
'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCAmelCase            = {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = [
						    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "GPTJForCausalLM",
						    "GPTJForQuestionAnswering",
						    "GPTJForSequenceClassification",
						    "GPTJModel",
						    "GPTJPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = [
						    "TFGPTJForCausalLM",
						    "TFGPTJForQuestionAnswering",
						    "TFGPTJForSequenceClassification",
						    "TFGPTJModel",
						    "TFGPTJPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase            = [
						    "FlaxGPTJForCausalLM",
						    "FlaxGPTJModel",
						    "FlaxGPTJPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gptj import (
												    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTJForCausalLM,
												    GPTJForQuestionAnswering,
												    GPTJForSequenceClassification,
												    GPTJModel,
												    GPTJPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_gptj import (
												    TFGPTJForCausalLM,
												    TFGPTJForQuestionAnswering,
												    TFGPTJForSequenceClassification,
												    TFGPTJModel,
												    TFGPTJPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
						import sys

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



'''simple docstring'''





import math
from timeit import timeit

def   A			(							A_				:       int							):
						snake_case							:  List[str]			=     0
						snake_case							:  Any			=     abs(A_							)
						while True:
												snake_case							:  List[Any]			=     n // 10
												digits += 1
												if n == 0:
																		break
						return digits

def   A			(							A_				:       int							):
						return 1 if n == 0 else math.floor(math.log(abs(A_							)     ,				10							) + 1							)

def   A			(							A_				:       int							):
						return len(str(abs(A_							)							)							)

def   A			(							):
						from collections.abc import Callable

						def benchmark_a_function(A_				:       Callable     ,				A_				:       int							) -> None:
												snake_case							:  Optional[int]			=     F"""{func.__name__}({value})"""
												snake_case							:  Optional[int]			=     timeit(F"""__main__.{call}"""     ,				setup='''import __main__'''							)
												print(F"""{call}: {func(A_							)} -- {timing} seconds"""							)

						for value in (262144, 1125899906842624, 1267650600228229401496703205376):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(A_     ,				A_							)
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()

