



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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a__      :				str   	=			logging.get_logger(__name__)


if is_vision_available():
       import PIL



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  Tuple       				=    ["pixel_values"]


   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Dict[str, int] = None			,				lowerCAmelCase  :			PILImageResampling = PILImageResampling.BICUBIC			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Dict[str, int] = None			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Union[int, float] = 1 / 2_55			,				lowerCAmelCase  :			bool = True			,				lowerCAmelCase  :			Optional[Union[float, List[float]]] = None			,				lowerCAmelCase  :			Optional[Union[float, List[float]]] = None			,				lowerCAmelCase  :			bool = True			,				**lowerCAmelCase  :			Any			,				)	->				None:




         """simple docstring"""

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

         lowercase__     =  do_resize
         lowercase__     =  size
         lowercase__     =  resample
         lowercase__     =  do_center_crop
         lowercase__     =  crop_size
         lowercase__     =  do_rescale
         lowercase__     =  rescale_factor
         lowercase__     =  do_normalize
         lowercase__     =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         lowercase__     =  image_std if image_std is not None else OPENAI_CLIP_STD
         lowercase__     =  do_convert_rgb


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Dict[str, int]			,				lowerCAmelCase  :			PILImageResampling = PILImageResampling.BICUBIC			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			str			,				)	->				np.ndarray:




         """simple docstring"""

         lowercase__     =  get_size_dict(lowerCAmelCase			,				default_to_square=lowerCAmelCase)
         if "shortest_edge" not in size:
               raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
         lowercase__     =  get_resize_output_image_size(lowerCAmelCase			,				size=size['shortest_edge']			,				default_to_square=lowerCAmelCase)
         return resize(lowerCAmelCase			,				size=lowerCAmelCase			,				resample=lowerCAmelCase			,				data_format=lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Dict[str, int]			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			Union[str, Any]			,				)	->				np.ndarray:




         """simple docstring"""

         lowercase__     =  get_size_dict(lowerCAmelCase)
         if "height" not in size or "width" not in size:
               raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
         return center_crop(lowerCAmelCase			,				size=(size['height'], size['width'])			,				data_format=lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Union[int, float]			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			List[Any]			,				)	->				Optional[Any]:




         """simple docstring"""

         return rescale(lowerCAmelCase			,				scale=lowerCAmelCase			,				data_format=lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			np.ndarray			,				lowerCAmelCase  :			Union[float, List[float]]			,				lowerCAmelCase  :			Union[float, List[float]]			,				lowerCAmelCase  :			Optional[Union[str, ChannelDimension]] = None			,				**lowerCAmelCase  :			List[str]			,				)	->				np.ndarray:




         """simple docstring"""

         return normalize(lowerCAmelCase			,				mean=lowerCAmelCase			,				std=lowerCAmelCase			,				data_format=lowerCAmelCase			,				**lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			ImageInput			,				lowerCAmelCase  :			bool = None			,				lowerCAmelCase  :			Dict[str, int] = None			,				lowerCAmelCase  :			PILImageResampling = None			,				lowerCAmelCase  :			bool = None			,				lowerCAmelCase  :			int = None			,				lowerCAmelCase  :			bool = None			,				lowerCAmelCase  :			float = None			,				lowerCAmelCase  :			bool = None			,				lowerCAmelCase  :			Optional[Union[float, List[float]]] = None			,				lowerCAmelCase  :			Optional[Union[float, List[float]]] = None			,				lowerCAmelCase  :			bool = None			,				lowerCAmelCase  :			Optional[Union[str, TensorType]] = None			,				lowerCAmelCase  :			Optional[ChannelDimension] = ChannelDimension.FIRST			,				**lowerCAmelCase  :			Tuple			,				)	->				PIL.Image.Image:




         """simple docstring"""

         lowercase__     =  do_resize if do_resize is not None else self.do_resize
         lowercase__     =  size if size is not None else self.size
         lowercase__     =  get_size_dict(lowerCAmelCase			,				param_name='size'			,				default_to_square=lowerCAmelCase)
         lowercase__     =  resample if resample is not None else self.resample
         lowercase__     =  do_center_crop if do_center_crop is not None else self.do_center_crop
         lowercase__     =  crop_size if crop_size is not None else self.crop_size
         lowercase__     =  get_size_dict(lowerCAmelCase			,				param_name='crop_size'			,				default_to_square=lowerCAmelCase)
         lowercase__     =  do_rescale if do_rescale is not None else self.do_rescale
         lowercase__     =  rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__     =  do_normalize if do_normalize is not None else self.do_normalize
         lowercase__     =  image_mean if image_mean is not None else self.image_mean
         lowercase__     =  image_std if image_std is not None else self.image_std
         lowercase__     =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         lowercase__     =  make_list_of_images(lowerCAmelCase)

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

         if do_resize and size is None:
               raise ValueError('Size 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.')

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
               lowercase__     =  [convert_to_rgb(lowerCAmelCase) for image in images]

         # All transformations expect numpy arrays.
         lowercase__     =  [to_numpy_array(lowerCAmelCase) for image in images]

         if do_resize:
               lowercase__     =  [self.resize(image=lowerCAmelCase			,				size=lowerCAmelCase			,				resample=lowerCAmelCase) for image in images]

         if do_center_crop:
               lowercase__     =  [self.center_crop(image=lowerCAmelCase			,				size=lowerCAmelCase) for image in images]

         if do_rescale:
               lowercase__     =  [self.rescale(image=lowerCAmelCase			,				scale=lowerCAmelCase) for image in images]

         if do_normalize:
               lowercase__     =  [self.normalize(image=lowerCAmelCase			,				mean=lowerCAmelCase			,				std=lowerCAmelCase) for image in images]

         lowercase__     =  [to_channel_dimension_format(lowerCAmelCase			,				lowerCAmelCase) for image in images]

         lowercase__     =  {'pixel_values': images}
         return BatchFeature(data=lowerCAmelCase			,				tensor_type=lowerCAmelCase)





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



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Optional[Any]			,				lowerCAmelCase  :			str)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  data
         lowercase__     =  [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 			UpperCAmelCase		(						lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Optional[int])	->				str:




         """simple docstring"""

         return ((n << b) | (n >> (32 - b))) & 0XF_F_F_F_F_F_F_F


   def 			UpperCAmelCase		(						self  :			Dict)	->				Dict:




         """simple docstring"""

         lowercase__     =  B'\x80' + B'\x00' * (63 - (len(self.data) + 8) % 64)
         lowercase__     =  self.data + padding + struct.pack('>Q'			,				8 * len(self.data))
         return padded_data


   def 			UpperCAmelCase		(						self  :			int)	->				Tuple:




         """simple docstring"""

         return [
             self.padded_data[i : i + 64] for i in range(0			,				len(self.padded_data)			,				64)
         ]


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			int)	->				List[Any]:




         """simple docstring"""

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



   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.padding()
         lowercase__     =  self.split_blocks()
         for block in self.blocks:
               lowercase__     =  self.expand_block(lowerCAmelCase)
               lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  self.h
               for i in range(0			,				80):
                     if 0 <= i < 20:
                           lowercase__     =  (b & c) | ((~b) & d)
                           lowercase__     =  0X5_A_8_2_7_9_9_9
                     elif 20 <= i < 40:
                           lowercase__     =  b ^ c ^ d
                           lowercase__     =  0X6_E_D_9_E_B_A_1
                     elif 40 <= i < 60:
                           lowercase__     =  (b & c) | (b & d) | (c & d)
                           lowercase__     =  0X8_F_1_B_B_C_D_C
                     elif 60 <= i < 80:
                           lowercase__     =  b ^ c ^ d
                           lowercase__     =  0XC_A_6_2_C_1_D_6
                     lowercase__,	lowercase__,	lowercase__,	lowercase__,	lowercase__     =  (
                         self.rotate(lowerCAmelCase			,				5) + f + e + k + expanded_block[i] & 0XF_F_F_F_F_F_F_F,
                         a,
                         self.rotate(lowerCAmelCase			,				30),
                         c,
                         d,
                     )
               lowercase__     =  (
                   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   _lowerCAmelCase   (						):
      lowercase__     =  B'Test String'
      assert SHAaHash(A__	).final_hash() == hashlib.shaa(A__	).hexdigest()  # noqa: S324

def   _lowerCAmelCase   (						):
      lowercase__     =  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'	)
      lowercase__     =  parser.parse_args()
      lowercase__     =  args.input_string
      # In any case hash input should be a bytestring
      if args.input_file:
            with open(args.input_file						,  'rb'	) as f:
                  lowercase__     =  f.read()
      else:
            lowercase__     =  bytes(A__						,  'utf-8'	)
      print(SHAaHash(A__	).final_hash()	)


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

       doctest.testmod()
