




import math
import os
import sys



def   lowerCAmelCase						(						_lowerCAmelCase							:      Dict		):




 """simple docstring"""







 UpperCAmelCase__							       =  ""
 try:
  with open(A_  ,							"rb"		) as binary_file:
   UpperCAmelCase__							       =  binary_file.read()
  for dat in data:
   UpperCAmelCase__							       =  F'''{dat:08b}'''
   result += curr_byte
  return result
 except OSError:
  print("File not accessible"		)
  sys.exit()



def   lowerCAmelCase						(						_lowerCAmelCase							:      int  ,							_lowerCAmelCase							:      List[str]  ,							_lowerCAmelCase							:      Optional[int]  ,							_lowerCAmelCase							:      Optional[Any]		):




 """simple docstring"""







 lexicon.pop(A_		)
 UpperCAmelCase__							       =  last_match_id

 if math.loga(A_		).is_integer():
  for curr_key in lexicon:
   UpperCAmelCase__							       =  "0" + lexicon[curr_key]

 UpperCAmelCase__							       =  bin(A_		)[2:]



def   lowerCAmelCase						(						_lowerCAmelCase							:      Tuple		):




 """simple docstring"""







 UpperCAmelCase__							       =  {"0": "0", "1": "1"}
 UpperCAmelCase__       ,       UpperCAmelCase__							       =  "", ""
 UpperCAmelCase__							       =  len(A_		)

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

  UpperCAmelCase__							       =  lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(A_  ,							A_  ,							A_  ,							A_		)
  index += 1
  UpperCAmelCase__							       =  ""

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

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

 return result



def   lowerCAmelCase						(						_lowerCAmelCase							:      int  ,							_lowerCAmelCase							:      str		):




 """simple docstring"""







 UpperCAmelCase__							       =  os.path.getsize(A_		)
 UpperCAmelCase__							       =  bin(A_		)[2:]
 UpperCAmelCase__							       =  len(A_		)

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



def   lowerCAmelCase						(						_lowerCAmelCase							:      Optional[Any]  ,							_lowerCAmelCase							:      str		):




 """simple docstring"""







 UpperCAmelCase__							       =  8
 try:
  with open(A_  ,							"wb"		) as opened_file:
   UpperCAmelCase__							       =  [
       to_write[i : i + byte_length]
       for i in range(0  ,							len(A_		)  ,							A_		)
   ]

   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(A_  ,							2		).to_bytes(1  ,							byteorder="big"		)		)
 except OSError:
  print("File not accessible"		)
  sys.exit()



def   lowerCAmelCase						(						_lowerCAmelCase							:      Union[str, Any]  ,							_lowerCAmelCase							:      Any		):




 """simple docstring"""







 UpperCAmelCase__							       =  read_file_binary(A_		)
 UpperCAmelCase__							       =  compress_data(A_		)
 UpperCAmelCase__							       =  add_file_length(A_  ,							A_		)
 write_file_binary(A_  ,							A_		)


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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase			:     List[str]           =   {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     str           =   [
        'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XLMForMultipleChoice',
        'XLMForQuestionAnswering',
        'XLMForQuestionAnsweringSimple',
        'XLMForSequenceClassification',
        'XLMForTokenClassification',
        'XLMModel',
        'XLMPreTrainedModel',
        'XLMWithLMHeadModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Dict           =   [
        'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFXLMForMultipleChoice',
        'TFXLMForQuestionAnsweringSimple',
        'TFXLMForSequenceClassification',
        'TFXLMForTokenClassification',
        'TFXLMMainLayer',
        'TFXLMModel',
        'TFXLMPreTrainedModel',
        'TFXLMWithLMHeadModel',
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

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

