"""simple docstring"""
import math
import os
import sys





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			str:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			dict[str, str]	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			str   )   ->			None:





						"""simple docstring"""






						lexicon.pop(_UpperCamelCase   )
						snake_case        =     last_match_id

						if math.loga(_UpperCamelCase   ).is_integer():
												for curr_key in lexicon:
																		snake_case        =     '0' + lexicon[curr_key]

						snake_case        =     bin(_UpperCamelCase   )[2:]





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			str:





						"""simple docstring"""






						snake_case        =     {'0': '0', '1': '1'}
						snake_case   ,snake_case        =     '', ''
						snake_case        =     len(_UpperCamelCase   )

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

												snake_case        =     lexicon[curr_string]
												result += last_match_id
												add_key_to_lexicon(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
												index += 1
												snake_case        =     ''

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

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

						return result





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str   )   ->			str:





						"""simple docstring"""






						snake_case        =     os.path.getsize(_UpperCamelCase   )
						snake_case        =     bin(_UpperCamelCase   )[2:]
						snake_case        =     len(_UpperCamelCase   )

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str   )   ->			None:





						"""simple docstring"""






						snake_case        =     8
						try:
												with open(_UpperCamelCase	,     'wb'   ) as opened_file:
																		snake_case        =     [
																		    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 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str   )   ->			None:





						"""simple docstring"""






						snake_case        =     read_file_binary(_UpperCamelCase   )
						snake_case        =     compress_data(_UpperCamelCase   )
						snake_case        =     add_file_length(_UpperCamelCase	,     _UpperCamelCase   )
						write_file_binary(_UpperCamelCase	,     _UpperCamelCase   )


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




"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "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:
					SCREAMING_SNAKE_CASE__   =					[
					    "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

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


