
"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

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 TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_a								=						logging.get_logger(__name__)



def  lowerCamelCase__						(     __snake_case      )				->						List[List[ImageInput]]:


					"""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(       lowerCamelCase      ):
		lowercase__         = ['pixel_values']



		def __init__(   self							,						__a = True							,						__a = None							,						__a = PILImageResampling.BILINEAR							,						__a = True							,						__a = None							,						__a = True							,						__a = 1 / 2_55							,						__a = True							,						__a = None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase									=    size if size is not None else {'''shortest_edge''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						param_name='''crop_size''')

							_UpperCamelCase									=    do_resize
							_UpperCamelCase									=    size
							_UpperCamelCase									=    do_center_crop
							_UpperCamelCase									=    crop_size
							_UpperCamelCase									=    resample
							_UpperCamelCase									=    do_rescale
							_UpperCamelCase									=    rescale_factor
							_UpperCamelCase									=    do_normalize
							_UpperCamelCase									=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_UpperCamelCase									=    image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = PILImageResampling.BILINEAR							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a)
							if "shortest_edge" in size:
												_UpperCamelCase									=    get_resize_output_image_size(__a							,						size['''shortest_edge''']							,						default_to_square=__a)
							elif "height" in size and "width" in size:
												_UpperCamelCase									=    (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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			Dict:






							'''simple docstring'''
							return rescale(__a							,						scale=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							return normalize(__a							,						mean=__a							,						std=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						)			->			np.ndarray:






							'''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.''')

							# All transformations expect numpy arrays.
							_UpperCamelCase									=    to_numpy_array(__a)

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

							if do_center_crop:
												_UpperCamelCase									=    self.center_crop(__a							,						size=__a)

							if do_rescale:
												_UpperCamelCase									=    self.rescale(image=__a							,						scale=__a)

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

							_UpperCamelCase									=    to_channel_dimension_format(__a							,						__a)
							return image




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						**__a							,						)			->			PIL.Image.Image:






							'''simple docstring'''
							_UpperCamelCase									=    do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase									=    resample if resample is not None else self.resample
							_UpperCamelCase									=    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase									=    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase									=    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase									=    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase									=    image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase									=    image_std if image_std is not None else self.image_std

							_UpperCamelCase									=    size if size is not None else self.size
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase									=    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.''')

							_UpperCamelCase									=    make_batched(__a)

							_UpperCamelCase									=    [
							    [
							        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							,						do_normalize=__a							,						image_mean=__a							,						image_std=__a							,						data_format=__a							,						)
							        for img in video
							    ]
							    for video in videos
							]

							_UpperCamelCase									=    {'''pixel_values''': videos}
							return BatchFeature(data=__a							,						tensor_type=__a)









'''simple docstring'''

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				data
											SCREAMING_SNAKE_CASE_				=				[0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]



				@staticmethod
				def   a__					(    _lowercase,     _lowercase       )     ->							List[str]:
											return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				B'\x80' + B'\x00' * (63 - (len(self.data       ) + 8) % 64)
											SCREAMING_SNAKE_CASE_				=				self.data + padding + struct.pack('>Q',     8 * len(self.data       )       )
											return padded_data



				def   a__					(    self       )     ->							Optional[Any]:
											return [
											    self.padded_data[i : i + 64] for i in range(0,     len(self.padded_data       ),     64       )
											]



				def   a__					(    self,     _lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				list(struct.unpack('>16L',     _lowercase       )       ) + [0] * 64
											for i in range(16,     80       ):
																		SCREAMING_SNAKE_CASE_				=				self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),     1       )
											return w





				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.padding()
											SCREAMING_SNAKE_CASE_				=				self.split_blocks()
											for block in self.blocks:
																		SCREAMING_SNAKE_CASE_				=				self.expand_block(_lowercase       )
																		SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.h
																		for i in range(0,     80       ):
																									if 0 <= i < 20:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | ((~b) & d)
																																SCREAMING_SNAKE_CASE_				=				0x5_a_8_2_7_9_9_9
																									elif 20 <= i < 40:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0x6_e_d_9_e_b_a_1
																									elif 40 <= i < 60:
																																SCREAMING_SNAKE_CASE_				=				(b & c) | (b & d) | (c & d)
																																SCREAMING_SNAKE_CASE_				=				0x8_f_1_b_b_c_d_c
																									elif 60 <= i < 80:
																																SCREAMING_SNAKE_CASE_				=				b ^ c ^ d
																																SCREAMING_SNAKE_CASE_				=				0xc_a_6_2_c_1_d_6
																									SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				(
																									    self.rotate(_lowercase,     5       ) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
																									    a,
																									    self.rotate(_lowercase,     30       ),
																									    c,
																									    d,
																									)
																		SCREAMING_SNAKE_CASE_				=				(
																		    self.h[0] + a & 0xf_f_f_f_f_f_f_f,
																		    self.h[1] + b & 0xf_f_f_f_f_f_f_f,
																		    self.h[2] + c & 0xf_f_f_f_f_f_f_f,
																		    self.h[3] + d & 0xf_f_f_f_f_f_f_f,
																		    self.h[4] + e & 0xf_f_f_f_f_f_f_f,
																		)
											return ("{:08x}" * 5).format(*self.h       )




def 							_UpperCamelCase				(   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				B'Test String'
							assert SHAaHash(lowerCAmelCase__   ).final_hash() == hashlib.shaa(lowerCAmelCase__   ).hexdigest()  # noqa: S324




def 							_UpperCamelCase				(   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				argparse.ArgumentParser(description='Process some strings or files'   )
							parser.add_argument(
							    '--string'		,dest='input_string'		,default='Hello World!! Welcome to Cryptography'		,help='Hash the string'		,)
							parser.add_argument('--file'		,dest='input_file'		,help='Hash contents of a file'   )
							SCREAMING_SNAKE_CASE_				=				parser.parse_args()
							SCREAMING_SNAKE_CASE_				=				args.input_string
							# In any case hash input should be a bytestring
							if args.input_file:
														with open(args.input_file		,'rb'   ) as f:
																					SCREAMING_SNAKE_CASE_				=				f.read()
							else:
														SCREAMING_SNAKE_CASE_				=				bytes(lowerCAmelCase__		,'utf-8'   )
							print(SHAaHash(lowerCAmelCase__   ).final_hash()   )


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod()





