






import warnings

from .generation import FlaxGenerationMixin
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 warnings.warn(
     "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
     "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."	,				__lowercase	,				)












from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A)	->						Dict:

     """simple docstring"""



     super().__init__()
     self.register_modules(unet=_A							,					scheduler=_A)


 @torch.no_grad()
 def __call__(		self							,					_A = 1							,					_A = None							,					_A = 1000							,					_A = "pil"							,					_A = True							,					)	->						Union[ImagePipelineOutput, Tuple]:

     """simple docstring"""



     if isinstance(self.unet.config.sample_size							,					_A):
         _UpperCAmelCase			: List[str]     					=			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
     else:
         _UpperCAmelCase			: Union[str, Any]     					=			(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			: int     					=			randn_tensor(_A							,					generator=_A)
         _UpperCAmelCase			: Tuple     					=			image.to(self.device)
     else:
         _UpperCAmelCase			: Any     					=			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			: Optional[Any]     					=			self.unet(_A							,					_A).sample

         # 2. compute previous image: x_t -> x_t-1
         _UpperCAmelCase			: Dict     					=			self.scheduler.step(_A							,					_A							,					_A							,					generator=_A).prev_sample

     _UpperCAmelCase			: Optional[int]     					=			(image / 2 + 0.5).clamp(0							,					1)
     _UpperCAmelCase			: int     					=			image.cpu().permute(0							,					2							,					3							,					1).numpy()
     if output_type == "pil":
         _UpperCAmelCase			: str     					=			self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)




