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__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Dict		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				Any		):
  													super().__init__()
  													self.register_modules(unet=a_		,				scheduler=a_		)
  
  
  
  
  
  
  
  							@torch.no_grad()
  							def __call__(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				int = 1		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[torch.Generator, List[torch.Generator]]] = None		,				SCREAMING_SNAKE_CASE_  :				int = 1_0_0_0		,				SCREAMING_SNAKE_CASE_  :				Optional[str] = "pil"		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				):
  
  
  
  													if isinstance(self.unet.config.sample_size		,				a_		):
  																			_a     =			(
  																			    batch_size,
  																			    self.unet.config.in_channels,
  																			    self.unet.config.sample_size,
  																			    self.unet.config.sample_size,
  																			)
  													else:
  																			_a     =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
  
  													if self.device.type == "mps":
  																			# randn does not work reproducibly on mps
  																			_a     =			randn_tensor(a_		,				generator=a_		)
  																			_a     =			image.to(self.device		)
  													else:
  																			_a     =			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
  																			_a     =			self.unet(a_		,				a_		).sample
  
  																			# 2. compute previous image: x_t -> x_t-1
  																			_a     =			self.scheduler.step(a_		,				a_		,				a_		,				generator=a_		).prev_sample
  
  													_a     =			(image / 2 + 0.5).clamp(0		,				1		)
  													_a     =			image.cpu().permute(0		,				2		,				3		,				1		).numpy()
  													if output_type == "pil":
  																			_a     =			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_tokenizers_available, is_torch_available
  
  
  lowercase_								=    {
      'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
      'tokenization_codegen': ['CodeGenTokenizer'],
  }
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['CodeGenTokenizerFast']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'CodeGenForCausalLM',
  		    'CodeGenModel',
  		    'CodeGenPreTrainedModel',
  		]
  
  if TYPE_CHECKING:
  		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  		from .tokenization_codegen import CodeGenTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_codegen_fast import CodeGenTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_codegen import (
  				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    CodeGenForCausalLM,
  				    CodeGenModel,
  				    CodeGenPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  