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,
  )
  
  
  lowercase__      =				{
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
  					if not is_tokenizers_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				["""GPT2TokenizerFast"""]
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				[
  					    """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:
  					lowercase__      =				[
  					    """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:
  					lowercase__      =				["""TFGPT2Tokenizer"""]
  
  try:
  					if not is_flax_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				["""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
  
  					lowercase__      =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from .pipeline_kandinskya_a import KandinskyVaaPipeline
  from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
  from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
  from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
  from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
  from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
  from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline
  