






"""simple docstring"""


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Optional[Any]    ):

     '''simple docstring'''
     # numbermap keys are the number and its values are lists of indexes sorted
     # in ascending order
     __A		     =					{}
     # indexmap keys are an index and it's values are the number at that index
     __A		     =					{}


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	list | str | range,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,			(range, str)    ):
       __A		     =					list(_lowerCamelCase    )
     elif not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       raise TypeError(
           '''binary_search_delete() only accepts either a list, range or str'''    )

     __A		     =					0
     __A		     =					len(_lowerCamelCase    ) - 1

     while low <= high:
       __A		     =					(low + high) // 2
       if array[mid] == item:
         array.pop(_lowerCamelCase    )
         return array
       elif array[mid] < item:
         __A		     =					mid + 1
       else:
         __A		     =					mid - 1
     raise ValueError(
         '''Either the item is not in the array or the array was unsorted'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	list | str | range,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,			(range, str)    ):
       __A		     =					list(_lowerCamelCase    )
     elif not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       raise TypeError(
           '''binary_search_insert() only accepts either a list, range or str'''    )

     __A		     =					0
     __A		     =					len(_lowerCamelCase    ) - 1

     while low <= high:
       __A		     =					(low + high) // 2
       if array[mid] == index:
         # If the item already exists in the array,
         # insert it after the existing item
         array.insert(mid + 1,			_lowerCamelCase    )
         return array
       elif array[mid] < index:
         __A		     =					mid + 1
       else:
         __A		     =					mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
     array.insert(_lowerCamelCase,			_lowerCamelCase    )
     return array


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	int,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     # Remove previous index
     if index in self.indexmap:
       __A		     =					self.indexmap[index]
       if len(self.numbermap[n]    ) == 1:
         del self.numbermap[n]
       else:
         __A		     =					self.binary_search_delete(self.numbermap[n],			_lowerCamelCase    )

        # Set new index
     __A		     =					number

     # Number not seen before or empty so insert number value
     if number not in self.numbermap:
       __A		     =					[index]

     # Here we need to perform a binary search insertion in order to insert
     # The item in the correct place
     else:
       __A		     =					self.binary_search_insert(
           self.numbermap[number],			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	int    ):

     '''simple docstring'''
     return self.numbermap.get(_lowerCamelCase,			[-1]    )[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()








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  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     42
     SCREAMING_SNAKE_CASE__      =     42





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      super().__init__()

      _lowercase				 =PNDMScheduler.from_config(scheduler.config			)

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





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

       _lowercase				 =self.scheduler.step(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			).prev_sample

      _lowercase				 =(image / 2 + 0.5).clamp(0						,	1			)
      _lowercase				 =image.cpu().permute(0						,	2						,	3						,	1			).numpy()
      if output_type == "pil":
       _lowercase				 =self.numpy_to_pil(UpperCAmelCase			)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCAmelCase			)

