"""simple docstring"""


from __future__ import annotations

from enum import Enum






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       List[Any]  							=	2_4
				A__				:       List[str]  							=	2_1
				A__				:       List[Any]  							=	1_8
				A__				:       Optional[Any]  							=	1_5
				A__				:       List[Any]  							=	1_2
				A__				:       Optional[int]  							=	9
				A__				:       List[Any]  							=	6
				A__				:       int  							=	3
				A__				:       Optional[int]  							=	2
				A__				:       int  							=	1
				A__				:       Optional[int]  							=	-1
				A__				:       Dict  							=	-2
				A__				:       Optional[Any]  							=	-3
				A__				:       str  							=	-6
				A__				:       Union[str, Any]  							=	-9
				A__				:       Optional[Any]  							=	-1_2
				A__				:       int  							=	-1_5
				A__				:       Union[str, Any]  							=	-1_8
				A__				:       Any  							=	-2_1
				A__				:       Union[str, Any]  							=	-2_4






class        __UpperCAmelCase						(     __lowerCAmelCase     ):
				A__				:       Optional[int]  							=	8
				A__				:       Any  							=	7
				A__				:       Any  							=	6
				A__				:       Tuple  							=	5
				A__				:       Optional[Any]  							=	4
				A__				:       str  							=	3
				A__				:       Dict  							=	2
				A__				:       Tuple  							=	1





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   )							-> float:




						'''simple docstring'''



						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =SIUnit[known_prefix.lower()]
						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =SIUnit[unknown_prefix.lower()]
						lowerCamelCase__ =known_amount * (
						    10 ** (known_prefix.value - unknown_prefix.value)
						)
						return unknown_amount





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   )							-> float:




						'''simple docstring'''



						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =BinaryUnit[known_prefix.lower()]
						if isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =BinaryUnit[unknown_prefix.lower()]
						lowerCamelCase__ =known_amount * (
						    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
						)
						return unknown_amount


if __name__ == "__main__":
	import doctest

	doctest.testmod()



"""simple docstring"""


from typing import TYPE_CHECKING

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


a							={
    'configuration_openai': ['OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenAIGPTConfig'],
    'tokenization_openai': ['OpenAIGPTTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=['OpenAIGPTTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							=[
	    '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:
	a							=[
	    '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

	a							=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


