

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
_SCREAMING_SNAKE_CASE  :						Any         =    data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE  :						Tuple         =    data_utils
_SCREAMING_SNAKE_CASE  :						Dict         =    data_utils
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




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

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

          SCREAMING_SNAKE_CASE__					=					load_tf_weights_in_transfo_xl(_A   ,	_A   ,	_A       )
          # Save pytorch-model
          SCREAMING_SNAKE_CASE__					=					os.path.join(_A   ,	_A       )
          SCREAMING_SNAKE_CASE__					=					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__":
       _SCREAMING_SNAKE_CASE  :						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.''',
       )
       _SCREAMING_SNAKE_CASE  :						Optional[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,
       )





import doctest
from collections import deque

import numpy as np






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]      )	->							None:
       SCREAMING_SNAKE_CASE__					=					[2, 1, 2, -1]
       SCREAMING_SNAKE_CASE__					=					[1, 2, 3, 4]







  def    lowercase_	(							self		:	List[str]      )	->							list[float]:
       SCREAMING_SNAKE_CASE__					=					len(self.first_signal      )
       SCREAMING_SNAKE_CASE__					=					len(self.second_signal      )

       SCREAMING_SNAKE_CASE__					=					max(__lowerCamelCase			,     __lowerCamelCase      )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__					=					[[0] * max_length for i in range(__lowerCamelCase      )]

       # fills the smaller signal with zeros to make both signals of same length
       if length_first_signal < length_second_signal:
            self.first_signal += [0] * (max_length - length_first_signal)
       elif length_first_signal > length_second_signal:
            self.second_signal += [0] * (max_length - length_second_signal)
       for i in range(__lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					deque(self.second_signal      )
            rotated_signal.rotate(__lowerCamelCase      )
            for j, item in enumerate(__lowerCamelCase      ):
                 matrix[i][j] += item

        # multiply the matrix with the first signal
       SCREAMING_SNAKE_CASE__					=					np.matmul(np.transpose(__lowerCamelCase      )			,     np.transpose(self.first_signal      )      )

       # rounding-off to two decimal places
       return [round(__lowerCamelCase			,     2      ) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()


