




import math
import os
import sys




def 					lowerCamelCase__       (						A__       :					str	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					dict[str, str]  ,			A__       :					str  ,			A__       :					int  ,			A__       :					str	):





       '''simple docstring'''



       lexicon.pop(A__	)
       __lowerCamelCase   =						last_match_id

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

       __lowerCamelCase   =						bin(A__	)[2:]




def 					lowerCamelCase__       (						A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						{"""0""": """0""", """1""": """1"""}
       __lowerCamelCase,   __lowerCamelCase   =						"""""", """"""
       __lowerCamelCase   =						len(A__	)

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

              __lowerCamelCase   =						lexicon[curr_string]
              result += last_match_id
              add_key_to_lexicon(A__  ,			A__  ,			A__  ,			A__	)
              index += 1
              __lowerCamelCase   =						""""""

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

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

       return result




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						os.path.getsize(A__	)
       __lowerCamelCase   =						bin(A__	)[2:]
       __lowerCamelCase   =						len(A__	)

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




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						8
       try:
              with open(A__  ,			"""wb"""	) as opened_file:
                     __lowerCamelCase   =						[
                         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__       (						A__       :					str  ,			A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						read_file_binary(A__	)
       __lowerCamelCase   =						compress_data(A__	)
       __lowerCamelCase   =						add_file_length(A__  ,			A__	)
       write_file_binary(A__  ,			A__	)


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





"""simple docstring"""


from typing import TYPE_CHECKING

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


A     :   Tuple			    =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Dict			    =			["BlenderbotTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BlenderbotForCausalLM",
	    "BlenderbotForConditionalGeneration",
	    "BlenderbotModel",
	    "BlenderbotPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "TFBlenderbotForConditionalGeneration",
	    "TFBlenderbotModel",
	    "TFBlenderbotPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxBlenderbotForConditionalGeneration",
	    "FlaxBlenderbotModel",
	    "FlaxBlenderbotPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	A     :   Optional[Any]			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




