



'''simple docstring'''
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 							lowercase__			(							_SCREAMING_SNAKE_CASE     ):
	lowercase__						       =    CONFIG_NAME





	@register_to_config
	def __init__(				self      :       Optional[Any]				,lowerCamelCase__      :       Any = True				,lowerCamelCase__      :       Union[str, Any] = 8				,lowerCamelCase__      :       int = "lanczos"				,lowerCamelCase__      :       Optional[int] = True				,lowerCamelCase__      :       Union[str, Any] = False				,):







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





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       List[str]				):







							'''simple docstring'''
							if images.ndim == 3:
													_UpperCamelCase      :	List[Any]				       =     images[None, ...]
							_UpperCamelCase      :	str				       =     (images * 255).round().astype('uint8'				)
							if images.shape[-1] == 1:
													# special case for grayscale (single channel) images
													_UpperCamelCase      :	List[Any]				       =     [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_						(				lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							if not isinstance(__A				,__A				):
													_UpperCamelCase      :	List[Any]				       =     [images]
							_UpperCamelCase      :	Tuple				       =     [np.array(__A				).astype(np.floataa				) / 2_5_5.0 for image in images]
							_UpperCamelCase      :	Optional[Any]				       =     np.stack(__A				,axis=0				)

							return images





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							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_						(				lowerCamelCase__      :       Any				):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     images.cpu().permute(0				,2				,3				,1				).float().numpy()
							return images





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       int				):







							'''simple docstring'''
							return 2.0 * images - 1.0





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							return (images / 2 + 0.5).clamp(0				,1				)





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       Dict				):







							'''simple docstring'''
							_UpperCamelCase      :	List[Any]				       =     image.convert('RGB'				)
							return image





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       int = None				,lowerCamelCase__      :       Dict = None				,):







							'''simple docstring'''
							if height is None:
													_UpperCamelCase      :	Tuple				       =     image.height
							if width is None:
													_UpperCamelCase      :	Any				       =     image.width

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





	def 			UpperCamelCase_						(				self      :       Optional[int]				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       Optional[Any] = None				,lowerCamelCase__      :       List[str] = None				,):







							'''simple docstring'''
							_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      :	str				       =     [self.convert_to_rgb(__A				) for i in image]
													if self.config.do_resize:
																			_UpperCamelCase      :	Union[str, Any]				       =     [self.resize(__A				,__A				,__A				) for i in image]
													_UpperCamelCase      :	Optional[Any]				       =     self.pil_to_numpy(__A				)  # to np
													_UpperCamelCase      :	Any				       =     self.numpy_to_pt(__A				)  # to pt

							elif isinstance(image[0]				,np.ndarray				):
													_UpperCamelCase      :	int				       =     np.concatenate(__A				,axis=0				) if image[0].ndim == 4 else np.stack(__A				,axis=0				)
													_UpperCamelCase      :	Optional[Any]				       =     self.numpy_to_pt(__A				)
													_UpperCamelCase       ,    _UpperCamelCase       ,    _UpperCamelCase       ,    _UpperCamelCase      :	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      :	Tuple				       =     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      :	List[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      :	Tuple				       =     False

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

							return image





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Union[str, Any] = "pil"				,lowerCamelCase__      :       Union[str, Any] = None				,):







							'''simple docstring'''
							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      :	int				       =     (
													    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      :	Any				       =     'np'

							if output_type == "latent":
													return image

							if do_denormalize is None:
													_UpperCamelCase      :	Union[str, Any]				       =     [self.config.do_normalize] * image.shape[0]

							_UpperCamelCase      :	Union[str, 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      :	List[str]				       =     self.pt_to_numpy(__A				)

							if output_type == "np":
													return image

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






class 							lowercase__			(							_SCREAMING_SNAKE_CASE     ):
	lowercase__						       =    CONFIG_NAME





	@register_to_config
	def __init__(				self      :       str				,lowerCamelCase__      :       Any = True				,lowerCamelCase__      :       Optional[int] = 8				,lowerCamelCase__      :       List[str] = "lanczos"				,lowerCamelCase__      :       List[Any] = True				,):







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





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       List[Any]				):







							'''simple docstring'''
							if images.ndim == 3:
													_UpperCamelCase      :	List[Any]				       =     images[None, ...]
							_UpperCamelCase      :	int				       =     (images * 255).round().astype('uint8'				)
							if images.shape[-1] == 1:
													# special case for grayscale (single channel) images
													_UpperCamelCase      :	Any				       =     [Image.fromarray(image.squeeze()				,mode='L'				) for image in images]
							else:
													_UpperCamelCase      :	int				       =     [Image.fromarray(image[:, :, :3]				) for image in images]

							return pil_images





	@staticmethod
	def 			UpperCamelCase_						(				lowerCamelCase__      :       str				):







							'''simple docstring'''
							return image[:, :, 1] * 2**8 + image[:, :, 2]





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							if images.ndim == 3:
													_UpperCamelCase      :	Dict				       =     images[None, ...]
							_UpperCamelCase      :	int				       =     images[:, :, :, 3:]
							if images.shape[-1] == 6:
													_UpperCamelCase      :	Any				       =     (images_depth * 255).round().astype('uint8'				)
													_UpperCamelCase      :	int				       =     [
													    Image.fromarray(self.rgblike_to_depthmap(__A				)				,mode='I;16'				) for image_depth in images_depth
													]
							elif images.shape[-1] == 4:
													_UpperCamelCase      :	Optional[Any]				       =     (images_depth * 65535.0).astype(np.uintaa				)
													_UpperCamelCase      :	List[str]				       =     [Image.fromarray(__A				,mode='I;16'				) for image_depth in images_depth]
							else:
													raise Exception('Not supported'				)

							return pil_images





	def 			UpperCamelCase_						(				self      :       Any				,lowerCamelCase__      :       List[str]				,lowerCamelCase__      :       int = "pil"				,lowerCamelCase__      :       str = None				,):







							'''simple docstring'''
							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      :	str				       =     (
													    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[str]				       =     'np'

							if do_denormalize is None:
													_UpperCamelCase      :	str				       =     [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      :	int				       =     self.pt_to_numpy(__A				)

							if output_type == "np":
													if image.shape[-1] == 6:
																			_UpperCamelCase      :	Union[str, Any]				       =     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]				) for im in image]				,axis=0				)
													else:
																			_UpperCamelCase      :	Tuple				       =     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'				)







'''simple docstring'''
def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	Optional[Any]				       =     word.upper()
						_UpperCamelCase      :	Optional[Any]				       =     0
						_UpperCamelCase      :	Dict				       =     {}
						_UpperCamelCase      :	List[str]				       =     []

						for letter in word:
												if letter not in letter_nums:
																		_UpperCamelCase      :	int				       =     str(UpperCAmelCase_							)
																		next_num += 1
												word_pattern.append(letter_nums[letter]							)
						return ".".join(UpperCAmelCase_							)


if __name__ == "__main__":
					import pprint
					import time

					snake_case_				:			List[Any]						 =						time.time()
					with open('dictionary.txt') as in_file:
										snake_case_				:			Any						 =						in_file.read().splitlines()

					snake_case_				:			dict						 =						{}
					for word in word_list:
										snake_case_				:			Optional[int]						 =						get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															snake_case_				:			Any						 =						[word]

					with open('word_patterns.txt', 'w') as out_file:
										out_file.write(pprint.pformat(all_patterns))

					snake_case_				:			List[str]						 =						round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.


