






"""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,
)


UpperCamelCase			=      {
    '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			=      ['GPT2TokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			=      [
		    '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			=      [
		    '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			=      ['TFGPT2Tokenizer']

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














"""simple docstring"""





def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       assert isinstance(snake_case_						,       snake_case_		), F"""The input value of [set_a={set_a}] is not a set"""
       assert isinstance(snake_case_						,       snake_case_		), F"""The input value of [set_b={set_b}] is not a set"""

       return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
  from doctest import testmod

  testmod()






