

from collections.abc import Generator
from math import sin
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Union[str, Any]:



    '''simple docstring'''






    if len(_UpperCAmelCase ) != 32:
        raise ValueError('Input must be of length 32' )

    lowerCAmelCase :	Optional[Any]				=					b""
    for i in [3, 2, 1, 0]:
        little_endian += string_aa[8 * i : 8 * i + 8]
    return little_endian
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Optional[Any]:



    '''simple docstring'''






    if i < 0:
        raise ValueError('Input must be non-negative' )

    lowerCAmelCase :	Optional[Any]				=					format(_UpperCAmelCase,					'08x' )[-8:]
    lowerCAmelCase :	Tuple				=					b""
    for i in [3, 2, 1, 0]:
        little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8' )
    return little_endian_hex
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       str:



    '''simple docstring'''






    lowerCAmelCase :	Any				=					b""
    for char in message:
        bit_string += format(_UpperCAmelCase,					'08b' ).encode('utf-8' )
    lowerCAmelCase :	Optional[Any]				=					format(len(_UpperCAmelCase ),					'064b' ).encode('utf-8' )

    # Pad bit_string to a multiple of 512 chars
    bit_string += b"1"
    while len(_UpperCAmelCase ) % 512 != 448:
        bit_string += b"0"
    bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )

    return bit_string
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Dict:



    '''simple docstring'''






    if len(_UpperCAmelCase ) % 512 != 0:
        raise ValueError('Input must have length that\'s a multiple of 512' )

    for pos in range(0,					len(_UpperCAmelCase ),					512 ):
        lowerCAmelCase :	List[str]				=					bit_string[pos : pos + 512]
        lowerCAmelCase :	Union[str, Any]				=					[]
        for i in range(0,					512,					32 ):
            block_words.append(int(to_little_endian(block[i : i + 32] ),					2 ) )
        yield block_words
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Any:



    '''simple docstring'''






    if i < 0:
        raise ValueError('Input must be non-negative' )

    lowerCAmelCase :	Optional[int]				=					format(_UpperCAmelCase,					'032b' )
    lowerCAmelCase :	Optional[int]				=					""
    for c in i_str:
        new_str += "1" if c == "0" else "0"
    return int(_UpperCAmelCase,					2 )
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase,					_UpperCAmelCase )    ->       Union[str, Any]:



    '''simple docstring'''






    return (a + b) % 2**32
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase,					_UpperCAmelCase )    ->       str:



    '''simple docstring'''






    if i < 0:
        raise ValueError('Input must be non-negative' )
    if shift < 0:
        raise ValueError('Shift must be non-negative' )
    return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def 						SCREAMING_SNAKE_CASE__ (							_UpperCAmelCase )    ->       Any:



    '''simple docstring'''






    lowerCAmelCase :	str				=					preprocess(_UpperCAmelCase )

    lowerCAmelCase :	List[str]				=					[int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]

    # Starting states
    lowerCAmelCase :	List[Any]				=					0X67_45_23_01
    lowerCAmelCase :	Optional[Any]				=					0Xef_cd_ab_89
    lowerCAmelCase :	List[Any]				=					0X98_ba_dc_fe
    lowerCAmelCase :	Any				=					0X10_32_54_76

    lowerCAmelCase :	str				=					[
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
    ]

    # Process bit string in chunks, each with 16 32-char words
    for block_words in get_block_words(_UpperCAmelCase ):
        lowerCAmelCase :	List[Any]				=					aa
        lowerCAmelCase :	List[Any]				=					ba
        lowerCAmelCase :	List[Any]				=					ca
        lowerCAmelCase :	List[str]				=					da

        # Hash current chunk
        for i in range(64 ):
            if i <= 15:
                # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                lowerCAmelCase :	Tuple				=					d ^ (b & (c ^ d))
                lowerCAmelCase :	str				=					i
            elif i <= 31:
                # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                lowerCAmelCase :	Any				=					c ^ (d & (b ^ c))
                lowerCAmelCase :	str				=					(5 * i + 1) % 16
            elif i <= 47:
                lowerCAmelCase :	Dict				=					b ^ c ^ d
                lowerCAmelCase :	Optional[Any]				=					(3 * i + 5) % 16
            else:
                lowerCAmelCase :	Optional[Any]				=					c ^ (b | not_aa(_UpperCAmelCase ))
                lowerCAmelCase :	str				=					(7 * i) % 16
            lowerCAmelCase :	Optional[Any]				=					(f + a + added_consts[i] + block_words[g]) % 2**32
            lowerCAmelCase :	List[Any]				=					d
            lowerCAmelCase :	List[str]				=					c
            lowerCAmelCase :	int				=					b
            lowerCAmelCase :	Dict				=					sum_aa(_UpperCAmelCase,					left_rotate_aa(_UpperCAmelCase,					shift_amounts[i] ) )

        # Add hashed chunk to running total
        lowerCAmelCase :	Union[str, Any]				=					sum_aa(_UpperCAmelCase,					_UpperCAmelCase )
        lowerCAmelCase :	List[Any]				=					sum_aa(_UpperCAmelCase,					_UpperCAmelCase )
        lowerCAmelCase :	int				=					sum_aa(_UpperCAmelCase,					_UpperCAmelCase )
        lowerCAmelCase :	List[str]				=					sum_aa(_UpperCAmelCase,					_UpperCAmelCase )

    lowerCAmelCase :	Tuple				=					reformat_hex(_UpperCAmelCase ) + reformat_hex(_UpperCAmelCase ) + reformat_hex(_UpperCAmelCase ) + reformat_hex(_UpperCAmelCase )
    return digest


if __name__ == "__main__":
 import doctest

 doctest.testmod()












import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor







class 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      )   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					List[str]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Union[str, Any]										=batch_size
        a__	:					List[str]										=num_channels
        a__	:					List[Any]										=image_size
        a__	:					str										=min_resolution
        a__	:					Optional[int]										=max_resolution
        a__	:					Tuple										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					List[Any]										=do_center_crop
        a__	:					List[str]										=crop_size
        a__	:					Optional[int]										=do_flip_channel_order







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_flip_channel_order": self.do_flip_channel_order,
        }







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					int						=			MobileViTImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Tuple										=MobileViTImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_flip_channel_order"							)							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					List[Any]										=self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 2_0}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 1_8, "width": 1_8}							)

        a__	:					Union[str, Any]										=self.image_processing_class.from_dict(self.image_processor_dict		,      size=4_2		,      crop_size=8_4							)
        self.assertEqual(image_processor.size		,      {"shortest_edge": 4_2}							)
        self.assertEqual(image_processor.crop_size		,      {"height": 8_4, "width": 8_4}							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					Any										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      Image.Image							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[Any]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					List[Any]										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      numpify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      np.ndarray							)

        # Test not batched input
        a__	:					Tuple										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					int										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					int										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					Tuple										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      torchify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      torch.Tensor							)

        # Test not batched input
        a__	:					List[str]										=image_processing(image_inputs[0]		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )

        # Test batched
        a__	:					List[str]										=image_processing(lowerCAmelCase__		,      return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )		,      )






