
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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


lowerCamelCase_  :       int					      =     logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			__UpperCamelCase					:  Optional[int]    					=       ["""pixel_values"""]

			def __init__(				self       :				int  ,		snake_case_       :				bool = True  ,		snake_case_       :				Dict[str, int] = None  ,		snake_case_       :				PILImageResampling = PILImageResampling.BILINEAR  ,		snake_case_       :				bool = True  ,		snake_case_       :				bool = False  ,		snake_case_       :				bool = True  ,		snake_case_       :				bool = True  ,		snake_case_       :				Union[int, float] = 1 / 255  ,		snake_case_       :				bool = True  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		**snake_case_       :				List[Any]  ,		):
							super().__init__(**snake_case_					)

							UpperCamelCase_:							Tuple		=  size if size is not None else {"""height""": 2560, """width""": 1920}
							if isinstance(snake_case_  ,		(tuple, list)					):
											# The previous feature extractor size parameter was in (width, height) format
											UpperCamelCase_:							List[str]		=  size[::-1]
							UpperCamelCase_:							Union[str, Any]		=  get_size_dict(snake_case_					)

							UpperCamelCase_:							Optional[Any]		=  do_resize
							UpperCamelCase_:							List[Any]		=  size
							UpperCamelCase_:							int		=  resample
							UpperCamelCase_:							List[str]		=  do_thumbnail
							UpperCamelCase_:							List[str]		=  do_align_long_axis
							UpperCamelCase_:							Dict		=  do_pad
							UpperCamelCase_:							List[str]		=  do_rescale
							UpperCamelCase_:							Any		=  rescale_factor
							UpperCamelCase_:							Tuple		=  do_normalize
							UpperCamelCase_:							Union[str, Any]		=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase_:							Optional[Any]		=  image_std if image_std is not None else IMAGENET_STANDARD_STD

			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None					):
							UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  get_image_size(snake_case_					)
							UpperCamelCase_,      UpperCamelCase_:							Tuple		=  size["""height"""], size["""width"""]

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
											UpperCamelCase_:							Optional[Any]		=  np.rotaa(snake_case_  ,		3					)

							if data_format is not None:
											UpperCamelCase_:							List[Any]		=  to_channel_dimension_format(snake_case_  ,		snake_case_					)

							return image

			def 					lowerCAmelCase__		(				self       :				str  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				bool = False  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		):
							UpperCamelCase_,      UpperCamelCase_:							List[str]		=  size["""height"""], size["""width"""]
							UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  get_image_size(snake_case_					)

							UpperCamelCase_:							Any		=  output_width - input_width
							UpperCamelCase_:							Any		=  output_height - input_height

							if random_padding:
											UpperCamelCase_:							List[str]		=  np.random.randint(low=0  ,		high=delta_height + 1					)
											UpperCamelCase_:							Optional[Any]		=  np.random.randint(low=0  ,		high=delta_width + 1					)
							else:
											UpperCamelCase_:							Tuple		=  delta_height // 2
											UpperCamelCase_:							Optional[int]		=  delta_width // 2

							UpperCamelCase_:							List[Any]		=  delta_height - pad_top
							UpperCamelCase_:							List[str]		=  delta_width - pad_left

							UpperCamelCase_:							int		=  ((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(snake_case_  ,		snake_case_  ,		data_format=snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		*snake_case_       :				str  ,		**snake_case_       :				Union[str, Any]					):
							logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""					)
							return self.pad_image(*snake_case_  ,		**snake_case_					)

			def 					lowerCAmelCase__		(				self       :				List[Any]  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				PILImageResampling = PILImageResampling.BICUBIC  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				Tuple  ,		):
							UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  get_image_size(snake_case_					)
							UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  size["""height"""], size["""width"""]

							# We always resize to the smallest of either the input or output size.
							UpperCamelCase_:							int		=  min(snake_case_  ,		snake_case_					)
							UpperCamelCase_:							str		=  min(snake_case_  ,		snake_case_					)

							if height == input_height and width == input_width:
											return image

							if input_height > input_width:
											UpperCamelCase_:							Dict		=  int(input_width * height / input_height					)
							elif input_width > input_height:
											UpperCamelCase_:							Optional[Any]		=  int(input_height * width / input_width					)

							return resize(
							    snake_case_  ,		size=(height, width)  ,		resample=snake_case_  ,		reducing_gap=2.0  ,		data_format=snake_case_  ,		**snake_case_					)

			def 					lowerCAmelCase__		(				self       :				Any  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Dict[str, int]  ,		snake_case_       :				PILImageResampling = PILImageResampling.BICUBIC  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				str  ,		):
							UpperCamelCase_:							List[Any]		=  get_size_dict(snake_case_					)
							UpperCamelCase_:							List[Any]		=  min(size["""height"""]  ,		size["""width"""]					)
							UpperCamelCase_:							Tuple		=  get_resize_output_image_size(snake_case_  ,		size=snake_case_  ,		default_to_square=snake_case_					)
							UpperCamelCase_:							str		=  resize(snake_case_  ,		size=snake_case_  ,		resample=snake_case_  ,		data_format=snake_case_  ,		**snake_case_					)
							return resized_image

			def 					lowerCAmelCase__		(				self       :				Any  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Union[int, float]  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				Optional[Any]  ,		):
							return rescale(snake_case_  ,		scale=snake_case_  ,		data_format=snake_case_  ,		**snake_case_					)

			def 					lowerCAmelCase__		(				self       :				int  ,		snake_case_       :				np.ndarray  ,		snake_case_       :				Union[float, List[float]]  ,		snake_case_       :				Union[float, List[float]]  ,		snake_case_       :				Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_       :				List[str]  ,		):
							return normalize(snake_case_  ,		mean=snake_case_  ,		std=snake_case_  ,		data_format=snake_case_  ,		**snake_case_					)




			def 					lowerCAmelCase__		(				self       :				Dict  ,		snake_case_       :				ImageInput  ,		snake_case_       :				bool = None  ,		snake_case_       :				Dict[str, int] = None  ,		snake_case_       :				PILImageResampling = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				bool = False  ,		snake_case_       :				bool = None  ,		snake_case_       :				float = None  ,		snake_case_       :				bool = None  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		snake_case_       :				Optional[Union[float, List[float]]] = None  ,		snake_case_       :				Optional[Union[str, TensorType]] = None  ,		snake_case_       :				Optional[ChannelDimension] = ChannelDimension.FIRST  ,		**snake_case_       :				Optional[int]  ,		):
							UpperCamelCase_:							Optional[Any]		=  do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_:							Dict		=  size if size is not None else self.size
							if isinstance(snake_case_  ,		(tuple, list)					):
											# Previous feature extractor had size in (width, height) format
											UpperCamelCase_:							Any		=  size[::-1]
							UpperCamelCase_:							Tuple		=  get_size_dict(snake_case_					)
							UpperCamelCase_:							Union[str, Any]		=  resample if resample is not None else self.resample
							UpperCamelCase_:							List[Any]		=  do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							UpperCamelCase_:							Tuple		=  do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							UpperCamelCase_:							List[Any]		=  do_pad if do_pad is not None else self.do_pad
							UpperCamelCase_:							Union[str, Any]		=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase_:							Optional[int]		=  rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase_:							Dict		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							str		=  image_mean if image_mean is not None else self.image_mean
							UpperCamelCase_:							List[Any]		=  image_std if image_std is not None else self.image_std

							UpperCamelCase_:							Any		=  make_list_of_images(snake_case_					)

							if not valid_images(snake_case_					):
											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_rescale and rescale_factor is None:
											raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

							if do_pad and size is None:
											raise ValueError("""Size must be specified if do_pad 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."""					)

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Union[str, Any]		=  [to_numpy_array(snake_case_					) for image in images]

							if do_align_long_axis:
											UpperCamelCase_:							Optional[Any]		=  [self.align_long_axis(snake_case_  ,		size=snake_case_					) for image in images]

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

							if do_thumbnail:
											UpperCamelCase_:							Dict		=  [self.thumbnail(image=snake_case_  ,		size=snake_case_					) for image in images]

							if do_pad:
											UpperCamelCase_:							List[Any]		=  [self.pad_image(image=snake_case_  ,		size=snake_case_  ,		random_padding=snake_case_					) for image in images]

							if do_rescale:
											UpperCamelCase_:							List[str]		=  [self.rescale(image=snake_case_  ,		scale=snake_case_					) for image in images]

							if do_normalize:
											UpperCamelCase_:							str		=  [self.normalize(image=snake_case_  ,		mean=snake_case_  ,		std=snake_case_					) for image in images]

							UpperCamelCase_:							Union[str, Any]		=  [to_channel_dimension_format(snake_case_  ,		snake_case_					) for image in images]

							UpperCamelCase_:							Any		=  {"""pixel_values""": images}
							return BatchFeature(data=snake_case_  ,		tensor_type=snake_case_					)








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 ViTImageProcessor

class 				_UpperCamelCase (    unittest.TestCase     ):






			'''simple docstring'''

			def __init__(				self       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				List[Any]=7  ,		snake_case_       :				int=3  ,		snake_case_       :				Tuple=18  ,		snake_case_       :				List[Any]=30  ,		snake_case_       :				Tuple=400  ,		snake_case_       :				Dict=True  ,		snake_case_       :				List[str]=None  ,		snake_case_       :				Any=True  ,		snake_case_       :				str=[0.5, 0.5, 0.5]  ,		snake_case_       :				Optional[Any]=[0.5, 0.5, 0.5]  ,		):
							UpperCamelCase_:							int		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Any		=  parent
							UpperCamelCase_:							List[str]		=  batch_size
							UpperCamelCase_:							Union[str, Any]		=  num_channels
							UpperCamelCase_:							str		=  image_size
							UpperCamelCase_:							Optional[int]		=  min_resolution
							UpperCamelCase_:							Optional[Any]		=  max_resolution
							UpperCamelCase_:							Dict		=  do_resize
							UpperCamelCase_:							Union[str, Any]		=  size
							UpperCamelCase_:							Optional[Any]		=  do_normalize
							UpperCamelCase_:							str		=  image_mean
							UpperCamelCase_:							int		=  image_std




			def 					lowerCAmelCase__		(				self       :				Tuple					):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}




@require_torch
@require_vision
class 				_UpperCamelCase (    _A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  str    					=       ViTImageProcessor if is_vision_available() else None

			def 					lowerCAmelCase__		(				self       :				Any					):
							UpperCamelCase_:							Tuple		=  ViTImageProcessingTester(self					)

			@property
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return self.image_processor_tester.prepare_image_processor_dict()

			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_mean"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_std"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""size"""					)					)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18}					)

							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42					)
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42}					)

			def 					lowerCAmelCase__		(				self       :				int					):
							pass

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

							# Test not batched input
							UpperCamelCase_:							Optional[Any]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							List[str]		=  image_processing(snake_case_  ,		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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				str					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCamelCase_:							Tuple		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		numpify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		np.ndarray					)

							# Test not batched input
							UpperCamelCase_:							Any		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Dict		=  image_processing(snake_case_  ,		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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)




			def 					lowerCAmelCase__		(				self       :				Any					):
							# Initialize image_processing
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							Dict		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		torchify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		torch.Tensor					)

							# Test not batched input
							UpperCamelCase_:							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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Any		=  image_processing(snake_case_  ,		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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)






