






"""simple docstring"""




__UpperCamelCase   :						Optional[Any]							=  {
    """0000""": """0""",
    """0001""": """1""",
    """0010""": """2""",
    """0011""": """3""",
    """0100""": """4""",
    """0101""": """5""",
    """0110""": """6""",
    """0111""": """7""",
    """1000""": """8""",
    """1001""": """9""",
    """1010""": """a""",
    """1011""": """b""",
    """1100""": """c""",
    """1101""": """d""",
    """1110""": """e""",
    """1111""": """f""",
}
def        a_	( _A					)     ->     Optional[int]:







				"""simple docstring"""
				snake_case__													=       str(_A					).strip()

				# Exceptions
				if not binary_str:
								raise ValueError('Empty string was passed to the function'					)
				snake_case__													=       binary_str[0] == '''-'''
				snake_case__													=       binary_str[1:] if is_negative else binary_str
				if not all(char in '01' for char in binary_str					):
								raise ValueError('Non-binary value was passed to the function'					)

				snake_case__													=       (
				    '''0''' * (4 * (divmod(len(_A					) ,				4					)[0] + 1) - len(_A					)) + binary_str
				)

				snake_case__													=       []
				for x in range(0 ,				len(_A					) ,				4					):
								hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]					)
				snake_case__													=       '''0x''' + ''''''.join(_A					)

				return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
							from doctest import testmod

							testmod()












from typing import TYPE_CHECKING

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


__UpperCamelCase   :						int							=  {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Optional[Any]							=  ["""OpenAIGPTTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						List[str]							=  [
							    """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """OpenAIGPTDoubleHeadsModel""",
							    """OpenAIGPTForSequenceClassification""",
							    """OpenAIGPTLMHeadModel""",
							    """OpenAIGPTModel""",
							    """OpenAIGPTPreTrainedModel""",
							    """load_tf_weights_in_openai_gpt""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Dict							=  [
							    """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFOpenAIGPTDoubleHeadsModel""",
							    """TFOpenAIGPTForSequenceClassification""",
							    """TFOpenAIGPTLMHeadModel""",
							    """TFOpenAIGPTMainLayer""",
							    """TFOpenAIGPTModel""",
							    """TFOpenAIGPTPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
							from .tokenization_openai import OpenAIGPTTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_openai_fast import OpenAIGPTTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_openai import (
														    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    OpenAIGPTDoubleHeadsModel,
														    OpenAIGPTForSequenceClassification,
														    OpenAIGPTLMHeadModel,
														    OpenAIGPTModel,
														    OpenAIGPTPreTrainedModel,
														    load_tf_weights_in_openai_gpt,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_openai import (
														    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFOpenAIGPTDoubleHeadsModel,
														    TFOpenAIGPTForSequenceClassification,
														    TFOpenAIGPTLMHeadModel,
														    TFOpenAIGPTMainLayer,
														    TFOpenAIGPTModel,
														    TFOpenAIGPTPreTrainedModel,
														)

else:
							import sys

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




