


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    A_   ( unittest.TestCase     ):







	"""simple docstring"""





	def __init__(			self					: Union[str, Any]       ,    _a					: Optional[Any]       ,    _a					: List[Any]=7       ,    _a					: Dict=3       ,    _a					: Optional[Any]=3_0       ,    _a					: Dict=4_0_0       ,    _a					: List[Any]=True       ,    _a					: List[str]=None       ,    _a					: List[Any]=True       ,    _a					: Union[str, Any]=None       ,    _a					: Tuple=None       ,    _a					: Optional[int]=True       ,    _a					: List[str]=1 / 2_5_5       ,    _a					: List[str]=True       ,    _a					: Tuple=FLAVA_IMAGE_MEAN       ,    _a					: Optional[int]=FLAVA_IMAGE_STD       ,    _a					: Any=1_4       ,    _a					: int=7_5       ,    _a					: List[Any]=None       ,    _a					: List[str]=1_6       ,    _a					: str=0.3       ,    _a					: Dict=None       ,    _a					: Any=True       ,    _a					: List[str]=None       ,    _a					: str=None       ,    _a					: Tuple=True       ,    _a					: Optional[int]=None       ,    _a					: List[Any]=True       ,    _a					: Optional[int]=True       ,    _a					: int=FLAVA_CODEBOOK_MEAN       ,    _a					: str=FLAVA_CODEBOOK_STD       ,    )    ->			Tuple:
						UpperCAmelCase						:				List[Any]		  =			size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						UpperCAmelCase						:				List[Any]		  =			crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						UpperCAmelCase						:				int		  =			codebook_size if codebook_size is not None else {"height": 1_1_2, "width": 1_1_2}
						UpperCAmelCase						:				Dict		  =			codebook_crop_size if codebook_crop_size is not None else {"height": 1_1_2, "width": 1_1_2}

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

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

						UpperCAmelCase						:				Optional[Any]		  =			codebook_do_resize
						UpperCAmelCase						:				List[str]		  =			codebook_size
						UpperCAmelCase						:				Optional[Any]		  =			codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
						UpperCAmelCase						:				List[Any]		  =			codebook_do_center_crop
						UpperCAmelCase						:				List[str]		  =			codebook_crop_size
						UpperCAmelCase						:				List[str]		  =			codebook_do_map_pixels
						UpperCAmelCase						:				Dict		  =			codebook_do_normalize
						UpperCAmelCase						:				Any		  =			codebook_image_mean
						UpperCAmelCase						:				int		  =			codebook_image_std
	def   _UpperCamelCase    (			self					: Any				)    ->			str:
						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   _UpperCamelCase    (			self					: Tuple				)    ->			Any:
						return (self.size["height"], self.size["width"])
	def   _UpperCamelCase    (			self					: Optional[int]				)    ->			int:
						return (
						    (self.input_size_patches, self.input_size_patches)
						    if not isinstance(self.input_size_patches       ,    _a				)
						    else self.input_size_patches
						)

	def   _UpperCamelCase    (			self					: List[Any]				)    ->			Any:
						return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class    A_   ( a_      ,						unittest.TestCase     ):







	"""simple docstring"""





	_UpperCamelCase				: Union[str, Any]      =					FlavaImageProcessor if is_vision_available() else None
	_UpperCamelCase				: Tuple      =					None
	def   _UpperCamelCase    (			self					: List[Any]				)    ->			Dict:
						UpperCAmelCase						:				Union[str, Any]		  =			FlavaImageProcessingTester(self				)
	@property
	def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Any:
						return self.image_processor_tester.prepare_image_processor_dict()
	def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Tuple:
						UpperCAmelCase						:				List[Any]		  =			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   _UpperCamelCase    (			self					: Optional[Any]				)    ->			int:
						UpperCAmelCase						:				Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict				)
						self.assertEqual(image_processor.size       ,    {"height": 2_2_4, "width": 2_2_4}				)
						self.assertEqual(image_processor.crop_size       ,    {"height": 2_2_4, "width": 2_2_4}				)
						self.assertEqual(image_processor.codebook_size       ,    {"height": 1_1_2, "width": 1_1_2}				)
						self.assertEqual(image_processor.codebook_crop_size       ,    {"height": 1_1_2, "width": 1_1_2}				)

						UpperCAmelCase						:				List[Any]		  =			self.image_processing_class.from_dict(
						    self.image_processor_dict       ,    size=4_2       ,    crop_size=8_4       ,    codebook_size=3_3       ,    codebook_crop_size=6_6				)
						self.assertEqual(image_processor.size       ,    {"height": 4_2, "width": 4_2}				)
						self.assertEqual(image_processor.crop_size       ,    {"height": 8_4, "width": 8_4}				)
						self.assertEqual(image_processor.codebook_size       ,    {"height": 3_3, "width": 3_3}				)
						self.assertEqual(image_processor.codebook_crop_size       ,    {"height": 6_6, "width": 6_6}				)
	def   _UpperCamelCase    (			self					: str				)    ->			Optional[int]:
						pass
	def   _UpperCamelCase    (			self					: List[str]				)    ->			Union[str, Any]:
						# Initialize image_processing
						UpperCAmelCase						:				List[str]		  =			self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						UpperCAmelCase						:				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
						UpperCAmelCase						:				Dict		  =			image_processing(image_inputs[0]       ,    return_tensors="pt"				)

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

						UpperCAmelCase						:				Tuple		  =			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
						UpperCAmelCase						:				Optional[Any]		  =			image_processing(_a       ,    return_tensors="pt"				)
						UpperCAmelCase						:				Optional[int]		  =			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   _UpperCamelCase    (			self					: Tuple       ,    _a					: List[Any]       ,    _a					: Tuple				)    ->			int:
						# Initialize image_processing
						UpperCAmelCase						:				Any		  =			self.image_processing_class(**self.image_processor_dict				)
						# create random tensors
						UpperCAmelCase						:				List[str]		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=_a       ,    **_a				)
						for image in image_inputs:
											self.assertIsInstance(_a       ,    _a				)

						# Test not batched input
						UpperCAmelCase						:				Tuple		  =			image_processing(image_inputs[0]       ,    return_tensors="pt"				)

						UpperCAmelCase						:				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)       ,    )

						UpperCAmelCase						:				int		  =			image_processing(_a       ,    return_image_mask=_a       ,    return_tensors="pt"				)

						UpperCAmelCase						:				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,
						    )       ,    )

						UpperCAmelCase						:				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
						UpperCAmelCase						:				str		  =			image_processing(_a       ,    return_tensors="pt"				).pixel_values

						UpperCAmelCase						:				List[str]		  =			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
						UpperCAmelCase						:				Optional[Any]		  =			image_processing(_a       ,    return_image_mask=_a       ,    return_tensors="pt"				)

						UpperCAmelCase						:				Optional[Any]		  =			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,
						    )       ,    )

						UpperCAmelCase						:				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,
						    )       ,    )
	def   _UpperCamelCase    (			self					: Tuple				)    ->			Optional[int]:
						self._test_call_framework(np.ndarray       ,    prepare_kwargs={"numpify": True}				)
	def   _UpperCamelCase    (			self					: List[Any]				)    ->			Tuple:
						self._test_call_framework(torch.Tensor       ,    prepare_kwargs={"torchify": True}				)
	def   _UpperCamelCase    (			self					: List[str]				)    ->			Dict:
						# Initialize image_processing
						random.seed(1_2_3_4				)
						UpperCAmelCase						:				List[Any]		  =			self.image_processing_class(**self.image_processor_dict				)
						UpperCAmelCase						:				str		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=_a       ,    torchify=_a				)

						# Test not batched input
						UpperCAmelCase						:				Optional[Any]		  =			image_processing(image_inputs[0]       ,    return_image_mask=_a       ,    return_tensors="pt"				)
						self.assertEqual(encoded_images.bool_masked_pos.sum().item()       ,    7_5				)

	def   _UpperCamelCase    (			self					: Union[str, Any]				)    ->			Any:
						# Initialize image_processing
						UpperCAmelCase						:				Dict		  =			self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						UpperCAmelCase						:				Dict		  =			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
						UpperCAmelCase						:				Dict		  =			image_processing(image_inputs[0]       ,    return_codebook_pixels=_a       ,    return_tensors="pt"				)
						UpperCAmelCase						:				Union[str, Any]		  =			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
						UpperCAmelCase						:				Any		  =			image_processing(_a       ,    return_codebook_pixels=_a       ,    return_tensors="pt"				)
						UpperCAmelCase						:				List[str]		  =			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   A   (		A__		,		A__ ):
     UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			img.shape[0], img.shape[1]

     def to_grayscale(A__		,		A__		,		A__ ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(A__ ):
          return min(A__		,		2_5_5 )

     for i in range(A__ ):
          for j in range(A__ ):
               UpperCAmelCase						:				Optional[Any]		  =			int(to_grayscale(*img[i][j] ) )
               UpperCAmelCase						:				List[str]		  =			[
                   normalize(A__ ),
                   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 (1_0, 2_0, 3_0, 4_0)
      }

      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()



