code: '''simple docstring'''
  
  from typing import List, Optional, Tuple, Union
  
  import torch
  
  from ...utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  
  
  
  
  
  class  UpperCAmelCase (       A_):
  
   def __init__( self :	Dict,					a_ :	str,					a_ :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().__init__()
      self.register_modules(unet=a_,					scheduler=a_					)
  
   @torch.no_grad()
   def __call__( self :	Tuple,					a_ :	int = 1,					a_ :	Optional[Union[torch.Generator, List[torch.Generator]]] = None,					a_ :	int = 1000,					a_ :	Optional[str] = "pil",					a_ :	bool = True,					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if isinstance(self.unet.config.sample_size,					a_					):
         UpperCamelCase__ 						=						(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
      else:
         UpperCamelCase__ 						=						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
  
      if self.device.type == "mps":
         # randn does not work reproducibly on mps
         UpperCamelCase__ 						=						randn_tensor(a_,					generator=a_					)
         UpperCamelCase__ 						=						image.to(self.device					)
      else:
         UpperCamelCase__ 						=						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
         UpperCamelCase__ 						=						self.unet(a_,					a_					).sample
  
         # 2. compute previous image: x_t -> x_t-1
         UpperCamelCase__ 						=						self.scheduler.step(a_,					a_,					a_,					generator=a_					).prev_sample
  
      UpperCamelCase__ 						=						(image / 2 + 0.5).clamp(0,					1					)
      UpperCamelCase__ 						=						image.cpu().permute(0,					2,					3,					1					).numpy()
      if output_type == "pil":
         UpperCamelCase__ 						=						self.numpy_to_pil(a_					)
  
      if not return_dict:
         return (image,)
  
      return ImagePipelineOutput(images=a_					)
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowercase:		int							=     {
      "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
  }
  
  try:
         if not is_sentencepiece_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		str							=     ["LlamaTokenizer"]
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		List[Any]							=     ["LlamaTokenizerFast"]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		Union[str, Any]							=     [
             "OpenLlamaForCausalLM",
             "OpenLlamaModel",
             "OpenLlamaPreTrainedModel",
             "OpenLlamaForSequenceClassification",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
         try:
                if not is_sentencepiece_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizerFast
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_open_llama import (
                    OpenLlamaForCausalLM,
                    OpenLlamaForSequenceClassification,
                    OpenLlamaModel,
                    OpenLlamaPreTrainedModel,
                )
  
  
  else:
         import sys
  
         __lowercase:		Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)