code: 
  
  
  
  
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  
  
  
  class 	UpperCamelCase_     (     SCREAMING_SNAKE_CASE__       ):
  
  
  
  					def __init__(		self    :    Dict					,			lowerCamelCase    :    Optional[Any]					,			lowerCamelCase    :    Any	):
  							super().__init__()
  							self.register_modules(unet=a_					,			scheduler=a_	)
  
  
  
  
  
  					@torch.no_grad()
  					def __call__(		self    :    List[Any]					,			lowerCamelCase    :    int = 1					,			lowerCamelCase    :    Optional[Union[torch.Generator, List[torch.Generator]]] = None					,			lowerCamelCase    :    int = 10_00					,			lowerCamelCase    :    Optional[str] = "pil"					,			lowerCamelCase    :    bool = True					,			):
  
  
  
  							if isinstance(self.unet.config.sample_size					,			a_	):
  									lowerCamelCase_  :    Optional[int]   	=			(
  									    batch_size,
  									    self.unet.config.in_channels,
  									    self.unet.config.sample_size,
  									    self.unet.config.sample_size,
  									)
  							else:
  									lowerCamelCase_  :    List[str]   	=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
  
  							if self.device.type == "mps":
  									# randn does not work reproducibly on mps
  									lowerCamelCase_  :    Dict   	=			randn_tensor(a_					,			generator=a_	)
  									lowerCamelCase_  :    Optional[int]   	=			image.to(self.device	)
  							else:
  									lowerCamelCase_  :    List[str]   	=			randn_tensor(a_					,			generator=a_					,			device=self.device	)
  
  							# set step values
  							self.scheduler.set_timesteps(a_	)
  
  							for t in self.progress_bar(self.scheduler.timesteps	):
  									# 1. predict noise model_output
  									lowerCamelCase_  :    Union[str, Any]   	=			self.unet(a_					,			a_	).sample
  
  									# 2. compute previous image: x_t -> x_t-1
  									lowerCamelCase_  :    Union[str, Any]   	=			self.scheduler.step(a_					,			a_					,			a_					,			generator=a_	).prev_sample
  
  							lowerCamelCase_  :    Tuple   	=			(image / 2 + 0.5).clamp(0					,			1	)
  							lowerCamelCase_  :    str   	=			image.cpu().permute(0					,			2					,			3					,			1	).numpy()
  							if output_type == "pil":
  									lowerCamelCase_  :    str   	=			self.numpy_to_pil(a_	)
  
  							if not return_dict:
  									return (image,)
  
  							return ImagePipelineOutput(images=a_	)
  
style_context: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _lowercase		:     int 					={
      """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=["""MarianTokenizer"""]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Any 					=[
  		    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
  		    """MarianForCausalLM""",
  		    """MarianModel""",
  		    """MarianMTModel""",
  		    """MarianPreTrainedModel""",
  		]
  
  try:
  		if not is_tf_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Dict 					=["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Optional[Any] 					=["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]
  
  if TYPE_CHECKING:
  		from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_marian import MarianTokenizer
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_marian import (
  				    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    MarianForCausalLM,
  				    MarianModel,
  				    MarianMTModel,
  				    MarianPreTrainedModel,
  				)
  
  		try:
  				if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel
  
  else:
  		import sys
  
  		_lowercase		:     List[str] 					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  