



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_		 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[List, PIL.Image.Image, torch.Tensor]				)  ->     List[Any]:
   warnings.warn(
       '''The preprocess method is deprecated and will be removed in a future version. Please'''
       ''' use VaeImageProcessor.preprocess instead'''	,   _snake_case	,   )
   if isinstance(_snake_case	,   torch.Tensor				):
      return image
   elif isinstance(_snake_case	,   PIL.Image.Image				):
      _A      =       [image]

   if isinstance(image[0]	,   PIL.Image.Image				):
      _A  ,    _A      =       image[0].size
      _A  ,    _A      =       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

      _A      =       [np.array(i.resize((w, h)	,   resample=PIL_INTERPOLATION['''lanczos''']				)				)[None, :] for i in image]
      _A      =       np.concatenate(_snake_case	,   axis=0				)
      _A      =       np.array(_snake_case				).astype(np.floataa				) / 255.0
      _A      =       image.transpose(0	,   3	,   1	,   2				)
      _A      =       2.0 * image - 1.0
      _A      =       torch.from_numpy(_snake_case				)
   elif isinstance(image[0]	,   torch.Tensor				):
      _A      =       torch.cat(_snake_case	,   dim=0				)
   return image

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[List, PIL.Image.Image, torch.Tensor]				)  ->     Optional[Any]:
   if isinstance(_snake_case	,   torch.Tensor				):
      return mask
   elif isinstance(_snake_case	,   PIL.Image.Image				):
      _A      =       [mask]

   if isinstance(mask[0]	,   PIL.Image.Image				):
      _A  ,    _A      =       mask[0].size
      _A  ,    _A      =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      _A      =       [np.array(m.convert('''L'''				).resize((w, h)	,   resample=PIL_INTERPOLATION['''nearest''']				)				)[None, :] for m in mask]
      _A      =       np.concatenate(_snake_case	,   axis=0				)
      _A      =       mask.astype(np.floataa				) / 255.0
      _A      =       0
      _A      =       1
      _A      =       torch.from_numpy(_snake_case				)
   elif isinstance(mask[0]	,   torch.Tensor				):
      _A      =       torch.cat(_snake_case	,   dim=0				)
   return mask


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: UNetaDModel
 a__					: RePaintScheduler

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Any      )   ->	Optional[Any]:
    super().__init__()
    self.register_modules(unet=__lowerCAmelCase			,						scheduler=__lowerCAmelCase      )

 @torch.no_grad()
 def __call__(    self					:					Optional[int]			,						__lowerCAmelCase					:					Union[torch.Tensor, PIL.Image.Image]			,						__lowerCAmelCase					:					Union[torch.Tensor, PIL.Image.Image]			,						__lowerCAmelCase					:					int = 2_50			,						__lowerCAmelCase					:					float = 0.0			,						__lowerCAmelCase					:					int = 10			,						__lowerCAmelCase					:					int = 10			,						__lowerCAmelCase					:					Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						__lowerCAmelCase					:					Optional[str] = "pil"			,						__lowerCAmelCase					:					bool = True			,						)   ->	Union[ImagePipelineOutput, Tuple]:
    _A      =       image

    _A      =       _preprocess_image(__lowerCAmelCase      )
    _A      =       original_image.to(device=self.device			,						dtype=self.unet.dtype      )
    _A      =       _preprocess_mask(__lowerCAmelCase      )
    _A      =       mask_image.to(device=self.device			,						dtype=self.unet.dtype      )

    _A      =       original_image.shape[0]

    # sample gaussian noise to begin the loop
    if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) and len(__lowerCAmelCase      ) != batch_size:
       raise ValueError(
           f'''You have passed a list of generators of length {len(__lowerCAmelCase      )}, but requested an effective batch'''
           f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''      )

    _A      =       original_image.shape
    _A      =       randn_tensor(__lowerCAmelCase			,						generator=__lowerCAmelCase			,						device=self.device			,						dtype=self.unet.dtype      )

    # set step values
    self.scheduler.set_timesteps(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						self.device      )
    _A      =       eta

    _A      =       self.scheduler.timesteps[0] + 1
    _A      =       generator[0] if isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ) else generator
    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
       if t < t_last:
          # predict the noise residual
          _A      =       self.unet(__lowerCAmelCase			,						__lowerCAmelCase      ).sample
          # compute previous image: x_t -> x_t-1
          _A      =       self.scheduler.step(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      ).prev_sample

       else:
          # compute the reverse: x_t-1 -> x_t
          _A      =       self.scheduler.undo_step(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )
       _A      =       t

    _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(__lowerCAmelCase      )

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=__lowerCAmelCase      )






a__												=						'''Input must be a string of 8 numbers plus letter'''
a__												=						'''TRWAGMYFPDXBNJZSQVHLCKE'''



def   __UpperCAmelCase					(      __a :     str      )     ->	bool:

					"""simple docstring"""

					if not isinstance(__a						,__a      ):
										_a      :       List[str]   =						F"""Expected string as input, found {type(__a      ).__name__}"""
										raise TypeError(__a      )

					_a      :       List[Any]   =						spanish_id.replace('''-'''						,''''''      ).upper()
					if len(__a      ) != 9:
										raise ValueError(__a      )

					try:
										_a      :       Any   =						int(spanish_id_clean[0:8]      )
										_a      :       str   =						spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__a      ) from ex

					if letter.isdigit():
										raise ValueError(__a      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



