XLMTokenizer
lowerCamelCase : Optional[Any] = False
def UpperCAmelCase__ (self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase_ : Any = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''w</w>''',
'''r</w>''',
'''t</w>''',
'''lo''',
'''low''',
'''er</w>''',
'''low</w>''',
'''lowest</w>''',
'''newer</w>''',
'''wider</w>''',
'''<unk>''',
]
lowerCamelCase_ : List[str] = dict(zip(A , range(len(A ) ) ) )
lowerCamelCase_ : Dict = ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']
lowerCamelCase_ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCamelCase_ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' ) as fp:
fp.write(json.dumps(A ) )
with open(self.merges_file , '''w''' ) as fp:
fp.write('''\n'''.join(A ) )
def UpperCAmelCase__ (self , A ):
lowerCamelCase_ : Tuple = '''lower newer'''
lowerCamelCase_ : int = '''lower newer'''
return input_text, output_text
def UpperCAmelCase__ (self ):
lowerCamelCase_ : int = XLMTokenizer(self.vocab_file , self.merges_file )
lowerCamelCase_ : int = '''lower'''
lowerCamelCase_ : List[Any] = ['''low''', '''er</w>''']
lowerCamelCase_ : Dict = tokenizer.tokenize(A )
self.assertListEqual(A , A )
lowerCamelCase_ : List[Any] = tokens + ['''<unk>''']
lowerCamelCase_ : int = [1_4, 1_5, 2_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
@slow
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Union[str, Any] = XLMTokenizer.from_pretrained('''xlm-mlm-en-2048''' )
lowerCamelCase_ : Tuple = tokenizer.encode('''sequence builders''' , add_special_tokens=A )
lowerCamelCase_ : Any = tokenizer.encode('''multi-sequence build''' , add_special_tokens=A )
lowerCamelCase_ : Optional[int] = tokenizer.build_inputs_with_special_tokens(A )
lowerCamelCase_ : List[str] = tokenizer.build_inputs_with_special_tokens(A , A )
assert encoded_sentence == [0] + text + [1]
assert encoded_pair == [0] + text + [1] + text_a + [1]
| 318
|
'''simple docstring'''
from __future__ import annotations
import time
__lowercase : List[Any] = list[tuple[int, int]]
__lowercase : List[Any] = [
[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],
]
__lowercase : Dict = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class __lowercase :
def __init__(self , A , A , A , A , A ):
lowerCamelCase_ : Optional[int] = pos_x
lowerCamelCase_ : List[str] = pos_y
lowerCamelCase_ : List[Any] = (pos_y, pos_x)
lowerCamelCase_ : List[str] = goal_x
lowerCamelCase_ : Union[str, Any] = goal_y
lowerCamelCase_ : int = parent
class __lowercase :
def __init__(self , A , A ):
lowerCamelCase_ : Any = Node(start[1] , start[0] , goal[1] , goal[0] , A )
lowerCamelCase_ : Tuple = Node(goal[1] , goal[0] , goal[1] , goal[0] , A )
lowerCamelCase_ : Union[str, Any] = [self.start]
lowerCamelCase_ : List[str] = False
def UpperCAmelCase__ (self ):
while self.node_queue:
lowerCamelCase_ : Optional[Any] = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
lowerCamelCase_ : List[str] = True
return self.retrace_path(A )
lowerCamelCase_ : str = self.get_successors(A )
for node in successors:
self.node_queue.append(A )
if not self.reached:
return [self.start.pos]
return None
def UpperCAmelCase__ (self , A ):
lowerCamelCase_ : Dict = []
for action in delta:
lowerCamelCase_ : Any = parent.pos_x + action[1]
lowerCamelCase_ : Dict = 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 UpperCAmelCase__ (self , A ):
lowerCamelCase_ : int = node
lowerCamelCase_ : str = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowerCamelCase_ : List[Any] = current_node.parent
path.reverse()
return path
class __lowercase :
def __init__(self , A , A ):
lowerCamelCase_ : List[str] = BreadthFirstSearch(A , A )
lowerCamelCase_ : Any = BreadthFirstSearch(A , A )
lowerCamelCase_ : Union[str, Any] = False
def UpperCAmelCase__ (self ):
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
lowerCamelCase_ : List[str] = self.fwd_bfs.node_queue.pop(0 )
lowerCamelCase_ : int = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
lowerCamelCase_ : Optional[Any] = True
return self.retrace_bidirectional_path(
A , A )
lowerCamelCase_ : Optional[int] = current_bwd_node
lowerCamelCase_ : List[str] = current_fwd_node
lowerCamelCase_ : List[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 UpperCAmelCase__ (self , A , A ):
lowerCamelCase_ : List[str] = self.fwd_bfs.retrace_path(A )
lowerCamelCase_ : int = self.bwd_bfs.retrace_path(A )
bwd_path.pop()
bwd_path.reverse()
lowerCamelCase_ : Dict = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
__lowercase : List[str] = (0, 0)
__lowercase : List[Any] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__lowercase : Tuple = time.time()
__lowercase : int = BreadthFirstSearch(init, goal)
__lowercase : Dict = bfs.search()
__lowercase : Dict = time.time() - start_bfs_time
print('''Unidirectional BFS computation time : ''', bfs_time)
__lowercase : int = time.time()
__lowercase : Optional[Any] = BidirectionalBreadthFirstSearch(init, goal)
__lowercase : Any = bd_bfs.search()
__lowercase : Dict = time.time() - start_bd_bfs_time
print('''Bidirectional BFS computation time : ''', bd_bfs_time)
| 318
| 1
|
'''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
| 354
|
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[str] = 2
A : Dict = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(snake_case__ )
if n > 1:
factors.append(snake_case__ )
return factors
if __name__ == "__main__":
import doctest
doctest.testmod()
| 311
| 0
|
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
__snake_case =logging.get_logger("""transformers.models.encodec""")
__snake_case ={
"""quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
"""quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
"""quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
"""quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
__snake_case ={
"""encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
"""encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
"""encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
"""encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
"""encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
"""encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
"""encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
"""encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
"""encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
"""encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
"""encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
"""encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
"""encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
"""encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
"""encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
"""encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
"""encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
"""encoder.model.13.lstm""": """encoder.layers.13.lstm""",
"""encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
__snake_case ={
"""encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
"""encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
"""encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
"""encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
"""encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
"""encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
"""encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
"""encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
"""encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
"""encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
"""encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
"""encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
"""encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
"""encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
"""encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
"""encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
"""encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
"""encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
__snake_case ={
"""decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
"""decoder.model.1.lstm""": """decoder.layers.1.lstm""",
"""decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
"""decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
"""decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
"""decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
"""decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
"""decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
"""decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
"""decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
"""decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
"""decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
"""decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
"""decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
"""decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
"""decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
"""decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
"""decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
"""decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
__snake_case ={
"""decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
"""decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
"""decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
"""decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
"""decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
"""decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
"""decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
"""decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
"""decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
"""decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
"""decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
"""decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
"""decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
"""decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
"""decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
"""decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
"""decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
"""decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
__snake_case ={
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
__snake_case ={
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
__snake_case =[]
__snake_case =[]
def a_ ( lowerCamelCase : Optional[Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : int , lowerCamelCase : Any , lowerCamelCase : List[str] ):
for attribute in key.split('.' ):
lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
if weight_type is not None:
lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase ).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 == "weight_ih_l0":
lowerCAmelCase = value
elif weight_type == "weight_hh_l0":
lowerCAmelCase = value
elif weight_type == "bias_ih_l0":
lowerCAmelCase = value
elif weight_type == "bias_hh_l0":
lowerCAmelCase = value
elif weight_type == "weight_ih_l1":
lowerCAmelCase = value
elif weight_type == "weight_hh_l1":
lowerCAmelCase = value
elif weight_type == "bias_ih_l1":
lowerCAmelCase = value
elif weight_type == "bias_hh_l1":
lowerCAmelCase = value
else:
lowerCAmelCase = value
logger.info(f'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )
def a_ ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] ):
for key in ignore_keys:
if key.endswith('.*' ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowerCAmelCase , lowerCAmelCase = key.split('.*.' )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def a_ ( lowerCamelCase : List[Any] , lowerCamelCase : Any , lowerCamelCase : str ):
lowerCAmelCase = []
if model_name == "encodec_24khz" or "encodec_32khz":
lowerCAmelCase = MAPPING_24K
elif model_name == "encodec_48khz":
lowerCAmelCase = MAPPING_48K
else:
raise ValueError(f'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(lowerCamelCase , lowerCamelCase ):
logger.info(f'''{name} was ignored''' )
continue
lowerCAmelCase = False
for key, mapped_key in MAPPING.items():
if "*" in key:
lowerCAmelCase , lowerCAmelCase = key.split('.*.' )
if prefix in name and suffix in name:
lowerCAmelCase = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith('embed' ) and name.endswith('embed_avg' ):
continue
lowerCAmelCase = True
if "*" in mapped_key:
lowerCAmelCase = name.split(lowerCamelCase )[0].split('.' )[-2]
lowerCAmelCase = mapped_key.replace('*' , lowerCamelCase )
if "weight_g" in name:
lowerCAmelCase = 'weight_g'
elif "weight_v" in name:
lowerCAmelCase = 'weight_v'
elif "weight_ih_l0" in name:
lowerCAmelCase = 'weight_ih_l0'
elif "weight_hh_l0" in name:
lowerCAmelCase = 'weight_hh_l0'
elif "bias_ih_l0" in name:
lowerCAmelCase = 'bias_ih_l0'
elif "bias_hh_l0" in name:
lowerCAmelCase = 'bias_hh_l0'
elif "weight_ih_l1" in name:
lowerCAmelCase = 'weight_ih_l1'
elif "weight_hh_l1" in name:
lowerCAmelCase = 'weight_hh_l1'
elif "bias_ih_l1" in name:
lowerCAmelCase = 'bias_ih_l1'
elif "bias_hh_l1" in name:
lowerCAmelCase = 'bias_hh_l1'
elif "bias" in name:
lowerCAmelCase = 'bias'
elif "weight" in name:
lowerCAmelCase = 'weight'
elif "running_mean" in name:
lowerCAmelCase = 'running_mean'
elif "running_var" in name:
lowerCAmelCase = 'running_var'
elif "num_batches_tracked" in name:
lowerCAmelCase = 'num_batches_tracked'
else:
lowerCAmelCase = None
set_recursively(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
continue
if not is_used:
unused_weights.append(lowerCamelCase )
logger.warning(f'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def a_ ( lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : Dict=None , lowerCamelCase : Union[str, Any]=None , ):
if config_path is not None:
lowerCAmelCase = EncodecConfig.from_pretrained(lowerCamelCase )
else:
lowerCAmelCase = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
lowerCAmelCase = [8, 5, 4, 4]
lowerCAmelCase = [2.2]
lowerCAmelCase = 64
lowerCAmelCase = 32000
lowerCAmelCase = 2048
lowerCAmelCase = False
lowerCAmelCase = False
lowerCAmelCase = False
elif model_name == "encodec_48khz":
lowerCAmelCase = [8, 5, 4, 2]
lowerCAmelCase = [3.0, 6.0, 12.0, 24.0]
lowerCAmelCase = 48000
lowerCAmelCase = 2
lowerCAmelCase = False
lowerCAmelCase = 'time_group_norm'
lowerCAmelCase = True
lowerCAmelCase = 1.0
lowerCAmelCase = 0.01
else:
raise ValueError(f'''Unknown model name: {model_name}''' )
lowerCAmelCase = EncodecModel(lowerCamelCase )
lowerCAmelCase = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(lowerCamelCase )
lowerCAmelCase = torch.load(lowerCamelCase )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
lowerCAmelCase = original_checkpoint['best_state']
recursively_load_weights(lowerCamelCase , lowerCamelCase , lowerCamelCase )
model.save_pretrained(lowerCamelCase )
if repo_id:
print('Pushing to the hub...' )
feature_extractor.push_to_hub(lowerCamelCase )
model.push_to_hub(lowerCamelCase )
if __name__ == "__main__":
__snake_case =argparse.ArgumentParser()
parser.add_argument(
"""--model""",
default="""encodec_24khz""",
type=str,
help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
)
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
__snake_case =parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 4
|
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Any = len(_lowerCamelCase)
# We need to create solution object to save path.
__UpperCamelCase : List[str] = [[0 for _ in range(_lowerCamelCase)] for _ in range(_lowerCamelCase)]
__UpperCamelCase : Optional[int] = run_maze(_lowerCamelCase , 0 , 0 , _lowerCamelCase)
if solved:
print("\n".join(str(_lowerCamelCase) for row in solutions))
else:
print("No solution exists!")
return solved
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[int]] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : list[list[int]]) -> bool:
'''simple docstring'''
__UpperCamelCase : Tuple = len(_lowerCamelCase)
# Final check point.
if i == j == (size - 1):
__UpperCamelCase : Optional[int] = 1
return True
__UpperCamelCase : List[Any] = (not i < 0) and (not j < 0) # Check lower bounds
__UpperCamelCase : List[str] = (i < size) and (j < size) # Check upper bounds
if lower_flag and upper_flag:
# check for already visited and block points.
__UpperCamelCase : int = (not solutions[i][j]) and (not maze[i][j])
if block_flag:
# check visited
__UpperCamelCase : Tuple = 1
# check for directions
if (
run_maze(_lowerCamelCase , i + 1 , _lowerCamelCase , _lowerCamelCase)
or run_maze(_lowerCamelCase , _lowerCamelCase , j + 1 , _lowerCamelCase)
or run_maze(_lowerCamelCase , i - 1 , _lowerCamelCase , _lowerCamelCase)
or run_maze(_lowerCamelCase , _lowerCamelCase , j - 1 , _lowerCamelCase)
):
return True
__UpperCamelCase : Tuple = 0
return False
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 232
| 0
|
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1024 ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ , snake_case__ : List[str] = [], []
snake_case__ : List[str] = list(zip(__lowerCAmelCase , __lowerCAmelCase ) )
snake_case__ , snake_case__ : int = sorted_examples[0]
def is_too_big(__lowerCAmelCase ):
return tok(__lowerCAmelCase , return_tensors='''pt''' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
snake_case__ : Any = new_src + ''' ''' + src
snake_case__ : Optional[Any] = new_tgt + ''' ''' + tgt
if is_too_big(__lowerCAmelCase ) or is_too_big(__lowerCAmelCase ): # cant fit, finalize example
finished_src.append(__lowerCAmelCase )
finished_tgt.append(__lowerCAmelCase )
snake_case__ , snake_case__ : Tuple = src, tgt
else: # can fit, keep adding
snake_case__ , snake_case__ : Dict = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(__lowerCAmelCase )
finished_tgt.append(__lowerCAmelCase )
return finished_src, finished_tgt
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
"""simple docstring"""
snake_case__ : Tuple = Path(__lowerCAmelCase )
save_path.mkdir(exist_ok=__lowerCAmelCase )
for split in ["train"]:
snake_case__ , snake_case__ : Tuple = data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
snake_case__ : int = [x.rstrip() for x in Path(__lowerCAmelCase ).open().readlines()]
snake_case__ : List[Any] = [x.rstrip() for x in Path(__lowerCAmelCase ).open().readlines()]
snake_case__ , snake_case__ : Tuple = pack_examples(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
print(f"""packed {split} split from {len(__lowerCAmelCase )} examples -> {len(__lowerCAmelCase )}.""" )
Path(save_path / f"""{split}.source""" ).open('''w''' ).write('''\n'''.join(__lowerCAmelCase ) )
Path(save_path / f"""{split}.target""" ).open('''w''' ).write('''\n'''.join(__lowerCAmelCase ) )
for split in ["val", "test"]:
snake_case__ , snake_case__ : Tuple = data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
shutil.copyfile(__lowerCAmelCase , save_path / f"""{split}.source""" )
shutil.copyfile(__lowerCAmelCase , save_path / f"""{split}.target""" )
def _lowerCAmelCase ( ) -> List[str]:
"""simple docstring"""
snake_case__ : Dict = argparse.ArgumentParser()
parser.add_argument('''--tok_name''' , type=__lowerCAmelCase , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''--max_seq_len''' , type=__lowerCAmelCase , default=128 )
parser.add_argument('''--data_dir''' , type=__lowerCAmelCase )
parser.add_argument('''--save_path''' , type=__lowerCAmelCase )
snake_case__ : Dict = parser.parse_args()
snake_case__ : Any = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(__lowerCAmelCase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 359
|
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
class a :
def __init__( self :str ,__lowercase :Optional[Any] ,__lowercase :List[Any]=1_3 ,__lowercase :str=7 ,__lowercase :Dict=True ,__lowercase :Any=True ,__lowercase :str=True ,__lowercase :Any=True ,__lowercase :Tuple=9_9 ,__lowercase :List[str]=3_2 ,__lowercase :int=5 ,__lowercase :Union[str, Any]=4 ,__lowercase :List[str]=4 ,__lowercase :Any="gelu" ,__lowercase :Any=0.0 ,__lowercase :Tuple=0.1 ,__lowercase :str=True ,__lowercase :Tuple=5_1_2 ,__lowercase :Dict=1_6 ,__lowercase :Tuple=2 ,__lowercase :List[str]=0.02 ,__lowercase :Dict=3 ,__lowercase :Optional[int]=4 ,__lowercase :Tuple=None ,):
snake_case__ : Optional[int] = parent
snake_case__ : Optional[Any] = batch_size
snake_case__ : Optional[Any] = seq_length
snake_case__ : Tuple = is_training
snake_case__ : Optional[Any] = use_input_mask
snake_case__ : List[Any] = use_token_type_ids
snake_case__ : str = use_labels
snake_case__ : List[Any] = vocab_size
snake_case__ : Optional[int] = hidden_size
snake_case__ : List[Any] = num_hidden_layers
snake_case__ : str = num_attention_heads
snake_case__ : int = intermediate_multiple_size
snake_case__ : Tuple = hidden_act
snake_case__ : Optional[Any] = hidden_dropout
snake_case__ : str = attention_dropout
snake_case__ : List[str] = weight_tying
snake_case__ : Optional[Any] = max_position_embeddings
snake_case__ : Optional[int] = type_vocab_size
snake_case__ : str = type_sequence_label_size
snake_case__ : Dict = initializer_range
snake_case__ : int = num_labels
snake_case__ : int = num_choices
snake_case__ : int = scope
def __lowerCamelCase ( self :List[str] ):
snake_case__ : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case__ : str = None
if self.use_input_mask:
snake_case__ : int = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : Union[str, Any] = None
if self.use_labels:
snake_case__ : int = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
snake_case__ : Optional[Any] = self.get_config()
return config, input_ids, input_mask, token_labels
def __lowerCamelCase ( self :int ):
return GPTNeoXJapaneseConfig(
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_multiple_size=self.intermediate_multiple_size ,hidden_act=self.hidden_act ,hidden_dropout=self.hidden_dropout ,attention_dropout=self.attention_dropout ,weight_tying=self.weight_tying ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=__lowercase ,initializer_range=self.initializer_range ,)
def __lowerCamelCase ( self :str ):
snake_case__ , snake_case__ , snake_case__ , snake_case__ : str = self.prepare_config_and_inputs()
snake_case__ : Union[str, Any] = True
return config, input_ids, input_mask, token_labels
def __lowerCamelCase ( self :List[Any] ,__lowercase :Any ,__lowercase :Dict ,__lowercase :Optional[Any] ):
snake_case__ : Union[str, Any] = GPTNeoXJapaneseModel(config=__lowercase )
model.to(__lowercase )
model.eval()
snake_case__ : Union[str, Any] = model(__lowercase ,attention_mask=__lowercase )
snake_case__ : Optional[Any] = model(__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self :Any ,__lowercase :Union[str, Any] ,__lowercase :Tuple ,__lowercase :Union[str, Any] ):
snake_case__ : Any = True
snake_case__ : Tuple = GPTNeoXJapaneseModel(__lowercase )
model.to(__lowercase )
model.eval()
snake_case__ : str = model(__lowercase ,attention_mask=__lowercase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self :Any ,__lowercase :List[Any] ,__lowercase :List[Any] ,__lowercase :Optional[Any] ,__lowercase :Any ):
snake_case__ : Any = GPTNeoXJapaneseForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
snake_case__ : Any = model(__lowercase ,attention_mask=__lowercase ,labels=__lowercase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self :Optional[int] ,__lowercase :Any ,__lowercase :int ,__lowercase :List[str] ):
snake_case__ : Optional[int] = True
snake_case__ : Optional[int] = GPTNeoXJapaneseForCausalLM(config=__lowercase )
model.to(__lowercase )
model.eval()
# first forward pass
snake_case__ : List[Any] = model(__lowercase ,attention_mask=__lowercase ,use_cache=__lowercase )
snake_case__ : Dict = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
snake_case__ : Optional[Any] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
snake_case__ : int = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
snake_case__ : Optional[int] = torch.cat([input_ids, next_tokens] ,dim=-1 )
snake_case__ : Optional[int] = torch.cat([input_mask, next_mask] ,dim=-1 )
snake_case__ : Dict = model(__lowercase ,attention_mask=__lowercase ,output_hidden_states=__lowercase )
snake_case__ : Tuple = output_from_no_past['''hidden_states'''][0]
snake_case__ : List[str] = model(
__lowercase ,attention_mask=__lowercase ,past_key_values=__lowercase ,output_hidden_states=__lowercase ,)['''hidden_states'''][0]
# select random slice
snake_case__ : Any = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
snake_case__ : Tuple = output_from_no_past[:, -3:, random_slice_idx].detach()
snake_case__ : str = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowercase ,__lowercase ,atol=1e-3 ) )
def __lowerCamelCase ( self :Dict ):
snake_case__ : List[Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[Any] = config_and_inputs
snake_case__ : List[str] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class a ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Union[str, Any] = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
__lowerCAmelCase : List[str] = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
__lowerCAmelCase : int = (
{"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
__lowerCAmelCase : List[Any] = False
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : str = False
def __lowerCamelCase ( self :Any ):
snake_case__ : int = GPTNeoXJapaneseModelTester(self )
snake_case__ : Any = ConfigTester(self ,config_class=__lowercase ,hidden_size=3_7 )
def __lowerCamelCase ( self :Any ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self :str ):
snake_case__ , snake_case__ , snake_case__ , snake_case__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(__lowercase ,__lowercase ,__lowercase )
def __lowerCamelCase ( self :Optional[Any] ):
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(__lowercase ,__lowercase ,__lowercase )
def __lowerCamelCase ( self :Optional[Any] ):
# This regression test was failing with PyTorch < 1.3
snake_case__ , snake_case__ , snake_case__ , snake_case__ : int = self.model_tester.prepare_config_and_inputs_for_decoder()
snake_case__ : List[str] = None
self.model_tester.create_and_check_model_as_decoder(__lowercase ,__lowercase ,__lowercase )
def __lowerCamelCase ( self :Optional[int] ):
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(__lowercase ,__lowercase ,__lowercase )
def __lowerCamelCase ( self :str ):
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*__lowercase )
@slow
def __lowerCamelCase ( self :Dict ):
snake_case__ : str = '''abeja/gpt-neox-japanese-2.7b'''
snake_case__ : int = ['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']
snake_case__ : Optional[int] = [
'''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
'''100年後に必要とされる会社は、「人」が中心の会社です。''',
'''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
'''国境の長いトンネルを抜けると、そこは雪国だった。''',
'''美味しい日本食といえば、やっぱりお寿司ですよね。''',
]
snake_case__ : Optional[int] = GPTNeoXJapaneseTokenizer.from_pretrained(__lowercase )
snake_case__ : Union[str, Any] = GPTNeoXJapaneseForCausalLM.from_pretrained(__lowercase )
snake_case__ : Optional[int] = []
for prompt in prompts:
snake_case__ : Dict = tokenizer(__lowercase ,return_tensors='''pt''' ).input_ids
snake_case__ : Union[str, Any] = model.generate(__lowercase ,max_length=5_0 )
snake_case__ : int = tokenizer.batch_decode(__lowercase ,skip_special_tokens=__lowercase )
predicted_outputs += generated_string
self.assertListEqual(__lowercase ,__lowercase )
| 44
| 0
|
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
lowercase : Optional[int] = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n Schwartz, Rich and\n Micciulla, Linnea and\n Makhoul, John",\n booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n month = aug # " 8-12",\n year = "2006",\n address = "Cambridge, Massachusetts, USA",\n publisher = "Association for Machine Translation in the Americas",\n url = "https://aclanthology.org/2006.amta-papers.25",\n pages = "223--231",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
lowercase : Union[str, Any] = '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'
lowercase : Any = '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n predictions (list of str): The system stream (a sequence of segments).\n references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n Only applies if `normalized = True`. Defaults to `False`.\n case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n \'num_edits\' (int): The cumulative number of edits\n \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n Example 1:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n Example 2:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n Example 3:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... normalized=True,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n Example 4:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n Example 5:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCamelCase__ ( datasets.Metric):
'''simple docstring'''
def _lowerCamelCase ( self :Tuple ) -> Union[str, Any]:
if version.parse(scb.__version__ ) < version.parse("1.4.12" ):
raise ImportWarning(
"To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
"You can install it with `pip install \"sacrebleu>=1.4.12\"`." )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="http://www.cs.umd.edu/~snover/tercom/" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Sequence(datasets.Value("string" , id="sequence" ) , id="references" ),
} ) , codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"] , reference_urls=[
"https://github.com/jhclark/tercom",
] , )
def _lowerCamelCase ( self :str , a :Union[str, Any] , a :Dict , a :bool = False , a :bool = False , a :bool = False , a :bool = False , ) -> Dict:
__UpperCamelCase : str = len(references[0] )
if any(len(a ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
__UpperCamelCase : Dict = [[refs[i] for refs in references] for i in range(a )]
__UpperCamelCase : Optional[Any] = TER(
normalized=a , no_punct=a , asian_support=a , case_sensitive=a , )
__UpperCamelCase : int = sb_ter.corpus_score(a , a )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 232
|
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamelCase : int) -> int:
return int(128 + factor * (c - 128))
return img.point(_lowerCamelCase)
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change contrast to 170
lowercase : Tuple = change_contrast(img, 170)
cont_img.save('image_data/lena_high_contrast.png', format='png')
| 232
| 1
|
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class __lowercase :
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=True , _UpperCAmelCase=99 , _UpperCAmelCase=64 , _UpperCAmelCase=5 , _UpperCAmelCase=4 , _UpperCAmelCase=64 , _UpperCAmelCase="gelu" , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=512 , _UpperCAmelCase=16 , _UpperCAmelCase=2 , _UpperCAmelCase=0.0_2 , _UpperCAmelCase=3 , _UpperCAmelCase=4 , _UpperCAmelCase=None , ):
__a : Union[str, Any] = parent
__a : List[str] = batch_size
__a : List[str] = seq_length
__a : Union[str, Any] = is_training
__a : Optional[int] = use_input_mask
__a : Union[str, Any] = use_token_type_ids
__a : List[Any] = use_labels
__a : str = vocab_size
__a : Dict = hidden_size
__a : List[str] = num_hidden_layers
__a : List[str] = num_attention_heads
__a : Tuple = intermediate_size
__a : str = hidden_act
__a : List[Any] = hidden_dropout_prob
__a : Union[str, Any] = attention_probs_dropout_prob
__a : Dict = max_position_embeddings
__a : Dict = type_vocab_size
__a : Dict = type_sequence_label_size
__a : List[str] = initializer_range
__a : int = num_labels
__a : Optional[int] = num_choices
__a : Optional[int] = scope
def _lowerCamelCase ( self ):
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def _lowerCamelCase ( self ):
__a : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : Dict = None
if self.use_input_mask:
__a : str = random_attention_mask([self.batch_size, self.seq_length] )
__a : Any = None
__a : Any = None
__a : Any = None
if self.use_labels:
__a : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__a : Tuple = ids_tensor([self.batch_size] , self.num_choices )
__a : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self ):
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : List[Any] = MPNetModel(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : int = model(_UpperCAmelCase , _UpperCAmelCase )
__a : Any = 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 _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : Union[str, Any] = MPNetForQuestionAnswering(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : Tuple = model(
_UpperCAmelCase , attention_mask=_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 _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : List[Any] = self.num_labels
__a : Optional[int] = MPNetForSequenceClassification(_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : List[str] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : Tuple = self.num_choices
__a : Union[str, Any] = MPNetForMultipleChoice(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : Dict = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__a : Optional[Any] = model(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a : Tuple = self.num_labels
__a : str = MPNetForTokenClassification(config=_UpperCAmelCase )
model.to(_UpperCAmelCase )
model.eval()
__a : Optional[Any] = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , labels=_UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCamelCase ( self ):
__a : Optional[Any] = self.prepare_config_and_inputs()
((__a) , (__a) , (__a) , (__a) , (__a) , (__a)) : Dict = config_and_inputs
__a : List[Any] = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowercase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__lowerCAmelCase = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
__lowerCAmelCase = (
{
'''feature-extraction''': MPNetModel,
'''fill-mask''': MPNetForMaskedLM,
'''question-answering''': MPNetForQuestionAnswering,
'''text-classification''': MPNetForSequenceClassification,
'''token-classification''': MPNetForTokenClassification,
'''zero-shot''': MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
__lowerCAmelCase = False
__lowerCAmelCase = True
def _lowerCamelCase ( self ):
__a : str = MPNetModelTester(self )
__a : List[str] = ConfigTester(self , config_class=_UpperCAmelCase , hidden_size=37 )
def _lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def _lowerCamelCase ( self ):
__a : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*_UpperCAmelCase )
def _lowerCamelCase ( self ):
__a : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*_UpperCAmelCase )
@require_torch
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self ):
__a : Optional[int] = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
__a : List[Any] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__a : List[Any] = model(_UpperCAmelCase )[0]
__a : str = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _UpperCAmelCase )
__a : Tuple = torch.tensor(
[[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , _UpperCAmelCase , atol=1e-4 ) )
| 188
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = '''openai/whisper-base'''
__lowerCAmelCase = (
'''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
'''transcribed text.'''
)
__lowerCAmelCase = '''transcriber'''
__lowerCAmelCase = WhisperProcessor
__lowerCAmelCase = WhisperForConditionalGeneration
__lowerCAmelCase = ['''audio''']
__lowerCAmelCase = ['''text''']
def _lowerCamelCase ( self , _UpperCAmelCase ):
return self.pre_processor(_UpperCAmelCase , return_tensors='''pt''' ).input_features
def _lowerCamelCase ( self , _UpperCAmelCase ):
return self.model.generate(inputs=_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase ):
return self.pre_processor.batch_decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )[0]
| 188
| 1
|
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
_a : str= logging.get_logger(__name__)
def __UpperCAmelCase ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Dict ) -> List[str]:
'''simple docstring'''
__snake_case : Union[str, Any] = set()
__snake_case : List[str] = []
def parse_line(UpperCAmelCase_ : Tuple ):
for line in fp:
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
__snake_case : str = line.decode('UTF-8' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(' ' ):
# process a single warning and move it to `selected_warnings`.
if len(UpperCAmelCase_ ) > 0:
__snake_case : Optional[Any] = '\n'.join(UpperCAmelCase_ )
# Only keep the warnings specified in `targets`
if any(F": {x}: " in warning for x in targets ):
selected_warnings.add(UpperCAmelCase_ )
buffer.clear()
continue
else:
__snake_case : Dict = line.strip()
buffer.append(UpperCAmelCase_ )
if from_gh:
for filename in os.listdir(UpperCAmelCase_ ):
__snake_case : Tuple = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ )
if not os.path.isdir(UpperCAmelCase_ ):
# read the file
if filename != "warnings.txt":
continue
with open(UpperCAmelCase_ ) as fp:
parse_line(UpperCAmelCase_ )
else:
try:
with zipfile.ZipFile(UpperCAmelCase_ ) as z:
for filename in z.namelist():
if not os.path.isdir(UpperCAmelCase_ ):
# read the file
if filename != "warnings.txt":
continue
with z.open(UpperCAmelCase_ ) as fp:
parse_line(UpperCAmelCase_ )
except Exception:
logger.warning(
F"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped." )
return selected_warnings
def __UpperCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any] ) -> Dict:
'''simple docstring'''
__snake_case : Optional[Any] = set()
__snake_case : Tuple = [os.path.join(UpperCAmelCase_ , UpperCAmelCase_ ) for p in os.listdir(UpperCAmelCase_ ) if (p.endswith('.zip' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(UpperCAmelCase_ , UpperCAmelCase_ ) )
return selected_warnings
if __name__ == "__main__":
def __UpperCAmelCase ( UpperCAmelCase_ : Union[str, Any] ) -> str:
'''simple docstring'''
return values.split(',' )
_a : Optional[int]= argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Where to store the downloaded artifacts and other result files.",
)
parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
# optional parameters
parser.add_argument(
"--targets",
default="DeprecationWarning,UserWarning,FutureWarning",
type=list_str,
help="Comma-separated list of target warning(s) which we want to extract.",
)
parser.add_argument(
"--from_gh",
action="store_true",
help="If running from a GitHub action workflow and collecting warnings from its artifacts.",
)
_a : Tuple= parser.parse_args()
_a : Tuple= args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
_a : int= get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print("=" * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
_a : Dict= extract_warnings(args.output_dir, args.targets)
_a : int= sorted(selected_warnings)
with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 172
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int= logging.get_logger(__name__)
_a : Optional[Any]= {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[Any] = """lilt"""
def __init__(self : Dict , _A : Any=3_05_22 , _A : Union[str, Any]=7_68 , _A : Any=12 , _A : Tuple=12 , _A : Optional[int]=30_72 , _A : Tuple="gelu" , _A : str=0.1 , _A : List[Any]=0.1 , _A : Union[str, Any]=5_12 , _A : Any=2 , _A : Tuple=0.02 , _A : List[str]=1E-12 , _A : Optional[int]=0 , _A : Optional[Any]="absolute" , _A : Any=None , _A : List[Any]=4 , _A : Optional[int]=10_24 , **_A : Union[str, Any] , ) -> Tuple:
super().__init__(pad_token_id=_A , **_A)
__snake_case : Optional[int] = vocab_size
__snake_case : List[Any] = hidden_size
__snake_case : Any = num_hidden_layers
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[int] = hidden_act
__snake_case : List[str] = intermediate_size
__snake_case : Union[str, Any] = hidden_dropout_prob
__snake_case : Dict = attention_probs_dropout_prob
__snake_case : List[Any] = max_position_embeddings
__snake_case : Dict = type_vocab_size
__snake_case : List[Any] = initializer_range
__snake_case : Optional[Any] = layer_norm_eps
__snake_case : Optional[int] = position_embedding_type
__snake_case : Any = classifier_dropout
__snake_case : Optional[int] = channel_shrink_ratio
__snake_case : Tuple = max_ad_position_embeddings
| 172
| 1
|
def lowercase__ ( lowercase_ ,lowercase_ ,lowercase_ ,lowercase_ ) -> str:
"""simple docstring"""
_UpperCamelCase : Any = len(__SCREAMING_SNAKE_CASE ), len(grid[0] )
if (
min(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_UpperCamelCase : List[Any] = 0
count += depth_first_search(__SCREAMING_SNAKE_CASE ,row + 1 ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
count += depth_first_search(__SCREAMING_SNAKE_CASE ,row - 1 ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE )
count += depth_first_search(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,col + 1 ,__SCREAMING_SNAKE_CASE )
count += depth_first_search(__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,col - 1 ,__SCREAMING_SNAKE_CASE )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370
|
"""simple docstring"""
from typing import Any
def lowercase__ ( lowercase_ ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
_UpperCamelCase : Dict = [input_list.count(lowercase_ ) for value in input_list]
_UpperCamelCase : Union[str, Any] = max(lowercase_ ) # Gets the maximum count in the input list.
# Gets values of modes
return sorted({input_list[i] for i, value in enumerate(lowercase_ ) if value == y} )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 310
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """ctrl"""
lowerCamelCase__ = ["""past_key_values"""]
lowerCamelCase__ = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , lowercase=246534 , lowercase=256 , lowercase=1280 , lowercase=8192 , lowercase=48 , lowercase=16 , lowercase=0.1 , lowercase=0.1 , lowercase=1E-6 , lowercase=0.02 , lowercase=True , **lowercase , ):
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Dict = n_positions
_lowerCamelCase : Optional[int] = n_embd
_lowerCamelCase : str = n_layer
_lowerCamelCase : Union[str, Any] = n_head
_lowerCamelCase : Any = dff
_lowerCamelCase : int = resid_pdrop
_lowerCamelCase : Dict = embd_pdrop
_lowerCamelCase : Union[str, Any] = layer_norm_epsilon
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : str = use_cache
super().__init__(**lowercase )
| 96
|
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class A_ ( unittest.TestCase ):
def __init__( self : Tuple ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Any=1_3 ,SCREAMING_SNAKE_CASE__ : int=7 ,SCREAMING_SNAKE_CASE__ : str=True ,SCREAMING_SNAKE_CASE__ : Dict=True ,SCREAMING_SNAKE_CASE__ : str=True ,SCREAMING_SNAKE_CASE__ : Dict=True ,SCREAMING_SNAKE_CASE__ : List[Any]=9_9 ,SCREAMING_SNAKE_CASE__ : List[Any]=3_2 ,SCREAMING_SNAKE_CASE__ : int=5 ,SCREAMING_SNAKE_CASE__ : List[Any]=4 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=3_7 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu" ,SCREAMING_SNAKE_CASE__ : int=0.1 ,SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 ,SCREAMING_SNAKE_CASE__ : Optional[int]=5_1_2 ,SCREAMING_SNAKE_CASE__ : Dict=1_6 ,SCREAMING_SNAKE_CASE__ : Dict=2 ,SCREAMING_SNAKE_CASE__ : Optional[int]=0.02 ,SCREAMING_SNAKE_CASE__ : Dict=4 ,):
__lowerCamelCase : int = parent
__lowerCamelCase : Dict = batch_size
__lowerCamelCase : Union[str, Any] = seq_length
__lowerCamelCase : List[Any] = is_training
__lowerCamelCase : Tuple = use_attention_mask
__lowerCamelCase : List[str] = use_token_type_ids
__lowerCamelCase : Any = use_labels
__lowerCamelCase : List[str] = vocab_size
__lowerCamelCase : Any = hidden_size
__lowerCamelCase : Tuple = num_hidden_layers
__lowerCamelCase : Union[str, Any] = num_attention_heads
__lowerCamelCase : Union[str, Any] = intermediate_size
__lowerCamelCase : List[Any] = hidden_act
__lowerCamelCase : int = hidden_dropout_prob
__lowerCamelCase : int = attention_probs_dropout_prob
__lowerCamelCase : Union[str, Any] = max_position_embeddings
__lowerCamelCase : Union[str, Any] = type_vocab_size
__lowerCamelCase : List[str] = type_sequence_label_size
__lowerCamelCase : Tuple = initializer_range
__lowerCamelCase : Optional[int] = num_choices
def lowerCAmelCase ( self : Union[str, Any]):
__lowerCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size)
__lowerCamelCase : Union[str, Any] = None
if self.use_attention_mask:
__lowerCamelCase : Any = random_attention_mask([self.batch_size, self.seq_length])
__lowerCamelCase : str = 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 ,tie_weights_=SCREAMING_SNAKE_CASE__ ,)
return config, input_ids, attention_mask
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : List[str] = self.prepare_config_and_inputs()
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : Dict = config_and_inputs
__lowerCamelCase : Any = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class A_ ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
_UpperCAmelCase : Dict = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase ( self : Optional[Any]):
__lowerCamelCase : Tuple = FlaxDistilBertModelTester(self)
@slow
def lowerCAmelCase ( self : int):
for model_class_name in self.all_model_classes:
__lowerCamelCase : List[Any] = model_class_name.from_pretrained('distilbert-base-uncased')
__lowerCamelCase : List[str] = model(np.ones((1, 1)))
self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
@require_flax
class A_ ( unittest.TestCase ):
@slow
def lowerCAmelCase ( self : str):
__lowerCamelCase : Union[str, Any] = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased')
__lowerCamelCase : str = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]])
__lowerCamelCase : List[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
__lowerCamelCase : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ ,attention_mask=SCREAMING_SNAKE_CASE__)[0]
__lowerCamelCase : Optional[int] = (1, 1_1, 7_6_8)
self.assertEqual(output.shape ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,SCREAMING_SNAKE_CASE__ ,atol=1E-4))
| 73
| 0
|
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
_a = filter(lambda UpperCamelCase : p.requires_grad , model.parameters() )
_a = sum([np.prod(p.size() ) for p in model_parameters] )
return params
_snake_case : Any = logging.getLogger(__name__)
def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : List[str] ):
'''simple docstring'''
if metric == "rouge2":
_a = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
_a = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
_a = '''{val_avg_em:.4f}-{step_count}'''
elif metric == "loss":
_a = '''{val_avg_loss:.4f}-{step_count}'''
else:
raise NotImplementedError(
f'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
''' function.''' )
_a = ModelCheckpoint(
dirpath=UpperCamelCase , filename=UpperCamelCase , monitor=f'val_{metric}' , mode='''max''' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def snake_case_ (UpperCamelCase : int , UpperCamelCase : str ):
'''simple docstring'''
return EarlyStopping(
monitor=f'val_{metric}' , mode='''min''' if '''loss''' in metric else '''max''' , patience=UpperCamelCase , verbose=UpperCamelCase , )
class A ( pl.Callback ):
def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int ) -> Any:
"""simple docstring"""
_a = {F'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(lowerCAmelCase_ )
@rank_zero_only
def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : pl.Trainer , lowerCAmelCase_ : pl.LightningModule , lowerCAmelCase_ : str , lowerCAmelCase_ : str=True ) -> None:
"""simple docstring"""
logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****' )
_a = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
_a = Path(pl_module.hparams.output_dir )
if type_path == "test":
_a = od / '''test_results.txt'''
_a = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
_a = od / F'{type_path}_results/{trainer.global_step:05d}.txt'
_a = od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
results_file.parent.mkdir(exist_ok=lowerCAmelCase_ )
generations_file.parent.mkdir(exist_ok=lowerCAmelCase_ )
with open(lowerCAmelCase_ , '''a+''' ) as writer:
for key in sorted(lowerCAmelCase_ ):
if key in ["log", "progress_bar", "preds"]:
continue
_a = metrics[key]
if isinstance(lowerCAmelCase_ , torch.Tensor ):
_a = val.item()
_a = F'{key}: {val:.6f}\n'
writer.write(lowerCAmelCase_ )
if not save_generations:
return
if "preds" in metrics:
_a = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(lowerCAmelCase_ )
@rank_zero_only
def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple ) -> Optional[Any]:
"""simple docstring"""
try:
_a = pl_module.model.model.num_parameters()
except AttributeError:
_a = pl_module.model.num_parameters()
_a = count_trainable_parameters(lowerCAmelCase_ )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6} )
@rank_zero_only
def __lowerCAmelCase ( self : Union[str, Any] , lowerCAmelCase_ : pl.Trainer , lowerCAmelCase_ : pl.LightningModule ) -> List[Any]:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(lowerCAmelCase_ , lowerCAmelCase_ , '''test''' )
@rank_zero_only
def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : pl.Trainer , lowerCAmelCase_ : int ) -> Optional[int]:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 179
|
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def snake_case_ (UpperCamelCase : Namespace ):
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
_snake_case : List[Any] = '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'
class A ( _a ):
@staticmethod
def __lowerCAmelCase ( lowerCAmelCase_ : ArgumentParser ) -> Any:
"""simple docstring"""
_a = parser.add_parser(
'''convert''' , help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.''' , )
train_parser.add_argument('''--model_type''' , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help='''Model\'s type.''' )
train_parser.add_argument(
'''--tf_checkpoint''' , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help='''TensorFlow checkpoint path or folder.''' )
train_parser.add_argument(
'''--pytorch_dump_output''' , type=lowerCAmelCase_ , required=lowerCAmelCase_ , help='''Path to the PyTorch saved model output.''' )
train_parser.add_argument('''--config''' , type=lowerCAmelCase_ , default='''''' , help='''Configuration file path or folder.''' )
train_parser.add_argument(
'''--finetuning_task_name''' , type=lowerCAmelCase_ , default=lowerCAmelCase_ , help='''Optional fine-tuning task name if the TF model was a finetuned model.''' , )
train_parser.set_defaults(func=lowerCAmelCase_ )
def __init__( self : int , lowerCAmelCase_ : str , lowerCAmelCase_ : str , lowerCAmelCase_ : str , lowerCAmelCase_ : str , lowerCAmelCase_ : str , *lowerCAmelCase_ : Optional[Any] , ) -> List[str]:
"""simple docstring"""
_a = logging.get_logger('''transformers-cli/converting''' )
self._logger.info(F'Loading model {model_type}' )
_a = model_type
_a = tf_checkpoint
_a = pytorch_dump_output
_a = config
_a = finetuning_task_name
def __lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCAmelCase_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCAmelCase_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCAmelCase_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(lowerCAmelCase_ )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCAmelCase_ )
if "ckpt" in self._tf_checkpoint.lower():
_a = self._tf_checkpoint
_a = ''''''
else:
_a = self._tf_checkpoint
_a = ''''''
convert_transfo_xl_checkpoint_to_pytorch(
lowerCAmelCase_ , self._config , self._pytorch_dump_output , lowerCAmelCase_ )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCAmelCase_ )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(lowerCAmelCase_ )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
'''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]''' )
| 179
| 1
|
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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case : Tuple = logging.get_logger(__name__)
def a_ ( lowerCAmelCase_ : Tuple ):
# initialize config
if "resnet-50" in model_name:
__lowerCAmelCase = ResNetConfig.from_pretrained('microsoft/resnet-50' )
elif "resnet-101" in model_name:
__lowerCAmelCase = ResNetConfig.from_pretrained('microsoft/resnet-101' )
else:
raise ValueError('Model name should include either resnet50 or resnet101' )
__lowerCAmelCase = DetrConfig(use_timm_backbone=lowerCAmelCase_, backbone_config=lowerCAmelCase_ )
# set label attributes
__lowerCAmelCase = 'panoptic' in model_name
if is_panoptic:
__lowerCAmelCase = 250
else:
__lowerCAmelCase = 91
__lowerCAmelCase = 'huggingface/label-files'
__lowerCAmelCase = 'coco-detection-id2label.json'
__lowerCAmelCase = json.load(open(hf_hub_download(lowerCAmelCase_, lowerCAmelCase_, repo_type='dataset' ), 'r' ) )
__lowerCAmelCase = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def a_ ( lowerCAmelCase_ : List[str] ):
# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCAmelCase = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight') )
rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight') )
rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias') )
rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean') )
rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
F"""encoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
F"""decoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
) )
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
) )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
] )
return rename_keys
def a_ ( lowerCAmelCase_ : Any, lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : Tuple ):
__lowerCAmelCase = state_dict.pop(lowerCAmelCase_ )
__lowerCAmelCase = val
def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : int=False ):
__lowerCAmelCase = ''
if is_panoptic:
__lowerCAmelCase = 'detr.'
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
__lowerCAmelCase = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
__lowerCAmelCase = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCAmelCase = in_proj_weight[:256, :]
__lowerCAmelCase = in_proj_bias[:256]
__lowerCAmelCase = in_proj_weight[256:512, :]
__lowerCAmelCase = in_proj_bias[256:512]
__lowerCAmelCase = in_proj_weight[-256:, :]
__lowerCAmelCase = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
__lowerCAmelCase = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
__lowerCAmelCase = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCAmelCase = in_proj_weight[:256, :]
__lowerCAmelCase = in_proj_bias[:256]
__lowerCAmelCase = in_proj_weight[256:512, :]
__lowerCAmelCase = in_proj_bias[256:512]
__lowerCAmelCase = in_proj_weight[-256:, :]
__lowerCAmelCase = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
__lowerCAmelCase = state_dict.pop(
F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
__lowerCAmelCase = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
__lowerCAmelCase = in_proj_weight_cross_attn[:256, :]
__lowerCAmelCase = in_proj_bias_cross_attn[:256]
__lowerCAmelCase = in_proj_weight_cross_attn[256:512, :]
__lowerCAmelCase = in_proj_bias_cross_attn[256:512]
__lowerCAmelCase = in_proj_weight_cross_attn[-256:, :]
__lowerCAmelCase = in_proj_bias_cross_attn[-256:]
def a_ ( ):
__lowerCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCAmelCase = Image.open(requests.get(lowerCAmelCase_, stream=lowerCAmelCase_ ).raw )
return im
@torch.no_grad()
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : Any=None, lowerCAmelCase_ : str=False ):
__lowerCAmelCase , __lowerCAmelCase = get_detr_config(lowerCAmelCase_ )
# load original model from torch hub
__lowerCAmelCase = {
'detr-resnet-50': 'detr_resnet50',
'detr-resnet-101': 'detr_resnet101',
}
logger.info(F"""Converting model {model_name}...""" )
__lowerCAmelCase = torch.hub.load('facebookresearch/detr', model_name_to_original_name[model_name], pretrained=lowerCAmelCase_ ).eval()
__lowerCAmelCase = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(lowerCAmelCase_ ):
if is_panoptic:
__lowerCAmelCase = 'detr.' + src
rename_key(lowerCAmelCase_, lowerCAmelCase_, lowerCAmelCase_ )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCAmelCase_, is_panoptic=lowerCAmelCase_ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
__lowerCAmelCase = 'detr.model.' if is_panoptic else 'model.'
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
__lowerCAmelCase = state_dict.pop(lowerCAmelCase_ )
__lowerCAmelCase = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
__lowerCAmelCase = state_dict.pop(lowerCAmelCase_ )
__lowerCAmelCase = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
__lowerCAmelCase = state_dict.pop(lowerCAmelCase_ )
__lowerCAmelCase = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
__lowerCAmelCase = state_dict.pop(lowerCAmelCase_ )
__lowerCAmelCase = val
# finally, create HuggingFace model and load state dict
__lowerCAmelCase = DetrForSegmentation(lowerCAmelCase_ ) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_ )
model.load_state_dict(lowerCAmelCase_ )
model.eval()
# verify our conversion on an image
__lowerCAmelCase = 'coco_panoptic' if is_panoptic else 'coco_detection'
__lowerCAmelCase = DetrImageProcessor(format=lowerCAmelCase_ )
__lowerCAmelCase = processor(images=prepare_img(), return_tensors='pt' )
__lowerCAmelCase = encoding['pixel_values']
__lowerCAmelCase = detr(lowerCAmelCase_ )
__lowerCAmelCase = model(lowerCAmelCase_ )
assert torch.allclose(outputs.logits, original_outputs['pred_logits'], atol=1E-3 )
assert torch.allclose(outputs.pred_boxes, original_outputs['pred_boxes'], atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks, original_outputs['pred_masks'], atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('Uploading PyTorch model and image processor to the hub...' )
model.push_to_hub(F"""nielsr/{model_name}""" )
processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
_snake_case : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='detr-resnet-50',
type=str,
choices=['detr-resnet-50', 'detr-resnet-101'],
help='Name of the DETR model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
_snake_case : Any = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 284
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected', [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
({'num_shards': 10, 'max_num_jobs': 10}, [range(lowerCAmelCase_, i + 1 ) for i in range(10 )]),
({'num_shards': 1, 'max_num_jobs': 10}, [range(1 )]),
({'num_shards': 10, 'max_num_jobs': 3}, [range(0, 4 ), range(4, 7 ), range(7, 10 )]),
({'num_shards': 3, 'max_num_jobs': 10}, [range(0, 1 ), range(1, 2 ), range(2, 3 )]),
], )
def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : Any ):
__lowerCAmelCase = _distribute_shards(**lowerCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
'gen_kwargs, max_num_jobs, expected', [
({'foo': 0}, 10, [{'foo': 0}]),
({'shards': [0, 1, 2, 3]}, 1, [{'shards': [0, 1, 2, 3]}]),
({'shards': [0, 1, 2, 3]}, 4, [{'shards': [0]}, {'shards': [1]}, {'shards': [2]}, {'shards': [3]}]),
({'shards': [0, 1]}, 4, [{'shards': [0]}, {'shards': [1]}]),
({'shards': [0, 1, 2, 3]}, 2, [{'shards': [0, 1]}, {'shards': [2, 3]}]),
], )
def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : List[str], lowerCAmelCase_ : Optional[int] ):
__lowerCAmelCase = _split_gen_kwargs(lowerCAmelCase_, lowerCAmelCase_ )
assert out == expected
@pytest.mark.parametrize(
'gen_kwargs, expected', [
({'foo': 0}, 1),
({'shards': [0]}, 1),
({'shards': [0, 1, 2, 3]}, 4),
({'shards': [0, 1, 2, 3], 'foo': 0}, 4),
({'shards': [0, 1, 2, 3], 'other': (0, 1)}, 4),
({'shards': [0, 1, 2, 3], 'shards2': [0, 1]}, RuntimeError),
], )
def a_ ( lowerCAmelCase_ : int, lowerCAmelCase_ : Any ):
if expected is RuntimeError:
with pytest.raises(lowerCAmelCase_ ):
_number_of_shards_in_gen_kwargs(lowerCAmelCase_ )
else:
__lowerCAmelCase = _number_of_shards_in_gen_kwargs(lowerCAmelCase_ )
assert out == expected
| 284
| 1
|
def a( A : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
a = []
for temp in range(int(A ) ):
series.append(f'''1/{temp + 1}''' if series else "1" )
return series
if __name__ == "__main__":
_lowercase: str = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| 71
|
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Optional[Any] = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n"
_lowercase: Union[str, Any] = "\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\n(https://arxiv.org/abs/2107.03374).\n"
_lowercase: Union[str, Any] = "\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric(\"code_eval\")\n >>> test_cases = [\"assert add(2,3)==5\"]\n >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {'pass@1': 0.5, 'pass@2': 1.0}\n"
_lowercase: List[Any] = "\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\n\n################################################################################\\n"
_lowercase: List[Any] = "The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Value("string" ),
} ) , homepage="https://github.com/openai/human-eval" , codebase_urls=["https://github.com/openai/human-eval"] , reference_urls=["https://github.com/openai/human-eval"] , license=_LICENSE , )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=[1, 10, 100] , lowerCamelCase_=4 , lowerCamelCase_=3.0 ):
"""simple docstring"""
if os.getenv("HF_ALLOW_CODE_EVAL" , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError("This metric is currently not supported on Windows." )
with ThreadPoolExecutor(max_workers=lowerCamelCase_ ) as executor:
a = []
a = Counter()
a = 0
a = defaultdict(lowerCamelCase_ )
for task_id, (candidates, test_case) in enumerate(zip(lowerCamelCase_ , lowerCamelCase_ ) ):
for candidate in candidates:
a = candidate + "\n" + test_case
a = (test_program, timeout, task_id, completion_id[task_id])
a = executor.submit(lowerCamelCase_ , *lowerCamelCase_ )
futures.append(lowerCamelCase_ )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(lowerCamelCase_ ):
a = future.result()
results[result["task_id"]].append((result["completion_id"], result) )
a , a = [], []
for result in results.values():
result.sort()
a = [r[1]["passed"] for r in result]
total.append(len(lowerCamelCase_ ) )
correct.append(sum(lowerCamelCase_ ) )
a = np.array(lowerCamelCase_ )
a = np.array(lowerCamelCase_ )
a = k
a = {F'''pass@{k}''': estimate_pass_at_k(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def a( A : Optional[Any] , A : str , A : Dict ) -> Optional[int]:
"""simple docstring"""
def estimator(A : int , A : int , A : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(A , A ):
a = itertools.repeat(A , len(A ) )
else:
assert len(A ) == len(A )
a = iter(A )
return np.array([estimator(int(A ) , int(A ) , A ) for n, c in zip(A , A )] )
| 71
| 1
|
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
UpperCAmelCase__ : int = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'
UpperCAmelCase__ : List[str] = '\nArgs:\n predictions (`List[ndarray]`):\n List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n references (`List[ndarray]`):\n List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n num_labels (`int`):\n Number of classes (categories).\n ignore_index (`int`):\n Index that will be ignored during evaluation.\n nan_to_num (`int`, *optional*):\n If specified, NaN values will be replaced by the number defined by the user.\n label_map (`dict`, *optional*):\n If specified, dictionary mapping old label indices to new label indices.\n reduce_labels (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n `Dict[str, float | ndarray]` comprising various elements:\n - *mean_iou* (`float`):\n Mean Intersection-over-Union (IoU averaged over all categories).\n - *mean_accuracy* (`float`):\n Mean accuracy (averaged over all categories).\n - *overall_accuracy* (`float`):\n Overall accuracy on all images.\n - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n Per category accuracy.\n - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n Per category IoU.\n\nExamples:\n\n >>> import numpy as np\n\n >>> mean_iou = datasets.load_metric("mean_iou")\n\n >>> # suppose one has 3 different segmentation maps predicted\n >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n >>> predicted = [predicted_1, predicted_2, predicted_3]\n >>> ground_truth = [actual_1, actual_2, actual_3]\n\n >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0. , 0. , 0.375, 0.4 , 0.5 , 0. , 0.5 , 1. , 1. , 1. ]), \'per_category_accuracy\': array([0. , 0. , 0.75 , 0.66666667, 1. , 0. , 0.5 , 1. , 1. , 1. ])}\n'
UpperCAmelCase__ : Union[str, Any] = '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = None ,_snake_case = False ,):
if label_map is not None:
for old_id, new_id in label_map.items():
SCREAMING_SNAKE_CASE__ : Dict = new_id
# turn into Numpy arrays
SCREAMING_SNAKE_CASE__ : Dict = np.array(_snake_case )
SCREAMING_SNAKE_CASE__ : List[Any] = np.array(_snake_case )
if reduce_labels:
SCREAMING_SNAKE_CASE__ : Dict = 255
SCREAMING_SNAKE_CASE__ : Dict = label - 1
SCREAMING_SNAKE_CASE__ : int = 255
SCREAMING_SNAKE_CASE__ : Optional[int] = label != ignore_index
SCREAMING_SNAKE_CASE__ : Dict = np.not_equal(_snake_case ,_snake_case )
SCREAMING_SNAKE_CASE__ : Optional[int] = pred_label[mask]
SCREAMING_SNAKE_CASE__ : Any = np.array(_snake_case )[mask]
SCREAMING_SNAKE_CASE__ : Tuple = pred_label[pred_label == label]
SCREAMING_SNAKE_CASE__ : str = np.histogram(_snake_case ,bins=_snake_case ,range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE__ : Any = np.histogram(_snake_case ,bins=_snake_case ,range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE__ : List[str] = np.histogram(_snake_case ,bins=_snake_case ,range=(0, num_labels - 1) )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = area_pred_label + area_label - area_intersect
return area_intersect, area_union, area_pred_label, area_label
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = None ,_snake_case = False ,):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.zeros((num_labels,) ,dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : List[Any] = np.zeros((num_labels,) ,dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.zeros((num_labels,) ,dtype=np.floataa )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.zeros((num_labels,) ,dtype=np.floataa )
for result, gt_seg_map in zip(_snake_case ,_snake_case ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = intersect_and_union(
_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case )
total_area_intersect += area_intersect
total_area_union += area_union
total_area_pred_label += area_pred_label
total_area_label += area_label
return total_area_intersect, total_area_union, total_area_pred_label, total_area_label
def lowercase_ ( _snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case = None ,_snake_case = None ,_snake_case = False ,):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = total_intersect_and_union(
_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case ,_snake_case )
# compute metrics
SCREAMING_SNAKE_CASE__ : Optional[Any] = {}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = total_area_intersect.sum() / total_area_label.sum()
SCREAMING_SNAKE_CASE__ : Dict = total_area_intersect / total_area_union
SCREAMING_SNAKE_CASE__ : Optional[Any] = total_area_intersect / total_area_label
SCREAMING_SNAKE_CASE__ : int = np.nanmean(_snake_case )
SCREAMING_SNAKE_CASE__ : Dict = np.nanmean(_snake_case )
SCREAMING_SNAKE_CASE__ : List[str] = all_acc
SCREAMING_SNAKE_CASE__ : List[Any] = iou
SCREAMING_SNAKE_CASE__ : Tuple = acc
if nan_to_num is not None:
SCREAMING_SNAKE_CASE__ : Any = {metric: np.nan_to_num(_snake_case ,nan=_snake_case ) for metric, metric_value in metrics.items()}
return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ (datasets.Metric ):
"""simple docstring"""
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
# 1st Seq - height dim, 2nd - width dim
{
"""predictions""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16""" ) ) ),
"""references""": datasets.Sequence(datasets.Sequence(datasets.Value("""uint16""" ) ) ),
} ) , reference_urls=[
"""https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"""
] , )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = mean_iou(
results=SCREAMING_SNAKE_CASE__ , gt_seg_maps=SCREAMING_SNAKE_CASE__ , num_labels=SCREAMING_SNAKE_CASE__ , ignore_index=SCREAMING_SNAKE_CASE__ , nan_to_num=SCREAMING_SNAKE_CASE__ , label_map=SCREAMING_SNAKE_CASE__ , reduce_labels=SCREAMING_SNAKE_CASE__ , )
return iou_result
| 25
|
"""simple docstring"""
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def snake_case_ ( ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : int = 9, 14 # noqa: F841
_lowerCamelCase : List[Any] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
_lowerCamelCase : Any = defaultdict(A_ )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
_lowerCamelCase : List[str] = mst(A_ )
_lowerCamelCase : Optional[int] = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
_lowerCamelCase : int = tuple(answer[:2] )
_lowerCamelCase : int = tuple(edge[::-1] )
assert edge in result or reverse in result
| 72
| 0
|
'''simple docstring'''
from copy import deepcopy
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : List[str] ,_a : Tuple = None ,_a : Any = None ):
'''simple docstring'''
if arr is None and size is not None:
_a : Dict = size
_a : int = [0] * size
elif arr is not None:
self.init(lowercase_ )
else:
raise ValueError('Either arr or size must be specified' )
def __lowercase ( self : int ,_a : Optional[int] ):
'''simple docstring'''
_a : Union[str, Any] = len(lowercase_ )
_a : List[Any] = deepcopy(lowercase_ )
for i in range(1 ,self.size ):
_a : Tuple = self.next_(lowercase_ )
if j < self.size:
self.tree[j] += self.tree[i]
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
_a : List[str] = self.tree[:]
for i in range(self.size - 1 ,0 ,-1 ):
_a : Optional[Any] = self.next_(lowercase_ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def __lowercase ( _a : Dict ):
'''simple docstring'''
return index + (index & (-index))
@staticmethod
def __lowercase ( _a : Dict ):
'''simple docstring'''
return index - (index & (-index))
def __lowercase ( self : Union[str, Any] ,_a : Optional[int] ,_a : Union[str, Any] ):
'''simple docstring'''
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
_a : Tuple = self.next_(lowercase_ )
def __lowercase ( self : int ,_a : Optional[int] ,_a : Union[str, Any] ):
'''simple docstring'''
self.add(lowercase_ ,value - self.get(lowercase_ ) )
def __lowercase ( self : List[str] ,_a : int ):
'''simple docstring'''
if right == 0:
return 0
_a : int = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
_a : List[Any] = self.prev(lowercase_ )
return result
def __lowercase ( self : Optional[int] ,_a : Dict ,_a : List[str] ):
'''simple docstring'''
return self.prefix(lowercase_ ) - self.prefix(lowercase_ )
def __lowercase ( self : Union[str, Any] ,_a : Optional[int] ):
'''simple docstring'''
return self.query(lowercase_ ,index + 1 )
def __lowercase ( self : Tuple ,_a : Any ):
'''simple docstring'''
value -= self.tree[0]
if value < 0:
return -1
_a : str = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
_a : Tuple = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 363
|
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
__lowerCAmelCase = """docs/source/en/_toctree.yml"""
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
_a : Any = defaultdict(__a )
for doc in model_doc:
counts[doc["local"]] += 1
_a : List[str] = [key for key, value in counts.items() if value > 1]
_a : str = []
for duplicate_key in duplicates:
_a : Union[str, Any] = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key} )
if len(__a ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1] )
# Sort
return sorted(__a , key=lambda __a : s["title"].lower() )
def UpperCAmelCase_ (__a : Optional[int]=False ):
"""simple docstring"""
with open(__a , encoding='utf-8' ) as f:
_a : Tuple = yaml.safe_load(f.read() )
# Get to the API doc
_a : Union[str, Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_a : Union[str, Any] = content[api_idx]['sections']
# Then to the model doc
_a : List[str] = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
_a : List[str] = api_doc[model_idx]['sections']
_a : List[Any] = [(idx, section) for idx, section in enumerate(__a ) if 'sections' in section]
_a : Tuple = False
for idx, modality_doc in modalities_docs:
_a : List[Any] = modality_doc['sections']
_a : Any = clean_model_doc_toc(__a )
if old_modality_doc != new_modality_doc:
_a : Union[str, Any] = True
if overwrite:
_a : str = new_modality_doc
if diff:
if overwrite:
_a : Dict = model_doc
_a : Dict = api_doc
with open(__a , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(__a , allow_unicode=__a ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` 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_model_doc(args.fix_and_overwrite)
| 5
| 0
|
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