






"""simple docstring"""




import math
import os
import sys

def   snake_case (UpperCAmelCase__  )	->    Optional[Any]:
  UpperCamelCase_: Optional[int]   				=    ''
  try:
    with open(UpperCAmelCase__     ,   'rb'  ) as binary_file:
      UpperCamelCase_: Optional[int]   				=    binary_file.read()
    for dat in data:
      UpperCamelCase_: int   				=    F'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print('File not accessible'  )
    sys.exit()

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    str:
  lexicon.pop(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    last_match_id

  if math.loga(UpperCAmelCase__  ).is_integer():
    for curr_key in lexicon:
      UpperCamelCase_: Tuple   				=    '0' + lexicon[curr_key]

  UpperCamelCase_: Optional[Any]   				=    bin(UpperCAmelCase__  )[2:]

def   snake_case (UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Optional[int]   				=    {'0': '0', '1': '1'}
  UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    '', ''
  UpperCamelCase_: Optional[int]   				=    len(UpperCAmelCase__  )

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

    UpperCamelCase_: List[str]   				=    lexicon[curr_string]
    result += last_match_id
    add_key_to_lexicon(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
    index += 1
    UpperCamelCase_: List[str]   				=    ''

  while curr_string != "" and curr_string not in lexicon:
    curr_string += "0"

  if curr_string != "":
    UpperCamelCase_: int   				=    lexicon[curr_string]
    result += last_match_id

  return result

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Union[str, Any]   				=    os.path.getsize(UpperCAmelCase__  )
  UpperCamelCase_: Optional[Any]   				=    bin(UpperCAmelCase__  )[2:]
  UpperCamelCase_: List[Any]   				=    len(UpperCAmelCase__  )

  return "0" * (length_length - 1) + file_length_binary + compressed

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: Optional[Any]   				=    8
  try:
    with open(UpperCAmelCase__     ,   'wb'  ) as opened_file:
      UpperCamelCase_: str   				=    [
          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:
        opened_file.write(int(UpperCAmelCase__     ,   2  ).to_bytes(1     ,   byteorder='big'  )  )
  except OSError:
    print('File not accessible'  )
    sys.exit()

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Any:
  UpperCamelCase_: List[str]   				=    read_file_binary(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    compress_data(UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    add_file_length(UpperCAmelCase__     ,   UpperCAmelCase__  )
  write_file_binary(UpperCAmelCase__     ,   UpperCAmelCase__  )


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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_		:		Tuple		     =			{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			['FunnelTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			[
       'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FunnelBaseModel',
       'FunnelForMaskedLM',
       'FunnelForMultipleChoice',
       'FunnelForPreTraining',
       'FunnelForQuestionAnswering',
       'FunnelForSequenceClassification',
       'FunnelForTokenClassification',
       'FunnelModel',
       'FunnelPreTrainedModel',
       'load_tf_weights_in_funnel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		str		     =			[
       'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFunnelBaseModel',
       'TFFunnelForMaskedLM',
       'TFFunnelForMultipleChoice',
       'TFFunnelForPreTraining',
       'TFFunnelForQuestionAnswering',
       'TFFunnelForSequenceClassification',
       'TFFunnelForTokenClassification',
       'TFFunnelModel',
       'TFFunnelPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
   from .tokenization_funnel import FunnelTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_funnel_fast import FunnelTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_funnel import (
          FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
          FunnelPreTrainedModel,
          load_tf_weights_in_funnel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_funnel import (
          TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFunnelBaseModel,
          TFFunnelForMaskedLM,
          TFFunnelForMultipleChoice,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForSequenceClassification,
          TFFunnelForTokenClassification,
          TFFunnelModel,
          TFFunnelPreTrainedModel,
      )

else:
   import sys

   A_		:		Any		     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)