"""simple docstring"""


def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> int:
						lowerCamelCase__ =int((lower_bound + upper_bound) // 2    )
						if array[r] == value:
												return r
						if lower_bound >= upper_bound:
												return -1
						if array[r] < value:
												return binary_search(__lowerCAmelCase       ,   r + 1       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )
						else:
												return binary_search(__lowerCAmelCase       ,   __lowerCAmelCase       ,   r - 1       ,   __lowerCAmelCase    )





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> int:
						lowerCamelCase__ =0
						if matrix[index][0] == value:
												return [index, 0]
						while index < len(__lowerCAmelCase    ) and matrix[index][0] < value:
												lowerCamelCase__ =binary_search(matrix[index]       ,   0       ,   len(matrix[index]    ) - 1       ,   __lowerCAmelCase    )
												if r != -1:
																		return [index, r]
												index += 1
						return [-1, -1]


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__)


