

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

		__A :			List[Any]		    =			corpus.__dict__
		corpus_dict_no_vocab.pop("""vocab"""			,a__       )
		__A :			Optional[Any]		    =			pytorch_dump_folder_path + """/""" + CORPUS_NAME
		print(f"""Save dataset to {pytorch_dataset_dump_path}"""       )
		torch.save(a__			,a__       )

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		__A :			Optional[Any]		    =			os.path.abspath(a__       )
		__A :			List[str]		    =			os.path.abspath(a__       )

		print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""       )
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			__A :			str		    =			TransfoXLConfig()
		else:
			__A :			str		    =			TransfoXLConfig.from_json_file(a__       )
		print(f"""Building PyTorch model from configuration: {config}"""       )
		__A :			Union[str, Any]		    =			TransfoXLLMHeadModel(a__       )

		__A :			List[Any]		    =			load_tf_weights_in_transfo_xl(a__			,a__			,a__       )
		# Save pytorch-model
		__A :			int		    =			os.path.join(a__			,a__       )
		__A :			Tuple		    =			os.path.join(a__			,a__       )
		print(f"""Save PyTorch model to {os.path.abspath(a__       )}"""       )
		torch.save(model.state_dict()			,a__       )
		print(f"""Save configuration file to {os.path.abspath(a__       )}"""       )
		with open(a__			,"""w"""			,encoding="""utf-8"""       ) as f:
			f.write(config.to_json_string()       )


if __name__ == "__main__":
			UpperCAmelCase_							:					Optional[int]		 =   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.''',
			)
			UpperCAmelCase_							:					str		 =   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'SEWForCTC',
		    'SEWForSequenceClassification',
		    'SEWModel',
		    'SEWPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




