






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[Any]						      =					data_utils.TransfoXLTokenizer
A_						:						int						      =					data_utils.TransfoXLCorpus
A_						:						Union[str, Any]						      =					data_utils
A_						:						List[Any]						      =					data_utils

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:


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

    UpperCamelCase_:   List[Any]        =							corpus.__dict__
    corpus_dict_no_vocab.pop('vocab'      ,       UpperCAmelCase__    )
    UpperCamelCase_:   Optional[Any]        =							pytorch_dump_folder_path + '/' + CORPUS_NAME
    print(F'''Save dataset to {pytorch_dataset_dump_path}'''    )
    torch.save(UpperCAmelCase__      ,       UpperCAmelCase__    )

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    UpperCamelCase_:   str        =							os.path.abspath(UpperCAmelCase__    )
    UpperCamelCase_:   int        =							os.path.abspath(UpperCAmelCase__    )

    print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      UpperCamelCase_:   Dict        =							TransfoXLConfig()
    else:
      UpperCamelCase_:   int        =							TransfoXLConfig.from_json_file(UpperCAmelCase__    )
    print(F'''Building PyTorch model from configuration: {config}'''    )
    UpperCamelCase_:   Optional[int]        =							TransfoXLLMHeadModel(UpperCAmelCase__    )

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


if __name__ == "__main__":
   A_						:						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.',
   )
   A_						:						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 math import pi, sqrt
def 						snake_case_       (UpperCamelCase			:    float ):





							'''simple docstring'''


							if num <= 0:
														raise ValueError('''math domain error''' )
							if num > 171.5:
														raise OverflowError('''math range error''' )
							elif num - int(UpperCamelCase ) not in (0, 0.5):
														raise NotImplementedError('''num must be an integer or a half-integer''' )
							elif num == 0.5:
														return sqrt(UpperCamelCase )
							else:
														return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def 						snake_case_       ():





							'''simple docstring'''


							assert gamma(0.5 ) == sqrt(UpperCamelCase )
							assert gamma(1 ) == 1.0
							assert gamma(2 ) == 1.0


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				_snake_case : Optional[Any]  				=						1.0
				while num:
								_snake_case : Dict  				=						float(input('Gamma of: '))
								print(F'''gamma({num}) = {gamma(num)}''')
								print('\nEnter 0 to exit...')

