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_     =	data_utils.TransfoXLTokenizer
lowerCAmelCase_     =	data_utils.TransfoXLCorpus
lowerCAmelCase_     =	data_utils
lowerCAmelCase_     =	data_utils



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:




							if transfo_xl_dataset_file:
														# Convert a pre-processed corpus (see original TensorFlow repo)
														with open(_lowerCAmelCase     ,	"""rb"""  ) as fp:
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    pickle.load(_lowerCAmelCase     ,	encoding="""latin1"""  )
														# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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(_lowerCAmelCase     ,	_lowerCAmelCase  )

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    corpus.__dict__
														corpus_dict_no_vocab.pop("""vocab"""     ,	_lowerCAmelCase  )
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    pytorch_dump_folder_path + "/" + CORPUS_NAME
														print(F"""Save dataset to {pytorch_dataset_dump_path}"""  )
														torch.save(_lowerCAmelCase     ,	_lowerCAmelCase  )

							if tf_checkpoint_path:
														# Convert a pre-trained TensorFlow model
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    os.path.abspath(_lowerCAmelCase  )
														_SCREAMING_SNAKE_CASE		:						Any			  =    os.path.abspath(_lowerCAmelCase  )

														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		:						List[Any]			  =    TransfoXLConfig()
														else:
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    TransfoXLConfig.from_json_file(_lowerCAmelCase  )
														print(F"""Building PyTorch model from configuration: {config}"""  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    TransfoXLLMHeadModel(_lowerCAmelCase  )

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


if __name__ == "__main__":
			lowerCAmelCase_     =	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_     =	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 decimal import Decimal, getcontext
from math import ceil, factorial



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:

							if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														raise TypeError("""Undefined for non-integers"""  )
							elif precision < 1:
														raise ValueError("""Undefined for non-natural numbers"""  )

							_SCREAMING_SNAKE_CASE		:						int			  =    precision
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ceil(precision / 14  )
							_SCREAMING_SNAKE_CASE		:						int			  =    426_880 * Decimal(10_005  ).sqrt()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    1
							_SCREAMING_SNAKE_CASE		:						str			  =    13_591_409
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    Decimal(__SCREAMING_SNAKE_CASE  )
							for k in range(1     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    factorial(6 * k  ) // (factorial(3 * k  ) * factorial(__SCREAMING_SNAKE_CASE  ) ** 3)
														linear_term += 545_140_134
														exponential_term *= -262_537_412_640_768_000
														partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
							return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
			lowerCAmelCase_     =	50
			print(F"The first {n} digits of pi is: {pi(n)}")


