






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
A__			:    Optional[int]         =				data_utils.TransfoXLTokenizer
A__			:    Any         =				data_utils.TransfoXLCorpus
A__			:    str         =				data_utils
A__			:    List[Any]         =				data_utils






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''



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

								lowercase__     				=    corpus.__dict__
								corpus_dict_no_vocab.pop('''vocab'''	,     _SCREAMING_SNAKE_CASE       )
								lowercase__     				=    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
								lowercase__     				=    os.path.abspath(_SCREAMING_SNAKE_CASE       )
								lowercase__     				=    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 == "":
												lowercase__     				=    TransfoXLConfig()
								else:
												lowercase__     				=    TransfoXLConfig.from_json_file(_SCREAMING_SNAKE_CASE       )
								print(F"""Building PyTorch model from configuration: {config}"""       )
								lowercase__     				=    TransfoXLLMHeadModel(_SCREAMING_SNAKE_CASE       )

								lowercase__     				=    load_tf_weights_in_transfo_xl(_SCREAMING_SNAKE_CASE	,     _SCREAMING_SNAKE_CASE	,     _SCREAMING_SNAKE_CASE       )
								# Save pytorch-model
								lowercase__     				=    os.path.join(_SCREAMING_SNAKE_CASE	,     _SCREAMING_SNAKE_CASE       )
								lowercase__     				=    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__":
							A__			:    Tuple         =				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.',
							)
							A__			:    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,
							)







from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    [[] for _ in range(lowerCamelCase						)]
								lowercase__     				=    size




				def __getitem__(  self		:					Optional[Any],						lowerCamelCase		:					int						):

								'''simple docstring'''
								return iter(self._graph[vertex]						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self._size




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								if weight not in (0, 1):
												raise ValueError('''Edge weight must be either 0 or 1.'''						)

								if to_vertex < 0 or to_vertex >= self.size:
												raise ValueError('''Vertex indexes must be in [0; size).'''						)

								self._graph[from_vertex].append(Edge(lowerCamelCase,						lowerCamelCase						)						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    deque([start_vertex]						)
								lowercase__     				=    [None] * self.size
								lowercase__     				=    0

								while queue:
												lowercase__     				=    queue.popleft()
												lowercase__     				=    distances[current_vertex]
												if current_distance is None:
																continue

												for edge in self[current_vertex]:
																lowercase__     				=    current_distance + edge.weight
																lowercase__     				=    distances[edge.destination_vertex]
																if (
																    isinstance(lowerCamelCase,						lowerCamelCase						)
																    and new_distance >= dest_vertex_distance
																):
																				continue
																lowercase__     				=    new_distance
																if edge.weight == 0:
																				queue.appendleft(edge.destination_vertex						)
																else:
																				queue.append(edge.destination_vertex						)

								if distances[finish_vertex] is None:
												raise ValueError('''No path from start_vertex to finish_vertex.'''						)

								return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




