code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  lowerCAmelCase					 =	{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''GPTJForCausalLM''',
  	    '''GPTJForQuestionAnswering''',
  	    '''GPTJForSequenceClassification''',
  	    '''GPTJModel''',
  	    '''GPTJPreTrainedModel''',
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''TFGPTJForCausalLM''',
  	    '''TFGPTJForQuestionAnswering''',
  	    '''TFGPTJForSequenceClassification''',
  	    '''TFGPTJModel''',
  	    '''TFGPTJPreTrainedModel''',
  	]
  
  try:
  	if not is_flax_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowerCAmelCase					 =	[
  	    '''FlaxGPTJForCausalLM''',
  	    '''FlaxGPTJModel''',
  	    '''FlaxGPTJPreTrainedModel''',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_gptj import (
  		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    GPTJForCausalLM,
  		    GPTJForQuestionAnswering,
  		    GPTJForSequenceClassification,
  		    GPTJModel,
  		    GPTJPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_gptj import (
  		    TFGPTJForCausalLM,
  		    TFGPTJForQuestionAnswering,
  		    TFGPTJForSequenceClassification,
  		    TFGPTJModel,
  		    TFGPTJPreTrainedModel,
  		)
  
  	try:
  		if not is_flax_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
  	import sys
  
  	lowerCAmelCase					 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  