






import math
import sys


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Any						    =     ''''''
  try:
    with open(_UpperCamelCase    ,  "rb"					) as binary_file:
      SCREAMING_SNAKE_CASE							:   Dict						    =     binary_file.read()
    for dat in data:
      SCREAMING_SNAKE_CASE							:   List[str]						    =     F'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print("File not accessible"					)
    sys.exit()


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Any						    =     {'''0''': '''0''', '''1''': '''1'''}
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     '''''', ''''''
  SCREAMING_SNAKE_CASE							:   Dict						    =     len(_UpperCamelCase					)

  for i in range(len(_UpperCamelCase					)					):
    curr_string += data_bits[i]
    if curr_string not in lexicon:
      continue

    SCREAMING_SNAKE_CASE							:   Any						    =     lexicon[curr_string]
    result += last_match_id
    SCREAMING_SNAKE_CASE							:   Optional[int]						    =     last_match_id + '''0'''

    if math.loga(_UpperCamelCase					).is_integer():
      SCREAMING_SNAKE_CASE							:   str						    =     {}
      for curr_key in list(_UpperCamelCase					):
        SCREAMING_SNAKE_CASE							:   List[Any]						    =     lexicon.pop(_UpperCamelCase					)
      SCREAMING_SNAKE_CASE							:   Tuple						    =     new_lex

    SCREAMING_SNAKE_CASE							:   List[Any]						    =     last_match_id + '''1'''
    index += 1
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     ''''''
  return result


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     8
  try:
    with open(_UpperCamelCase    ,  "wb"					) as opened_file:
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     [
          to_write[i : i + byte_length]
          for i in range(0    ,  len(_UpperCamelCase					)    ,  _UpperCamelCase					)
      ]

      if len(result_byte_array[-1]					) % byte_length == 0:
        result_byte_array.append("10000000"					)
      else:
        result_byte_array[-1] += "1" + "0" * (
            byte_length - len(result_byte_array[-1]					) - 1
        )

      for elem in result_byte_array[:-1]:
        opened_file.write(int(_UpperCamelCase    ,  2					).to_bytes(1    ,  byteorder="big"					)					)
  except OSError:
    print("File not accessible"					)
    sys.exit()


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Any						    =     0
  for letter in data_bits:
    if letter == "1":
      break
    counter += 1

  SCREAMING_SNAKE_CASE							:   List[str]						    =     data_bits[counter:]
  SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     data_bits[counter + 1 :]
  return data_bits


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Tuple						    =     read_file_binary(_UpperCamelCase					)
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     remove_prefix(_UpperCamelCase					)
  SCREAMING_SNAKE_CASE							:   Dict						    =     decompress_data(_UpperCamelCase					)
  write_file_binary(_UpperCamelCase    ,  _UpperCamelCase					)


if __name__ == "__main__":
   compress(sys.argv[1], sys.argv[2])








"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  BigBirdConfig.from_json_file(_UpperCamelCase    )
  print(f'''Building PyTorch model from configuration: {config}'''    )

  if is_trivia_qa:
    _lowercase:      Union[str, Any]   			=  BigBirdForQuestionAnswering(_UpperCamelCase    )
  else:
    _lowercase:      Dict   			=  BigBirdForPreTraining(_UpperCamelCase    )

  # Load weights from tf checkpoint
  load_tf_weights_in_big_bird(_UpperCamelCase			,					_UpperCamelCase			,					is_trivia_qa=_UpperCamelCase    )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--big_bird_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
  )
  A__ :       Optional[Any]				= parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )
