






'''simple docstring'''





def  lowerCamelCase_  (     lowercase__):
 if number != int(lowercase__):
  raise ValueError("factorial() only accepts integral values")
 if number < 0:
  raise ValueError("factorial() not defined for negative values")
 lowerCamelCase__	=  1
 for i in range(1					,  number + 1):
  value *= i
 return value






def  lowerCamelCase_  (     lowercase__):
 if not isinstance(lowercase__					,  lowercase__):
  raise ValueError("factorial() only accepts integral values")
 if n < 0:
  raise ValueError("factorial() not defined for negative values")
 return 1 if n == 0 or n == 1 else n * factorial(n - 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __A				:				Optional[int]					=		int(input("""Enter a positive integer: """).strip() or 0)
 print(F"""factorial{n} is {factorial(n)}""")









'''simple docstring'''





from typing import TYPE_CHECKING

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


__A				:				Union[str, Any]					=		{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""GPT2TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[int]					=		[
     """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPT2DoubleHeadsModel""",
     """GPT2ForQuestionAnswering""",
     """GPT2ForSequenceClassification""",
     """GPT2ForTokenClassification""",
     """GPT2LMHeadModel""",
     """GPT2Model""",
     """GPT2PreTrainedModel""",
     """load_tf_weights_in_gpt2""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Optional[Any]					=		[
     """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFGPT2DoubleHeadsModel""",
     """TFGPT2ForSequenceClassification""",
     """TFGPT2LMHeadModel""",
     """TFGPT2MainLayer""",
     """TFGPT2Model""",
     """TFGPT2PreTrainedModel""",
 ]

try:
 if not is_keras_nlp_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Any					=		["""TFGPT2Tokenizer"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A				:				Tuple					=		["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
 from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
 from .tokenization_gpta import GPTaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_fast import GPTaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpta import (
      GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTaDoubleHeadsModel,
      GPTaForQuestionAnswering,
      GPTaForSequenceClassification,
      GPTaForTokenClassification,
      GPTaLMHeadModel,
      GPTaModel,
      GPTaPreTrainedModel,
      load_tf_weights_in_gpta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gpta import (
      TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFGPTaDoubleHeadsModel,
      TFGPTaForSequenceClassification,
      TFGPTaLMHeadModel,
      TFGPTaMainLayer,
      TFGPTaModel,
      TFGPTaPreTrainedModel,
  )

 try:
  if not is_keras_nlp_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpta_tf import TFGPTaTokenizer

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
 import sys

 __A				:				Tuple					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

