


'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 					__magic_name__							(		lowerCAmelCase				):
       UpperCAmelCase						      =4_2
       UpperCAmelCase						      =4_2






       def __init__(			self  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          super().__init__()

          _UpperCAmelCase   :   Tuple							    =PNDMScheduler.from_config(scheduler.config)

          self.register_modules(unet=__UpperCAmelCase  ,							scheduler=__UpperCAmelCase)






       @torch.no_grad()
       def __call__(			self  ,							snake_case = 1  ,							snake_case = 5_0  ,							snake_case = None  ,							snake_case = "pil"  ,							snake_case = True  ,							**snake_case  ,							)						->  Union[ImagePipelineOutput, Tuple]:




          '''simple docstring'''
          _UpperCAmelCase   :   Optional[Any]							    =randn_tensor(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,							generator=__UpperCAmelCase  ,							device=self.device  ,							)

          self.scheduler.set_timesteps(__UpperCAmelCase)
          for t in self.progress_bar(self.scheduler.timesteps):
             _UpperCAmelCase   :   Union[str, Any]							    =self.unet(__UpperCAmelCase  ,							__UpperCAmelCase).sample

             _UpperCAmelCase   :   Any							    =self.scheduler.step(__UpperCAmelCase  ,							__UpperCAmelCase  ,							__UpperCAmelCase).prev_sample

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

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=__UpperCAmelCase)



"""simple docstring"""


from .hash_table import HashTable







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



			def __init__(							self     ,     *__UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     List[str]:
				super().__init__(*__UpperCAmelCase     ,     **__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     Union[str, Any]:
				_lowerCAmelCase								=1
				_lowerCAmelCase								=self.hash_function(key + i * i  )

				while self.values[new_key] is not None and self.values[new_key] != key:
					i += 1
					_lowerCAmelCase								=(
					    self.hash_function(key + i * i  )
					    if not self.balanced_factor() >= self.lim_charge
					    else None
					)

					if new_key is None:
						break

				return new_key



