


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_	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		CONFIG_NAME


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


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :str     )		->      PIL.Image.Image:






										if images.ndim == 3:
																__SCREAMING_SNAKE_CASE						:					str		      =		images[None, ...]
										__SCREAMING_SNAKE_CASE						:					int		      =		(images * 255).round().astype('''uint8'''     )
										if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		[Image.fromarray(image.squeeze()      ,      mode='''L'''     ) for image in images]
										else:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		[Image.fromarray(SCREAMING_SNAKE_CASE_     ) for image in images]

										return pil_images


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Optional[Any]     )		->      np.ndarray:






										if not isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
																__SCREAMING_SNAKE_CASE						:					Any		      =		[images]
										__SCREAMING_SNAKE_CASE						:					Dict		      =		[np.array(SCREAMING_SNAKE_CASE_     ).astype(np.floataa     ) / 255.0 for image in images]
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		np.stack(SCREAMING_SNAKE_CASE_      ,      axis=0     )

										return images


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Dict     )		->      torch.FloatTensor:






										if images.ndim == 3:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		images[..., None]

										__SCREAMING_SNAKE_CASE						:					Dict		      =		torch.from_numpy(images.transpose(0      ,      3      ,      1      ,      2     )     )
										return images


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Any     )		->      np.ndarray:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		images.cpu().permute(0      ,      2      ,      3      ,      1     ).float().numpy()
										return images


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Tuple     )		->      Union[str, Any]:
										return 2.0 * images - 1.0


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Any     )		->      str:
										return (images / 2 + 0.5).clamp(0      ,      1     )


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :Any     )		->      PIL.Image.Image:
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		image.convert('''RGB'''     )
										return image


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Optional[int] = None      ,      lowerCAmelCase__      :Dict = None      ,      )		->      PIL.Image.Image:






										if height is None:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		image.height
										if width is None:
																__SCREAMING_SNAKE_CASE						:					int		      =		image.width

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


				def 		__magic_name__(	self      :Union[str, Any]      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Optional[Any] = None      ,      lowerCAmelCase__      :str = None      ,      )		->      torch.Tensor:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		(PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		[image]
										elif not (isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ) and all(isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ) for i in image     )):
																raise ValueError(
																    f'''Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_     ) for i in image]}. Currently, we only support {', '.join(SCREAMING_SNAKE_CASE_     )}'''     )

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

										elif isinstance(image[0]      ,      np.ndarray     ):
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		np.concatenate(SCREAMING_SNAKE_CASE_      ,      axis=0     ) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE_      ,      axis=0     )
																__SCREAMING_SNAKE_CASE						:					Dict		      =		self.numpy_to_pt(SCREAMING_SNAKE_CASE_     )
																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					List[Any]		      =		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     ):
																__SCREAMING_SNAKE_CASE						:					str		      =		torch.cat(SCREAMING_SNAKE_CASE_      ,      axis=0     ) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE_      ,      axis=0     )
																__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		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]
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		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()}]'''      ,      SCREAMING_SNAKE_CASE_      ,      )
																__SCREAMING_SNAKE_CASE						:					Any		      =		False

										if do_normalize:
																__SCREAMING_SNAKE_CASE						:					Any		      =		self.normalize(SCREAMING_SNAKE_CASE_     )

										return image



				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :int = "pil"      ,      lowerCAmelCase__      :Any = None      ,      )		->      Optional[Any]:






										if not isinstance(SCREAMING_SNAKE_CASE_      ,      torch.Tensor     ):
																raise ValueError(
																    f'''Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE_     )}. We only support pytorch tensor'''     )
										if output_type not in ["latent", "pt", "np", "pil"]:
																__SCREAMING_SNAKE_CASE						:					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'''      ,      SCREAMING_SNAKE_CASE_      ,      standard_warn=SCREAMING_SNAKE_CASE_     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		'''np'''

										if output_type == "latent":
																return image

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

										__SCREAMING_SNAKE_CASE						:					List[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

										__SCREAMING_SNAKE_CASE						:					Any		      =		self.pt_to_numpy(SCREAMING_SNAKE_CASE_     )

										if output_type == "np":
																return image

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




class 	_lowercase				(      SCREAMING_SNAKE_CASE_	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		CONFIG_NAME


				@register_to_config
				def __init__(	self      :Any      ,      lowerCAmelCase__      :int = True      ,      lowerCAmelCase__      :List[Any] = 8      ,      lowerCAmelCase__      :Dict = "lanczos"      ,      lowerCAmelCase__      :List[str] = True      ,      )		->      Optional[Any]:
										super().__init__()


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :List[Any]     )		->      Union[str, Any]:






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

										return pil_images


				@staticmethod
				def 		__magic_name__(	lowerCAmelCase__      :List[Any]     )		->      str:
										return image[:, :, 1] * 2**8 + image[:, :, 2]


				def 		__magic_name__(	self      :List[Any]      ,      lowerCAmelCase__      :Optional[int]     )		->      Tuple:






										if images.ndim == 3:
																__SCREAMING_SNAKE_CASE						:					Dict		      =		images[None, ...]
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		images[:, :, :, 3:]
										if images.shape[-1] == 6:
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		(images_depth * 255).round().astype('''uint8'''     )
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		[
																    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE_     )      ,      mode='''I;16'''     ) for image_depth in images_depth
																]
										elif images.shape[-1] == 4:
																__SCREAMING_SNAKE_CASE						:					Tuple		      =		(images_depth * 65_535.0).astype(np.uintaa     )
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		[Image.fromarray(SCREAMING_SNAKE_CASE_      ,      mode='''I;16'''     ) for image_depth in images_depth]
										else:
																raise Exception('''Not supported'''     )

										return pil_images



				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Optional[int] = "pil"      ,      lowerCAmelCase__      :Union[str, Any] = None      ,      )		->      Any:






										if not isinstance(SCREAMING_SNAKE_CASE_      ,      torch.Tensor     ):
																raise ValueError(
																    f'''Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE_     )}. We only support pytorch tensor'''     )
										if output_type not in ["latent", "pt", "np", "pil"]:
																__SCREAMING_SNAKE_CASE						:					List[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'''      ,      SCREAMING_SNAKE_CASE_      ,      standard_warn=SCREAMING_SNAKE_CASE_     )
																__SCREAMING_SNAKE_CASE						:					int		      =		'''np'''

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

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

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.pt_to_numpy(SCREAMING_SNAKE_CASE_     )

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

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








def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
