


"""simple docstring"""
import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__SCREAMING_SNAKE_CASE							:      Optional[int]						=      data_utils.TransfoXLTokenizer
__SCREAMING_SNAKE_CASE							:      Optional[int]						=      data_utils.TransfoXLCorpus
__SCREAMING_SNAKE_CASE							:      Tuple						=      data_utils
__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      data_utils







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							if transfo_xl_dataset_file:
														# Convert a pre-processed corpus (see original TensorFlow repo)
														with open(_SCREAMING_SNAKE_CASE			,	"""rb"""			) as fp:
																					snake_case_          =						pickle.load(_SCREAMING_SNAKE_CASE			,	encoding="""latin1"""			)
														# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
														snake_case_          =						pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
														print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""			)
														snake_case_          =						corpus.vocab.__dict__
														torch.save(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

														snake_case_          =						corpus.__dict__
														corpus_dict_no_vocab.pop("""vocab"""			,	_SCREAMING_SNAKE_CASE			)
														snake_case_          =						pytorch_dump_folder_path + """/""" + CORPUS_NAME
														print(f"""Save dataset to {pytorch_dataset_dump_path}"""			)
														torch.save(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							if tf_checkpoint_path:
														# Convert a pre-trained TensorFlow model
														snake_case_          =						os.path.abspath(_SCREAMING_SNAKE_CASE			)
														snake_case_          =						os.path.abspath(_SCREAMING_SNAKE_CASE			)

														print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""			)
														# Initialise PyTorch model
														if transfo_xl_config_file == "":
																					snake_case_          =						TransfoXLConfig()
														else:
																					snake_case_          =						TransfoXLConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
														print(f"""Building PyTorch model from configuration: {config}"""			)
														snake_case_          =						TransfoXLLMHeadModel(_SCREAMING_SNAKE_CASE			)

														snake_case_          =						load_tf_weights_in_transfo_xl(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
														# Save pytorch-model
														snake_case_          =						os.path.join(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
														snake_case_          =						os.path.join(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
														print(f"""Save PyTorch model to {os.path.abspath(_SCREAMING_SNAKE_CASE			)}"""			)
														torch.save(model.state_dict()			,	_SCREAMING_SNAKE_CASE			)
														print(f"""Save configuration file to {os.path.abspath(_SCREAMING_SNAKE_CASE			)}"""			)
														with open(_SCREAMING_SNAKE_CASE			,	"""w"""			,	encoding="""utf-8"""			) as f:
																					f.write(config.to_json_string()			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      List[str]						=      argparse.ArgumentParser()
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=str,
				    required=True,
				    help='Path to the folder to store the PyTorch model or dataset/vocab.',
				)
				parser.add_argument(
				    '--tf_checkpoint_path',
				    default='',
				    type=str,
				    help='An optional path to a TensorFlow checkpoint path to be converted.',
				)
				parser.add_argument(
				    '--transfo_xl_config_file',
				    default='',
				    type=str,
				    help=(
				        'An optional config json file corresponding to the pre-trained BERT model. \n'
				        'This specifies the model architecture.'
				    ),
				)
				parser.add_argument(
				    '--transfo_xl_dataset_file',
				    default='',
				    type=str,
				    help='An optional dataset file to be converted in a vocabulary.',
				)
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      parser.parse_args()
				convert_transfo_xl_checkpoint_to_pytorch(
				    args.tf_checkpoint_path,
				    args.transfo_xl_config_file,
				    args.pytorch_dump_folder_path,
				    args.transfo_xl_dataset_file,
				)







"""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


__SCREAMING_SNAKE_CASE							:      Dict						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      Tuple						=      {
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[Any]							 =      """mobilenet_v1"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=3						,    UpperCAmelCase_							: List[Any]=224						,    UpperCAmelCase_							: List[Any]=1.0						,    UpperCAmelCase_							: Any=8						,    UpperCAmelCase_							: int="relu6"						,    UpperCAmelCase_							: str=True						,    UpperCAmelCase_							: List[str]=0.999						,    UpperCAmelCase_							: Optional[Any]=0.02						,    UpperCAmelCase_							: List[Any]=0.001						,    **UpperCAmelCase_							: Any						,    )   ->Union[str, Any]:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_    )

														if depth_multiplier <= 0:
																					raise ValueError("""depth_multiplier must be greater than zero."""    )

														snake_case_          =						num_channels
														snake_case_          =						image_size
														snake_case_          =						depth_multiplier
														snake_case_          =						min_depth
														snake_case_          =						hidden_act
														snake_case_          =						tf_padding
														snake_case_          =						classifier_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						layer_norm_eps





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						int							 =      version.parse("""1.11""")



							@property
							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														return OrderedDict([("""pixel_values""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->Mapping[str, Mapping[int, str]]:



														"""simple docstring"""



														if self.task == "image-classification":
																					return OrderedDict([("""logits""", {0: """batch"""})]    )
														else:
																					return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]    )



							@property
							def 						lowerCAmelCase    (			self							: int    )   ->float:



														"""simple docstring"""



														return 1E-4



