


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

_UpperCAmelCase            =		logging.get_logger(__name__)




def     UpperCamelCase						(	__lowercase						:			Optional[int]     ):
		'''simple docstring'''




		if isinstance(snake_case_						,(list, tuple)     ) and isinstance(videos[0]						,(list, tuple)     ) and is_valid_image(videos[0][0]     ):
				return videos

		elif isinstance(snake_case_						,(list, tuple)     ) and is_valid_image(videos[0]     ):
				return [videos]

		elif is_valid_image(snake_case_     ):
				return [[videos]]

		raise ValueError(f'''Could not make batched video from {videos}'''     )





class UpperCAmelCase						( UpperCamelCase__  ):


				'''simple docstring'''






				lowerCamelCase_    = ["""pixel_values"""]
				def __init__(						self		,     lowercase = True		,     lowercase = None		,     lowercase = PILImageResampling.BILINEAR		,     lowercase = True		,     lowercase = None		,     lowercase = True		,     lowercase = 1 / 2_5_5		,     lowercase = True		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						super().__init__(**__a )
						A_		:    Tuple         =				size if size is not None else {'shortest_edge': 2_5_6}
						A_		:    Union[str, Any]         =				get_size_dict(__a		,     default_to_square=__a )
						A_		:    Optional[int]         =				crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A_		:    Optional[int]         =				get_size_dict(__a		,     param_name='crop_size' )

						A_		:    Any         =				do_resize
						A_		:    Union[str, Any]         =				size
						A_		:    str         =				do_center_crop
						A_		:    Union[str, Any]         =				crop_size
						A_		:    Tuple         =				resample
						A_		:    Optional[Any]         =				do_rescale
						A_		:    Any         =				rescale_factor
						A_		:    str         =				offset
						A_		:    int         =				do_normalize
						A_		:    Optional[int]         =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A_		:    Optional[Any]         =				image_std if image_std is not None else IMAGENET_STANDARD_STD
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = PILImageResampling.BILINEAR		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				get_size_dict(__a		,     default_to_square=__a )
						if "shortest_edge" in size:
								A_		:    Dict         =				get_resize_output_image_size(__a		,     size['shortest_edge']		,     default_to_square=__a )
						elif "height" in size and "width" in size:
								A_		:    Optional[Any]         =				(size['height'], size['width'])
						else:
								raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
						return resize(__a		,     size=__a		,     resample=__a		,     data_format=__a		,     **__a )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				get_size_dict(__a )
						if "height" not in size or "width" not in size:
								raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
						return center_crop(__a		,     size=(size['height'], size['width'])		,     data_format=__a		,     **__a )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase = True		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    int         =				image.astype(np.floataa )
						if offset:
								A_		:    Optional[Any]         =				image - (scale / 2)
						return rescale(__a		,     scale=__a		,     data_format=__a		,     **__a )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						return normalize(__a		,     mean=__a		,     std=__a		,     data_format=__a		,     **__a )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = ChannelDimension.FIRST		,     ):



						"""simple docstring"""





						if do_resize and size is None or resample is None:
								raise ValueError('Size and resample must be specified if do_resize is True.' )

						if do_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop is True.' )

						if do_rescale and rescale_factor is None:
								raise ValueError('Rescale factor must be specified if do_rescale is True.' )

						if do_normalize and (image_mean is None or image_std is None):
								raise ValueError('Image mean and std must be specified if do_normalize is True.' )

						if offset and not do_rescale:
								raise ValueError('For offset, do_rescale must also be set to True.' )

						# All transformations expect numpy arrays.
						A_		:    Union[str, Any]         =				to_numpy_array(__a )

						if do_resize:
								A_		:    Tuple         =				self.resize(image=__a		,     size=__a		,     resample=__a )

						if do_center_crop:
								A_		:    Optional[Any]         =				self.center_crop(__a		,     size=__a )

						if do_rescale:
								A_		:    int         =				self.rescale(image=__a		,     scale=__a		,     offset=__a )

						if do_normalize:
								A_		:    int         =				self.normalize(image=__a		,     mean=__a		,     std=__a )

						A_		:    List[Any]         =				to_channel_dimension_format(__a		,     __a )
						return image







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = ChannelDimension.FIRST		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    str         =				do_resize if do_resize is not None else self.do_resize
						A_		:    List[Any]         =				resample if resample is not None else self.resample
						A_		:    Union[str, Any]         =				do_center_crop if do_center_crop is not None else self.do_center_crop
						A_		:    List[Any]         =				do_rescale if do_rescale is not None else self.do_rescale
						A_		:    Dict         =				rescale_factor if rescale_factor is not None else self.rescale_factor
						A_		:    List[str]         =				offset if offset is not None else self.offset
						A_		:    str         =				do_normalize if do_normalize is not None else self.do_normalize
						A_		:    Any         =				image_mean if image_mean is not None else self.image_mean
						A_		:    Tuple         =				image_std if image_std is not None else self.image_std

						A_		:    Union[str, Any]         =				size if size is not None else self.size
						A_		:    int         =				get_size_dict(__a		,     default_to_square=__a )
						A_		:    Optional[int]         =				crop_size if crop_size is not None else self.crop_size
						A_		:    Tuple         =				get_size_dict(__a		,     param_name='crop_size' )

						if not valid_images(__a ):
								raise ValueError(
								    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
								    'torch.Tensor, tf.Tensor or jax.ndarray.' )

						A_		:    Any         =				make_batched(__a )

						A_		:    Any         =				[
						    [
						        self._preprocess_image(
						            image=__a		,     do_resize=__a		,     size=__a		,     resample=__a		,     do_center_crop=__a		,     crop_size=__a		,     do_rescale=__a		,     rescale_factor=__a		,     offset=__a		,     do_normalize=__a		,     image_mean=__a		,     image_std=__a		,     data_format=__a		,     )
						        for img in video
						    ]
						    for video in videos
						]

						A_		:    List[str]         =				{'pixel_values': videos}
						return BatchFeature(data=__a		,     tensor_type=__a )







_UpperCAmelCase            =		"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				input('Enter message: '     )
		A_		:    int         =				input('Enter key [alphanumeric]: '     )
		A_		:    Optional[Any]         =				input('Encrypt/Decrypt [e/d]: '     )

		if mode.lower().startswith('e'     ):
				A_		:    List[Any]         =				'encrypt'
				A_		:    int         =				encrypt_message(__lowercase						,__lowercase     )
		elif mode.lower().startswith('d'     ):
				A_		:    Optional[Any]         =				'decrypt'
				A_		:    Dict         =				decrypt_message(__lowercase						,__lowercase     )

		print(f'''\n{mode.title()}ed message:'''     )
		print(__lowercase     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'encrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		return translate_message(__lowercase						,__lowercase						,'decrypt'     )




def     UpperCamelCase						(	__lowercase						:			str						,__lowercase						:			str						,__lowercase						:			str     ):
		'''simple docstring'''




		A_		:    Tuple         =				[]
		A_		:    str         =				0
		A_		:    Optional[int]         =				key.upper()

		for symbol in message:
				A_		:    Optional[Any]         =				LETTERS.find(symbol.upper()     )
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]     )
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]     )

						num %= len(__lowercase     )

						if symbol.isupper():
								translated.append(LETTERS[num]     )
						elif symbol.islower():
								translated.append(LETTERS[num].lower()     )

						key_index += 1
						if key_index == len(__lowercase     ):
								A_		:    str         =				0
				else:
						translated.append(__lowercase     )
		return "".join(__lowercase     )


if __name__ == "__main__":
	main()






