code: 
  """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,
  )
  
  
  __magic_name__		       =				{
      '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
      '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
  }
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''GPT2TokenizerFast''']
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				[
  			    '''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:
  			__magic_name__		       =				[
  			    '''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:
  			__magic_name__		       =				['''TFGPT2Tokenizer''']
  
  try:
  			if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''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
  
  			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __magic_name__		       =				{}
  
  try:
  			if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizer''']
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizerFast''']
  
  
  if TYPE_CHECKING:
  			try:
  						if not is_sentencepiece_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm import CpmTokenizer
  
  			try:
  						if not is_tokenizers_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm_fast import CpmTokenizerFast
  
  else:
  			import sys
  
  			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  