'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_      	=	logging.get_logger(__name__)




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Any     		= ['pixel_values']




						def __init__(    self :       Tuple							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[Dict[str, int]] = None							,							UpperCamelCase_ :       PILImageResampling = PILImageResampling.BICUBIC							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Union[int, float] = 1 / 2_55							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = None							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = None							,							**UpperCamelCase_ :       Union[str, Any]							,							)     ->				None:
													super().__init__(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = size if size is not None else {'height': 2_24, 'width': 2_24}
													SCREAMING_SNAKE_CASE__     :List[Any]						    = get_size_dict(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													SCREAMING_SNAKE_CASE__     :Dict						    = get_size_dict(UpperCamelCase_							,							default_to_square=UpperCamelCase_							,							param_name='crop_size'      )

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_resize
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_rescale
													SCREAMING_SNAKE_CASE__     :List[str]						    = do_normalize
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = do_center_crop
													SCREAMING_SNAKE_CASE__     :Tuple						    = crop_size
													SCREAMING_SNAKE_CASE__     :str						    = size
													SCREAMING_SNAKE_CASE__     :int						    = resample
													SCREAMING_SNAKE_CASE__     :List[str]						    = rescale_factor
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													SCREAMING_SNAKE_CASE__     :Dict						    = image_std if image_std is not None else IMAGENET_DEFAULT_STD




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       np.ndarray							,							UpperCamelCase_ :       Dict[str, int]							,							UpperCamelCase_ :       PILImageResampling = PILImageResampling.BILINEAR							,							UpperCamelCase_ :       Optional[Union[str, ChannelDimension]] = None							,							**UpperCamelCase_ :       Optional[Any]							,							)     ->				np.ndarray:
													SCREAMING_SNAKE_CASE__     :List[str]						    = get_size_dict(UpperCamelCase_      )

													if "shortest_edge" in size:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = get_resize_output_image_size(UpperCamelCase_							,							size=size['shortest_edge']							,							default_to_square=UpperCamelCase_      )
																				# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
													elif "height" in size and "width" in size:
																				SCREAMING_SNAKE_CASE__     :Any						    = (size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''      )
													return resize(UpperCamelCase_							,							size=UpperCamelCase_							,							resample=UpperCamelCase_							,							data_format=UpperCamelCase_							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       np.ndarray							,							UpperCamelCase_ :       Dict[str, int]							,							UpperCamelCase_ :       Optional[Union[str, ChannelDimension]] = None							,							**UpperCamelCase_ :       List[Any]							,							)     ->				np.ndarray:
													SCREAMING_SNAKE_CASE__     :int						    = get_size_dict(UpperCamelCase_      )
													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(UpperCamelCase_							,							size=(size['height'], size['width'])							,							data_format=UpperCamelCase_							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       np.ndarray							,							UpperCamelCase_ :       float							,							UpperCamelCase_ :       Optional[Union[str, ChannelDimension]] = None							,							**UpperCamelCase_ :       Tuple      )     ->				np.ndarray:
													return rescale(UpperCamelCase_							,							scale=UpperCamelCase_							,							data_format=UpperCamelCase_							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       np.ndarray							,							UpperCamelCase_ :       Union[float, List[float]]							,							UpperCamelCase_ :       Union[float, List[float]]							,							UpperCamelCase_ :       Optional[Union[str, ChannelDimension]] = None							,							**UpperCamelCase_ :       List[str]							,							)     ->				np.ndarray:
													return normalize(UpperCamelCase_							,							mean=UpperCamelCase_							,							std=UpperCamelCase_							,							data_format=UpperCamelCase_							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       ImageInput							,							UpperCamelCase_ :       Optional[bool] = None							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       PILImageResampling = None							,							UpperCamelCase_ :       bool = None							,							UpperCamelCase_ :       int = None							,							UpperCamelCase_ :       Optional[bool] = None							,							UpperCamelCase_ :       Optional[float] = None							,							UpperCamelCase_ :       Optional[bool] = None							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = None							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = None							,							UpperCamelCase_ :       Optional[Union[str, TensorType]] = None							,							UpperCamelCase_ :       Union[str, ChannelDimension] = ChannelDimension.FIRST							,							**UpperCamelCase_ :       str							,							)     ->				BatchFeature:
													SCREAMING_SNAKE_CASE__     :str						    = do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :int						    = do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :int						    = do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = get_size_dict(UpperCamelCase_							,							param_name='crop_size'							,							default_to_square=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :Dict						    = rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :str						    = image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :Dict						    = get_size_dict(UpperCamelCase_      )

													if not is_batched(UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = [images]

													if not valid_images(UpperCamelCase_      ):
																				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.'      )

													# All transformations expect numpy arrays.
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = [to_numpy_array(UpperCamelCase_      ) for image in images]

													if do_resize:
																				SCREAMING_SNAKE_CASE__     :int						    = [self.resize(image=UpperCamelCase_							,							size=UpperCamelCase_							,							resample=UpperCamelCase_      ) for image in images]

													if do_center_crop:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [self.center_crop(image=UpperCamelCase_							,							size=UpperCamelCase_      ) for image in images]

													if do_rescale:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = [self.rescale(image=UpperCamelCase_							,							scale=UpperCamelCase_      ) for image in images]

													if do_normalize:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = [self.normalize(image=UpperCamelCase_							,							mean=UpperCamelCase_							,							std=UpperCamelCase_      ) for image in images]

													SCREAMING_SNAKE_CASE__     :Any						    = [to_channel_dimension_format(UpperCamelCase_							,							UpperCamelCase_      ) for image in images]

													SCREAMING_SNAKE_CASE__     :str						    = {'pixel_values': images}
													return BatchFeature(data=UpperCamelCase_							,							tensor_type=UpperCamelCase_      )







'''simple docstring'''







import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def __init__(    self :       Any							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       int = 32							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Union[int, float] = 1 / 2_55							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       int=7							,							UpperCamelCase_ :       Dict=30							,							UpperCamelCase_ :       Tuple=4_00							,							UpperCamelCase_ :       List[Any]=3							,							)     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = parent
													SCREAMING_SNAKE_CASE__     :Tuple						    = do_resize
													SCREAMING_SNAKE_CASE__     :List[Any]						    = size if size is not None else {'shortest_edge': 2_88}
													SCREAMING_SNAKE_CASE__     :str						    = size_divisor
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_rescale
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rescale_factor
													SCREAMING_SNAKE_CASE__     :str						    = do_normalize
													SCREAMING_SNAKE_CASE__     :int						    = do_center_crop
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = image_mean
													SCREAMING_SNAKE_CASE__     :str						    = image_std
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = do_pad
													SCREAMING_SNAKE_CASE__     :Tuple						    = batch_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = num_channels
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = min_resolution
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_resolution




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str=False      )     ->				Optional[int]:
													if not batched:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.size['shortest_edge']
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = image_inputs[0]
																				if isinstance(UpperCamelCase_							,							Image.Image      ):
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = image.size
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = image.shape[1], image.shape[2]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = size / min(UpperCamelCase_							,							UpperCamelCase_      )
																				if h < w:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = size, scale * w
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = scale * h, size

																				SCREAMING_SNAKE_CASE__     :Any						    = int((13_33 / 8_00) * size      )
																				if max(UpperCamelCase_							,							UpperCamelCase_      ) > max_size:
																											SCREAMING_SNAKE_CASE__     :Tuple						    = max_size / max(UpperCamelCase_							,							UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = newh * scale
																											SCREAMING_SNAKE_CASE__     :Any						    = neww * scale

																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = int(newh + 0.5      ), int(neww + 0.5      )
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = []
																				for image in image_inputs:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.get_expected_values([image]      )
																											expected_values.append((expected_height, expected_width)      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[0]      )[0]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[1]      )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= BridgeTowerImageProcessor if is_vision_available() else None




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = BridgeTowerImageProcessingTester(self      )




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				Any:
													return self.image_processor_tester.prepare_image_processor_dict()




						def      __lowerCamelCase       (    self :       int      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_mean'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'image_std'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_normalize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'do_resize'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size_divisor'      )      )




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[str]:
													pass




						def      __lowerCamelCase       (    self :       int      )     ->				Dict:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :Any						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							Image.Image      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :Any						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Tuple:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :Tuple						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							numpify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							np.ndarray      )

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

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_							,							torchify=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							torch.Tensor      )

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

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)






