


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
__lowerCAmelCase	: Tuple  				=data_utils.TransfoXLTokenizer
__lowerCAmelCase	: int  				=data_utils.TransfoXLCorpus
__lowerCAmelCase	: str  				=data_utils
__lowerCAmelCase	: Dict  				=data_utils


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

												__SCREAMING_SNAKE_CASE						:     Tuple					=		corpus.__dict__
												corpus_dict_no_vocab.pop('''vocab'''    ,       lowercase__      )
												__SCREAMING_SNAKE_CASE						:     int					=		pytorch_dump_folder_path + '''/''' + CORPUS_NAME
												print(F'''Save dataset to {pytorch_dataset_dump_path}'''      )
												torch.save(lowercase__    ,       lowercase__      )

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

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

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


if __name__ == "__main__":
						__lowerCAmelCase	: 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.',
						)
						__lowerCAmelCase	: Optional[Any]  				=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


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		0.00
						__SCREAMING_SNAKE_CASE						:     List[str]					=		0
						for resistor in resistors:
												if resistor <= 0:
																		__SCREAMING_SNAKE_CASE						:     Any					=		F'''Resistor at index {index} has a negative or zero value!'''
																		raise ValueError(lowercase__      )
												first_sum += 1 / float(lowercase__      )
												index += 1
						return 1 / first_sum


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Tuple					=		0.00
						__SCREAMING_SNAKE_CASE						:     int					=		0
						for resistor in resistors:
												sum_r += resistor
												if resistor < 0:
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		F'''Resistor at index {index} has a negative value!'''
																		raise ValueError(lowercase__      )
												index += 1
						return sum_r


if __name__ == "__main__":
						import doctest

						doctest.testmod()
