

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


__lowercase     : Optional[Any]       =						logging.get_logger(__name__)  # pylint: disable=invalid-name







def       lowercase					(	__A :					Union[List, PIL.Image.Image, torch.Tensor]		)				->			Any:





    '''simple docstring'''




    warnings.warn(
        """The preprocess method is deprecated and will be removed in a future version. Please"""
        """ use VaeImageProcessor.preprocess instead""" ,    __A ,    )
    if isinstance(__A ,    torch.Tensor		):
        return image
    elif isinstance(__A ,    PIL.Image.Image		):
        snake_case							:		Optional[int]        =		[image]

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

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







def       lowercase					(	__A :					Union[List, PIL.Image.Image, torch.Tensor]		)				->			Optional[int]:





    '''simple docstring'''




    if isinstance(__A ,    torch.Tensor		):
        return mask
    elif isinstance(__A ,    PIL.Image.Image		):
        snake_case							:		int        =		[mask]

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



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      UNetaDModel
  __lowerCamelCase			:      RePaintScheduler





  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      super().__init__()
      self.register_modules(unet=SCREAMING_SNAKE_CASE_   ,scheduler=SCREAMING_SNAKE_CASE_    )





  @torch.no_grad()
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = 250   ,SCREAMING_SNAKE_CASE_ = 0.0   ,SCREAMING_SNAKE_CASE_ = 10   ,SCREAMING_SNAKE_CASE_ = 10   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = "pil"   ,SCREAMING_SNAKE_CASE_ = True   ,):

      '''simple docstring'''
      snake_case							:		Optional[int]        =		image

      snake_case							:		int        =		_preprocess_image(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		original_image.to(device=self.device   ,dtype=self.unet.dtype    )
      snake_case							:		int        =		_preprocess_mask(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		mask_image.to(device=self.device   ,dtype=self.unet.dtype    )

      snake_case							:		Optional[int]        =		original_image.shape[0]

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

      snake_case							:		int        =		original_image.shape
      snake_case							:		str        =		randn_tensor(SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,device=self.device   ,dtype=self.unet.dtype    )

      # set step values
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,self.device    )
      snake_case							:		int        =		eta

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

          else:
              # compute the reverse: x_t-1 -> x_t
              snake_case							:		Optional[Any]        =		self.scheduler.undo_step(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
          snake_case							:		Union[str, Any]        =		t

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

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_    )










'''simple docstring'''




from collections import Counter
from timeit import timeit




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""					,				)   ->bool:
	return sum(c % 2 for c in Counter(input_str.replace(' '					,				''							).lower()							).values()							) < 2




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->bool:
	if len(UpperCAmelCase_							) == 0:
		return True
	snake_case__	       =   input_str.replace(' '					,				''							).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	snake_case__	       =   {}

	for character in lower_case_input_str:
		snake_case__	       =   character_freq_dict.get(UpperCAmelCase_					,				0							) + 1
	snake_case__	       =   0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->None:
	print('\nFor string = '					,				UpperCAmelCase_					,				':'							)
	print(
	    '> can_string_be_rearranged_as_palindrome_counter()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)
	print(
	    '> can_string_be_rearranged_as_palindrome()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)


if __name__ == "__main__":
			a__					:			str          =     input(
			    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
			).strip()
			benchmark(check_str)
			a__					:			List[Any]          =     can_string_be_rearranged_as_palindrome_counter(check_str)
			print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")



