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,
)


__UpperCamelCase   :						int							=  {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						List[Any]							=  ["""GPT2TokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						List[str]							=  [
							    """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:
							__UpperCamelCase   :						List[str]							=  [
							    """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:
							__UpperCamelCase   :						Union[str, Any]							=  ["""TFGPT2Tokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Union[str, Any]							=  ["""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

							__UpperCamelCase   :						List[Any]							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












def        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res
def        a_	( _A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												snake_case__													=       ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




