

from typing import TYPE_CHECKING

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


_UpperCamelCase     =    {
    '''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     =    ['''OpenAIGPTTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase     =    [
        '''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     =    [
        '''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     =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






def 		UpperCamelCase_(    snake_case__:						int     ) ->				int:
 if not isinstance(snake_case__						,					snake_case__     ):
  UpperCAmelCase__           =       f"Input value of [number={number}] must be an integer"
  raise TypeError(snake_case__     )
 if number < 1:
  raise ValueError('Input must be a positive integer'     )
 return number * (2 * number - 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



