code: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowercase__    :       Optional[Any]						=			{
      '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
      '''tokenization_bart''': ['''BartTokenizer'''],
  }
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowercase__    :       Union[str, Any]						=			['''BartTokenizerFast''']
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowercase__    :       List[str]						=			[
  	    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''BartForCausalLM''',
  	    '''BartForConditionalGeneration''',
  	    '''BartForQuestionAnswering''',
  	    '''BartForSequenceClassification''',
  	    '''BartModel''',
  	    '''BartPretrainedModel''',
  	    '''PretrainedBartModel''',
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowercase__    :       Union[str, Any]						=			[
  	    '''TFBartForConditionalGeneration''',
  	    '''TFBartForSequenceClassification''',
  	    '''TFBartModel''',
  	    '''TFBartPretrainedModel''',
  	]
  
  try:
  	if not is_flax_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowercase__    :       List[str]						=			[
  	    '''FlaxBartDecoderPreTrainedModel''',
  	    '''FlaxBartForCausalLM''',
  	    '''FlaxBartForConditionalGeneration''',
  	    '''FlaxBartForQuestionAnswering''',
  	    '''FlaxBartForSequenceClassification''',
  	    '''FlaxBartModel''',
  	    '''FlaxBartPreTrainedModel''',
  	]
  
  if TYPE_CHECKING:
  	from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  	from .tokenization_bart import BartTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_bart_fast import BartTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_bart import (
  		    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    BartForCausalLM,
  		    BartForConditionalGeneration,
  		    BartForQuestionAnswering,
  		    BartForSequenceClassification,
  		    BartModel,
  		    BartPretrainedModel,
  		    PretrainedBartModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_bart import (
  		    TFBartForConditionalGeneration,
  		    TFBartForSequenceClassification,
  		    TFBartModel,
  		    TFBartPretrainedModel,
  		)
  
  	try:
  		if not is_flax_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_flax_bart import (
  		    FlaxBartDecoderPreTrainedModel,
  		    FlaxBartForCausalLM,
  		    FlaxBartForConditionalGeneration,
  		    FlaxBartForQuestionAnswering,
  		    FlaxBartForSequenceClassification,
  		    FlaxBartModel,
  		    FlaxBartPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	lowercase__    :       Dict						=			_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 ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available
  
  
  
  
  @dataclass
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Union[List[PIL.Image.Image], np.ndarray]
  
  
  if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
  	from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
  	from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
  
  
  
  