



"""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 (
    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


__a     =    logging.get_logger(__name__)


if is_vision_available():
						import PIL
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[Any]						=					["""pixel_values"""]






					def __init__(	self:							Any						,					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:							Any						,					)       ->     None:
											super().__init__(**snake_case						)

											snake_case_			:Optional[Any]						=      size if size is not None else {"""height""": 2_560, """width""": 1_920}
											if isinstance(snake_case						,					(tuple, list)						):
																	# The previous feature extractor size parameter was in (width, height) format
																	snake_case_			:Union[str, Any]						=      size[::-1]
											snake_case_			:Dict						=      get_size_dict(snake_case						)

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






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							np.ndarray						,					snake_case:							Dict[str, int]						,					snake_case:							Optional[Union[str, ChannelDimension]] = None						)       ->     np.ndarray:
											snake_case_			:Optional[Any]						=      get_image_size(snake_case						)
											snake_case_			:List[Any]						=      size["""height"""], size["""width"""]

											if (output_width < output_height and input_width > input_height) or (
											    output_width > output_height and input_width < input_height
											):
																	snake_case_			:int						=      np.rotaa(snake_case						,					3						)

											if data_format is not None:
																	snake_case_			:Optional[Any]						=      to_channel_dimension_format(snake_case						,					snake_case						)

											return image






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

											snake_case_			:Optional[Any]						=      output_width - input_width
											snake_case_			:List[str]						=      output_height - input_height

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

											snake_case_			:str						=      delta_height - pad_top
											snake_case_			:Optional[Any]						=      delta_width - pad_left

											snake_case_			:List[Any]						=      ((pad_top, pad_bottom), (pad_left, pad_right))
											return pad(snake_case						,					snake_case						,					data_format=snake_case						)






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

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

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

											if input_height > input_width:
																	snake_case_			:List[Any]						=      int(input_width * height / input_height						)
											elif input_width > input_height:
																	snake_case_			:str						=      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:							List[str]						,					snake_case:							np.ndarray						,					snake_case:							Dict[str, int]						,					snake_case:							PILImageResampling = PILImageResampling.BICUBIC						,					snake_case:							Optional[Union[str, ChannelDimension]] = None						,					**snake_case:							Tuple						,					)       ->     np.ndarray:
											snake_case_			:List[str]						=      get_size_dict(snake_case						)
											snake_case_			:Union[str, Any]						=      min(size["""height"""]						,					size["""width"""]						)
											snake_case_			:Optional[Any]						=      get_resize_output_image_size(snake_case						,					size=snake_case						,					default_to_square=snake_case						)
											snake_case_			:Optional[Any]						=      resize(snake_case						,					size=snake_case						,					resample=snake_case						,					data_format=snake_case						,					**snake_case						)
											return resized_image






					def        lowerCAmelCase_    (	self:							Tuple						,					snake_case:							np.ndarray						,					snake_case:							Union[int, float]						,					snake_case:							Optional[Union[str, ChannelDimension]] = None						,					**snake_case:							Dict						,					)       ->     Dict:
											return rescale(snake_case						,					scale=snake_case						,					data_format=snake_case						,					**snake_case						)






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					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:							Dict						,					)       ->     np.ndarray:
											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:							Dict						,					)       ->     PIL.Image.Image:
											snake_case_			:Union[str, Any]						=      do_resize if do_resize is not None else self.do_resize
											snake_case_			:int						=      size if size is not None else self.size
											if isinstance(snake_case						,					(tuple, list)						):
																	# Previous feature extractor had size in (width, height) format
																	snake_case_			:Optional[int]						=      size[::-1]
											snake_case_			:List[str]						=      get_size_dict(snake_case						)
											snake_case_			:List[str]						=      resample if resample is not None else self.resample
											snake_case_			:Tuple						=      do_thumbnail if do_thumbnail is not None else self.do_thumbnail
											snake_case_			:Union[str, Any]						=      do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
											snake_case_			:str						=      do_pad if do_pad is not None else self.do_pad
											snake_case_			:Any						=      do_rescale if do_rescale is not None else self.do_rescale
											snake_case_			:int						=      rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_			:int						=      do_normalize if do_normalize is not None else self.do_normalize
											snake_case_			:List[str]						=      image_mean if image_mean is not None else self.image_mean
											snake_case_			:Tuple						=      image_std if image_std is not None else self.image_std

											snake_case_			:Optional[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.
											snake_case_			:Union[str, Any]						=      [to_numpy_array(snake_case						) for image in images]

											if do_align_long_axis:
																	snake_case_			:str						=      [self.align_long_axis(snake_case						,					size=snake_case						) for image in images]

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

											if do_thumbnail:
																	snake_case_			:Union[str, Any]						=      [self.thumbnail(image=snake_case						,					size=snake_case						) for image in images]

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

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

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

											snake_case_			:str						=      [to_channel_dimension_format(snake_case						,					snake_case						) for image in images]

											snake_case_			:str						=      {"""pixel_values""": images}
											return BatchFeature(data=snake_case						,					tensor_type=snake_case						)





"""simple docstring"""
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     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





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






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											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     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					ViTImageProcessor if is_vision_available() else None






					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											snake_case_			:Dict						=      ViTImageProcessingTester(self						)






					@property
					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Optional[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()






					def        lowerCAmelCase_    (	self:							str						)       ->     List[str]:
											snake_case_			:List[str]						=      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:							Dict						)       ->     Union[str, Any]:
											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,					{"""height""": 18, """width""": 18}						)

											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						)
											self.assertEqual(image_processor.size						,					{"""height""": 42, """width""": 42}						)






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     int:
											pass






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											# Initialize image_processing
											snake_case_			:Any						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_			:str						=      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
											snake_case_			: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
											snake_case_			: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:							Optional[Any]						)       ->     Tuple:
											# Initialize image_processing
											snake_case_			:int						=      self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_			:str						=      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
											snake_case_			: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
											snake_case_			: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"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Optional[int]:
											# Initialize image_processing
											snake_case_			:Union[str, Any]						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_			:List[Any]						=      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
											snake_case_			: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
											snake_case_			:List[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"""],
											    )						,					)
