






import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate



class 						lowerCamelCase   (_SCREAMING_SNAKE_CASE    ):







  '''simple docstring'''



  _snake_case					:					Optional[int] 		=						CONFIG_NAME



  @register_to_config
  def __init__(							self       ,	_UpperCamelCase = True       ,	_UpperCamelCase = 8       ,	_UpperCamelCase = "lanczos"       ,	_UpperCamelCase = True       ,	_UpperCamelCase = False       ,	)			->							Union[str, Any]:
       super().__init__()



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							PIL.Image.Image:
       if images.ndim == 3:
            UpperCAmelCase_					:   Optional[int]   =		images[None, ...]
       UpperCAmelCase_					:   List[Any]   =		(images * 2_5_5).round().astype('uint8'     )
       if images.shape[-1] == 1:
            # special case for grayscale (single channel) images
            UpperCAmelCase_					:   Dict   =		[Image.fromarray(image.squeeze()       ,	mode='L'     ) for image in images]
       else:
            UpperCAmelCase_					:   Dict   =		[Image.fromarray(__A     ) for image in images]

       return pil_images



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							np.ndarray:
       if not isinstance(__A       ,	__A     ):
            UpperCAmelCase_					:   List[str]   =		[images]
       UpperCAmelCase_					:   str   =		[np.array(__A     ).astype(np.floataa     ) / 2_55.0 for image in images]
       UpperCAmelCase_					:   Tuple   =		np.stack(__A       ,	axis=0     )

       return images



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							torch.FloatTensor:
       if images.ndim == 3:
            UpperCAmelCase_					:   List[str]   =		images[..., None]

       UpperCAmelCase_					:   int   =		torch.from_numpy(images.transpose(0       ,	3       ,	1       ,	2     )     )
       return images



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							np.ndarray:
       UpperCAmelCase_					:   Tuple   =		images.cpu().permute(0       ,	2       ,	3       ,	1     ).float().numpy()
       return images



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							str:
       return 2.0 * images - 1.0



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							Optional[Any]:
       return (images / 2 + 0.5).clamp(0       ,	1     )



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							PIL.Image.Image:
       UpperCAmelCase_					:   Optional[int]   =		image.convert('RGB'     )
       return image



  def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None       ,	_UpperCamelCase = None       ,	)			->							PIL.Image.Image:
       if height is None:
            UpperCAmelCase_					:   Optional[Any]   =		image.height
       if width is None:
            UpperCAmelCase_					:   Dict   =		image.width

       UpperCAmelCase_ ,						UpperCAmelCase_					:   Union[str, Any]   =		(
           x - x % self.config.vae_scale_factor for x in (width, height)
       )  # resize to integer multiple of vae_scale_factor
       UpperCAmelCase_					:   Union[str, Any]   =		image.resize((width, height)       ,	resample=PIL_INTERPOLATION[self.config.resample]     )
       return image



  def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = None       ,	_UpperCamelCase = None       ,	)			->							torch.Tensor:
       UpperCAmelCase_					:   int   =		(PIL.Image.Image, np.ndarray, torch.Tensor)
       if isinstance(__A       ,	__A     ):
            UpperCAmelCase_					:   List[Any]   =		[image]
       elif not (isinstance(__A       ,	__A     ) and all(isinstance(__A       ,	__A     ) for i in image     )):
            raise ValueError(
                f"Input is in incorrect format: {[type(__A     ) for i in image]}. Currently, we only support {', '.join(__A     )}"     )

       if isinstance(image[0]       ,	PIL.Image.Image     ):
            if self.config.do_convert_rgb:
                 UpperCAmelCase_					:   List[str]   =		[self.convert_to_rgb(__A     ) for i in image]
            if self.config.do_resize:
                 UpperCAmelCase_					:   int   =		[self.resize(__A       ,	__A       ,	__A     ) for i in image]
            UpperCAmelCase_					:   Tuple   =		self.pil_to_numpy(__A     )  # to np
            UpperCAmelCase_					:   Tuple   =		self.numpy_to_pt(__A     )  # to pt

       elif isinstance(image[0]       ,	np.ndarray     ):
            UpperCAmelCase_					:   Tuple   =		np.concatenate(__A       ,	axis=0     ) if image[0].ndim == 4 else np.stack(__A       ,	axis=0     )
            UpperCAmelCase_					:   Any   =		self.numpy_to_pt(__A     )
            UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_					:   List[str]   =		image.shape
            if self.config.do_resize and (
                height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
            ):
                 raise ValueError(
                     f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
                     f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"     )

       elif isinstance(image[0]       ,	torch.Tensor     ):
            UpperCAmelCase_					:   List[str]   =		torch.cat(__A       ,	axis=0     ) if image[0].ndim == 4 else torch.stack(__A       ,	axis=0     )
            UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_					:   Optional[int]   =		image.shape

            # don't need any preprocess if the image is latents
            if channel == 4:
                 return image

            if self.config.do_resize and (
                height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
            ):
                 raise ValueError(
                     f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
                     f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"     )

        # expected range [0,1], normalize to [-1,1]
       UpperCAmelCase_					:   str   =		self.config.do_normalize
       if image.min() < 0:
            warnings.warn(
                'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
                f"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"       ,	__A       ,	)
            UpperCAmelCase_					:   Union[str, Any]   =		False

       if do_normalize:
            UpperCAmelCase_					:   List[Any]   =		self.normalize(__A     )

       return image



  def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = "pil"       ,	_UpperCamelCase = None       ,	)			->							Any:
       if not isinstance(__A       ,	torch.Tensor     ):
            raise ValueError(
                f"Input for postprocessing is in incorrect format: {type(__A     )}. We only support pytorch tensor"     )
       if output_type not in ["latent", "pt", "np", "pil"]:
            UpperCAmelCase_					:   Union[str, Any]   =		(
                f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                '`pil`, `np`, `pt`, `latent`'
            )
            deprecate('Unsupported output_type'       ,	'1.0.0'       ,	__A       ,	standard_warn=__A     )
            UpperCAmelCase_					:   List[Any]   =		'np'

       if output_type == "latent":
            return image

       if do_denormalize is None:
            UpperCAmelCase_					:   List[Any]   =		[self.config.do_normalize] * image.shape[0]

       UpperCAmelCase_					:   Optional[Any]   =		torch.stack(
           [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

       if output_type == "pt":
            return image

       UpperCAmelCase_					:   str   =		self.pt_to_numpy(__A     )

       if output_type == "np":
            return image

       if output_type == "pil":
            return self.numpy_to_pil(__A     )





class 						lowerCamelCase   (_SCREAMING_SNAKE_CASE    ):







  '''simple docstring'''



  _snake_case					:					Optional[int] 		=						CONFIG_NAME



  @register_to_config
  def __init__(							self       ,	_UpperCamelCase = True       ,	_UpperCamelCase = 8       ,	_UpperCamelCase = "lanczos"       ,	_UpperCamelCase = True       ,	)			->							Optional[Any]:
       super().__init__()



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							Optional[int]:
       if images.ndim == 3:
            UpperCAmelCase_					:   List[Any]   =		images[None, ...]
       UpperCAmelCase_					:   str   =		(images * 2_5_5).round().astype('uint8'     )
       if images.shape[-1] == 1:
            # special case for grayscale (single channel) images
            UpperCAmelCase_					:   List[str]   =		[Image.fromarray(image.squeeze()       ,	mode='L'     ) for image in images]
       else:
            UpperCAmelCase_					:   List[str]   =		[Image.fromarray(image[:, :, :3]     ) for image in images]

       return pil_images



  @staticmethod
  def       __UpperCAmelCase    (							_UpperCamelCase     )			->							Union[str, Any]:
       return image[:, :, 1] * 2**8 + image[:, :, 2]



  def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							Any:
       if images.ndim == 3:
            UpperCAmelCase_					:   List[Any]   =		images[None, ...]
       UpperCAmelCase_					:   int   =		images[:, :, :, 3:]
       if images.shape[-1] == 6:
            UpperCAmelCase_					:   Optional[int]   =		(images_depth * 2_5_5).round().astype('uint8'     )
            UpperCAmelCase_					:   List[str]   =		[
                Image.fromarray(self.rgblike_to_depthmap(__A     )       ,	mode='I;16'     ) for image_depth in images_depth
            ]
       elif images.shape[-1] == 4:
            UpperCAmelCase_					:   List[Any]   =		(images_depth * 6_5_5_3_5.0).astype(np.uintaa     )
            UpperCAmelCase_					:   Optional[Any]   =		[Image.fromarray(__A       ,	mode='I;16'     ) for image_depth in images_depth]
       else:
            raise Exception('Not supported'     )

       return pil_images



  def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = "pil"       ,	_UpperCamelCase = None       ,	)			->							Any:
       if not isinstance(__A       ,	torch.Tensor     ):
            raise ValueError(
                f"Input for postprocessing is in incorrect format: {type(__A     )}. We only support pytorch tensor"     )
       if output_type not in ["latent", "pt", "np", "pil"]:
            UpperCAmelCase_					:   Dict   =		(
                f"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                '`pil`, `np`, `pt`, `latent`'
            )
            deprecate('Unsupported output_type'       ,	'1.0.0'       ,	__A       ,	standard_warn=__A     )
            UpperCAmelCase_					:   Tuple   =		'np'

       if do_denormalize is None:
            UpperCAmelCase_					:   Dict   =		[self.config.do_normalize] * image.shape[0]

       UpperCAmelCase_					:   Optional[Any]   =		torch.stack(
           [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

       UpperCAmelCase_					:   Tuple   =		self.pt_to_numpy(__A     )

       if output_type == "np":
            if image.shape[-1] == 6:
                 UpperCAmelCase_					:   List[str]   =		np.stack([self.rgblike_to_depthmap(im[:, :, 3:]     ) for im in image]       ,	axis=0     )
            else:
                 UpperCAmelCase_					:   Optional[int]   =		image[:, :, :, 3:]
            return image[:, :, :, :3], image_depth

       if output_type == "pil":
            return self.numpy_to_pil(__A     ), self.numpy_to_depth(__A     )
       else:
            raise Exception(f"This type {output_type} is not supported"     )














def   lowercase__   (  __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					return len_board * len_board_column * row + column




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							int  , __snake_case					:							set[int]  , ):


					'''simple docstring'''


					if board[row][column] != word[word_index]:
										return False

					if word_index == len(__snake_case						) - 1:
										return True

					UpperCAmelCase_					:   Dict   =		[(0, 1), (0, -1), (-1, 0), (1, 0)]
					UpperCAmelCase_					:   Union[str, Any]   =		len(__snake_case						)
					UpperCAmelCase_					:   List[Any]   =		len(board[0]						)
					for direction in traverts_directions:
										UpperCAmelCase_					:   Optional[int]   =		row + direction[0]
										UpperCAmelCase_					:   Optional[int]   =		column + direction[1]
										if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
															continue

										UpperCAmelCase_					:   Tuple   =		get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)
										if key in visited_points_set:
															continue

										visited_points_set.add(__snake_case						)
										if exits_word(__snake_case  , __snake_case  , __snake_case  , __snake_case  , word_index + 1  , __snake_case						):
															return True

										visited_points_set.remove(__snake_case						)

					return False




def   lowercase__   (  __snake_case					:							list[list[str]]  , __snake_case					:							str						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		(
					    'The board should be a non empty matrix of single chars strings.'
					)

					UpperCAmelCase_					:   Optional[Any]   =		len(__snake_case						)
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(__snake_case						)

					for row in board:
										if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
															raise ValueError(__snake_case						)

										for item in row:
															if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) != 1:
																				raise ValueError(__snake_case						)

    # Validate word
					if not isinstance(__snake_case  , __snake_case						) or len(__snake_case						) == 0:
										raise ValueError(
										    'The word parameter should be a string of length greater than 0.'						)

					UpperCAmelCase_					:   Any   =		len(board[0]						)
					for i in range(__snake_case						):
										for j in range(__snake_case						):
															if exits_word(
															    __snake_case  , __snake_case  , __snake_case  , __snake_case  , 0  , {get_point_key(__snake_case  , __snake_case  , __snake_case  , __snake_case						)}						):
																				return True

					return False


if __name__ == "__main__":
		import doctest

		doctest.testmod()






