code: from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available
  
  
  lowercase__    :       Dict						=			{
      '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowercase__    :       int						=			['''XLMProphetNetTokenizer''']
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	lowercase__    :       List[Any]						=			[
  	    '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
  	    '''XLMProphetNetDecoder''',
  	    '''XLMProphetNetEncoder''',
  	    '''XLMProphetNetForCausalLM''',
  	    '''XLMProphetNetForConditionalGeneration''',
  	    '''XLMProphetNetModel''',
  	    '''XLMProphetNetPreTrainedModel''',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_xlm_prophetnet import (
  		    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    XLMProphetNetDecoder,
  		    XLMProphetNetEncoder,
  		    XLMProphetNetForCausalLM,
  		    XLMProphetNetForConditionalGeneration,
  		    XLMProphetNetModel,
  		    XLMProphetNetPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	lowercase__    :       List[str]						=			_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
  
  
  
  