

import random
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():
       import PIL

       from transformers import FlavaImageProcessor
       from transformers.image_utils import PILImageResampling
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )
else:
       snake_case_    						=				snake_case_    						=				snake_case_    						=				snake_case_    						=				None


class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):





    def __init__(   self		,     a		,     a=7		,     a=3		,     a=30		,     a=400		,     a=True		,     a=None		,     a=True		,     a=None		,     a=None		,     a=True		,     a=1 / 255		,     a=True		,     a=FLAVA_IMAGE_MEAN		,     a=FLAVA_IMAGE_STD		,     a=14		,     a=75		,     a=None		,     a=16		,     a=0.3		,     a=None		,     a=True		,     a=None		,     a=None		,     a=True		,     a=None		,     a=True		,     a=True		,     a=FLAVA_CODEBOOK_MEAN		,     a=FLAVA_CODEBOOK_STD		,     ):
         lowercase__					:     List[str]   = size if size is not None else {'height': 224, 'width': 224}
         lowercase__					:     Any   = crop_size if crop_size is not None else {'height': 224, 'width': 224}
         lowercase__					:     Any   = codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
         lowercase__					:     Optional[Any]   = codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

         lowercase__					:     List[str]   = parent
         lowercase__					:     List[str]   = batch_size
         lowercase__					:     int   = num_channels
         lowercase__					:     Optional[Any]   = do_resize
         lowercase__					:     Tuple   = do_rescale
         lowercase__					:     Any   = rescale_factor
         lowercase__					:     int   = min_resolution
         lowercase__					:     str   = max_resolution
         lowercase__					:     List[Any]   = size
         lowercase__					:     str   = resample if resample is not None else PILImageResampling.BICUBIC
         lowercase__					:     List[Any]   = do_normalize
         lowercase__					:     List[Any]   = image_mean
         lowercase__					:     List[Any]   = image_std
         lowercase__					:     List[Any]   = do_center_crop
         lowercase__					:     Union[str, Any]   = crop_size

         lowercase__					:     Optional[int]   = input_size_patches
         lowercase__					:     Optional[Any]   = total_mask_patches
         lowercase__					:     List[str]   = mask_group_max_patches
         lowercase__					:     int   = mask_group_min_patches
         lowercase__					:     int   = mask_group_min_aspect_ratio
         lowercase__					:     Any   = mask_group_max_aspect_ratio

         lowercase__					:     List[Any]   = codebook_do_resize
         lowercase__					:     Any   = codebook_size
         lowercase__					:     List[str]   = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
         lowercase__					:     List[Any]   = codebook_do_center_crop
         lowercase__					:     List[str]   = codebook_crop_size
         lowercase__					:     Optional[Any]   = codebook_do_map_pixels
         lowercase__					:     Union[str, Any]   = codebook_do_normalize
         lowercase__					:     Dict   = codebook_image_mean
         lowercase__					:     Union[str, Any]   = codebook_image_std





    def    snake_case_       (   self):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "resample": self.resample,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "input_size_patches": self.input_size_patches,
             "total_mask_patches": self.total_mask_patches,
             "mask_group_max_patches": self.mask_group_max_patches,
             "mask_group_min_patches": self.mask_group_min_patches,
             "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
             "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
             "codebook_do_resize": self.codebook_do_resize,
             "codebook_size": self.codebook_size,
             "codebook_resample": self.codebook_resample,
             "codebook_do_center_crop": self.codebook_do_center_crop,
             "codebook_crop_size": self.codebook_crop_size,
             "codebook_do_map_pixels": self.codebook_do_map_pixels,
             "codebook_do_normalize": self.codebook_do_normalize,
             "codebook_image_mean": self.codebook_image_mean,
             "codebook_image_std": self.codebook_image_std,
         }





    def    snake_case_       (   self):
         return (self.size["height"], self.size["width"])





    def    snake_case_       (   self):
         return (
             (self.input_size_patches, self.input_size_patches)
             if not isinstance(self.input_size_patches		,     a)
             else self.input_size_patches
         )





    def    snake_case_       (   self):
         return (self.codebook_size["height"], self.codebook_size["width"])



@require_torch
@require_vision
class    SCREAMING_SNAKE_CASE__      (__snake_case	, unittest.TestCase  ):
    __lowerCamelCase		: Optional[Any]														=  FlavaImageProcessor if is_vision_available() else None
    __lowerCamelCase		: str														=  None





    def    snake_case_       (   self):
         lowercase__					:     Optional[int]   = FlavaImageProcessingTester(self)





    @property
    def    snake_case_       (   self):
         return self.image_processor_tester.prepare_image_processor_dict()





    def    snake_case_       (   self):
         lowercase__					:     str   = self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(a		,     'image_mean'))
         self.assertTrue(hasattr(a		,     'image_std'))
         self.assertTrue(hasattr(a		,     'do_normalize'))
         self.assertTrue(hasattr(a		,     'do_resize'))
         self.assertTrue(hasattr(a		,     'resample'))
         self.assertTrue(hasattr(a		,     'crop_size'))
         self.assertTrue(hasattr(a		,     'do_center_crop'))
         self.assertTrue(hasattr(a		,     'do_rescale'))
         self.assertTrue(hasattr(a		,     'rescale_factor'))
         self.assertTrue(hasattr(a		,     'masking_generator'))
         self.assertTrue(hasattr(a		,     'codebook_do_resize'))
         self.assertTrue(hasattr(a		,     'codebook_size'))
         self.assertTrue(hasattr(a		,     'codebook_resample'))
         self.assertTrue(hasattr(a		,     'codebook_do_center_crop'))
         self.assertTrue(hasattr(a		,     'codebook_crop_size'))
         self.assertTrue(hasattr(a		,     'codebook_do_map_pixels'))
         self.assertTrue(hasattr(a		,     'codebook_do_normalize'))
         self.assertTrue(hasattr(a		,     'codebook_image_mean'))
         self.assertTrue(hasattr(a		,     'codebook_image_std'))





    def    snake_case_       (   self):
         lowercase__					:     Dict   = self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size		,     {'height': 224, 'width': 224})
         self.assertEqual(image_processor.crop_size		,     {'height': 224, 'width': 224})
         self.assertEqual(image_processor.codebook_size		,     {'height': 112, 'width': 112})
         self.assertEqual(image_processor.codebook_crop_size		,     {'height': 112, 'width': 112})

         lowercase__					:     Optional[int]   = self.image_processing_class.from_dict(
             self.image_processor_dict		,     size=42		,     crop_size=84		,     codebook_size=33		,     codebook_crop_size=66)
         self.assertEqual(image_processor.size		,     {'height': 42, 'width': 42})
         self.assertEqual(image_processor.crop_size		,     {'height': 84, 'width': 84})
         self.assertEqual(image_processor.codebook_size		,     {'height': 33, 'width': 33})
         self.assertEqual(image_processor.codebook_crop_size		,     {'height': 66, 'width': 66})





    def    snake_case_       (   self):
         pass





    def    snake_case_       (   self):
         # Initialize image_processing
         lowercase__					:     Optional[int]   = self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__					:     Optional[Any]   = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=a)
         for image in image_inputs:
              self.assertIsInstance(a		,     PIL.Image.Image)

         # Test not batched input
         lowercase__					:     Optional[Any]   = image_processing(image_inputs[0]		,     return_tensors='pt')

         # Test no bool masked pos
         self.assertFalse('bool_masked_pos' in encoded_images)

         lowercase__   ,				lowercase__					:     List[str]   = self.image_processor_tester.get_expected_image_size()

         self.assertEqual(
             encoded_images.pixel_values.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

         # Test batched
         lowercase__					:     Optional[Any]   = image_processing(a		,     return_tensors='pt')
         lowercase__   ,				lowercase__					:     List[Any]   = self.image_processor_tester.get_expected_image_size()

         # Test no bool masked pos
         self.assertFalse('bool_masked_pos' in encoded_images)

         self.assertEqual(
             encoded_images.pixel_values.shape		,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,     )





    def    snake_case_       (   self		,     a		,     a):
         # Initialize image_processing
         lowercase__					:     Union[str, Any]   = self.image_processing_class(**self.image_processor_dict)
         # create random tensors
         lowercase__					:     Union[str, Any]   = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=a		,     **a)
         for image in image_inputs:
              self.assertIsInstance(a		,     a)

         # Test not batched input
         lowercase__					:     List[str]   = image_processing(image_inputs[0]		,     return_tensors='pt')

         lowercase__   ,				lowercase__					:     Optional[int]   = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

         lowercase__					:     Tuple   = image_processing(a		,     return_image_mask=a		,     return_tensors='pt')

         lowercase__   ,				lowercase__					:     Optional[int]   = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape		,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,     )

         lowercase__   ,				lowercase__					:     Optional[Any]   = self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape		,     (
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )		,     )

         # Test batched
         lowercase__					:     str   = image_processing(a		,     return_tensors='pt').pixel_values

         lowercase__   ,				lowercase__					:     List[Any]   = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.shape		,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,     )

         # Test masking
         lowercase__					:     str   = image_processing(a		,     return_image_mask=a		,     return_tensors='pt')

         lowercase__   ,				lowercase__					:     str   = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape		,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,     )

         lowercase__   ,				lowercase__					:     str   = self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape		,     (
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )		,     )





    def    snake_case_       (   self):
         self._test_call_framework(np.ndarray		,     prepare_kwargs={'numpify': True})





    def    snake_case_       (   self):
         self._test_call_framework(torch.Tensor		,     prepare_kwargs={'torchify': True})





    def    snake_case_       (   self):
         # Initialize image_processing
         random.seed(1234)
         lowercase__					:     Union[str, Any]   = self.image_processing_class(**self.image_processor_dict)
         lowercase__					:     str   = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=a		,     torchify=a)

         # Test not batched input
         lowercase__					:     int   = image_processing(image_inputs[0]		,     return_image_mask=a		,     return_tensors='pt')
         self.assertEqual(encoded_images.bool_masked_pos.sum().item()		,     75)





    def    snake_case_       (   self):
         # Initialize image_processing
         lowercase__					:     List[str]   = self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__					:     str   = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=a)
         for image in image_inputs:
              self.assertIsInstance(a		,     PIL.Image.Image)

         # Test not batched input
         lowercase__					:     Union[str, Any]   = image_processing(image_inputs[0]		,     return_codebook_pixels=a		,     return_tensors='pt')
         lowercase__   ,				lowercase__					:     Tuple   = self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

         # Test batched
         lowercase__					:     int   = image_processing(a		,     return_codebook_pixels=a		,     return_tensors='pt')
         lowercase__   ,				lowercase__					:     int   = self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape		,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )		,     )




from cva import destroyAllWindows, imread, imshow, waitKey

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]

     def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
          return min(SCREAMING_SNAKE_CASE_			,  255    )

     for i in range(SCREAMING_SNAKE_CASE_    ):
          for j in range(SCREAMING_SNAKE_CASE_    ):
               lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
               lowercase__					:     Dict   = [
                   normalize(SCREAMING_SNAKE_CASE_    ),
                   normalize(greyscale + factor    ),
                   normalize(greyscale + 2 * factor    ),
               ]

     return img


if __name__ == "__main__":
       # read original image
       snake_case_    						=				{
           percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F'''Original image with sepia (factor: {percentage})''', img)

       waitKey(0)
       destroyAllWindows()

