



from typing import TYPE_CHECKING

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


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

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase								=     [
        "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:
    __lowerCamelCase								=     [
        "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

    __lowerCamelCase								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




import math
from timeit import timeit




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  _A          =      0
  _A          =      abs(__snake_case							)
  while True:
    _A          =      n // 10
    digits += 1
    if n == 0:
      break
  return digits




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return 1 if n == 0 else math.floor(math.log(abs(__snake_case							)  ,			10							) + 1							)




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return len(str(abs(__snake_case							)							)							)




def       UpperCAmelCase__	(							)				->	None:
  from collections.abc import Callable

  def benchmark_a_function(__snake_case  ,			__snake_case							) -> None:
    _A          =      F'''{func.__name__}({value})'''
    _A          =      timeit(F'''__main__.{call}'''  ,			setup='''import __main__'''							)
    print(F'''{call}: {func(__snake_case							)} -- {timing} seconds'''							)

  for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(__snake_case  ,			__snake_case							)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()