

import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor

class 				__UpperCamelCase				(					unittest.TestCase			):







	def __init__(   self: List[str]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Dict=7  , __UpperCamelCase: Any=3  , __UpperCamelCase: Optional[Any]=18  , __UpperCamelCase: Optional[Any]=30  , __UpperCamelCase: Dict=4_00  , __UpperCamelCase: Optional[Any]=True  , __UpperCamelCase: List[Any]=None  , __UpperCamelCase: List[str]=True  , __UpperCamelCase: List[Any]=None  , __UpperCamelCase: Optional[Any]=True  , __UpperCamelCase: Tuple=[0.5, 0.5, 0.5]  , __UpperCamelCase: Tuple=[0.5, 0.5, 0.5]  , __UpperCamelCase: str=False  , ):

						'''simple docstring'''





						__magic_name__							=    size if size is not None else {'height': 20, 'width': 20}
						__magic_name__							=    crop_size if crop_size is not None else {'height': 18, 'width': 18}
						__magic_name__							=    parent
						__magic_name__							=    batch_size
						__magic_name__							=    num_channels
						__magic_name__							=    image_size
						__magic_name__							=    min_resolution
						__magic_name__							=    max_resolution
						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    do_center_crop
						__magic_name__							=    crop_size
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean
						__magic_name__							=    image_std
						__magic_name__							=    do_reduce_labels







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_reduce_labels": self.do_reduce_labels,
						}




def 					_lowercase     (				)				->	List[str]:




					'''simple docstring'''







					__magic_name__							=    load_dataset('hf-internal-testing/fixtures_ade20k'					,split='test'				)

					__magic_name__							=    Image.open(dataset[0]['file']				)
					__magic_name__							=    Image.open(dataset[1]['file']				)

					return image, map




def 					_lowercase     (				)				->	Union[str, Any]:




					'''simple docstring'''







					__magic_name__							=    load_dataset('hf-internal-testing/fixtures_ade20k'					,split='test'				)

					__magic_name__							=    Image.open(ds[0]['file']				)
					__magic_name__							=    Image.open(ds[1]['file']				)
					__magic_name__							=    Image.open(ds[2]['file']				)
					__magic_name__							=    Image.open(ds[3]['file']				)

					return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						unittest.TestCase			):
	_lowercase  :   Any       				=  BeitImageProcessor if is_vision_available() else None







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    BeitImageProcessingTester(self      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return self.image_processor_tester.prepare_image_processor_dict()







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_resize'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'size'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_center_crop'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'center_crop'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'do_normalize'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'image_mean'      )      )
						self.assertTrue(hasattr(__UpperCamelCase  , 'image_std'      )      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class.from_dict(self.image_processor_dict      )
						self.assertEqual(image_processor.size  , {'height': 20, 'width': 20}      )
						self.assertEqual(image_processor.crop_size  , {'height': 18, 'width': 18}      )
						self.assertEqual(image_processor.do_reduce_labels  , __UpperCamelCase      )

						__magic_name__							=    self.image_processing_class.from_dict(
						    self.image_processor_dict  , size=42  , crop_size=84  , reduce_labels=__UpperCamelCase      )
						self.assertEqual(image_processor.size  , {'height': 42, 'width': 42}      )
						self.assertEqual(image_processor.crop_size  , {'height': 84, 'width': 84}      )
						self.assertEqual(image_processor.do_reduce_labels  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						pass







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase      )
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , Image.Image      )

						# Test not batched input
						__magic_name__							=    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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , 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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						__magic_name__							=    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
						__magic_name__							=    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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , 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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						__magic_name__							=    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
						__magic_name__							=    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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , 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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						__magic_name__							=    prepare_image_inputs(self.image_processor_tester  , equal_resolution=__UpperCamelCase  , torchify=__UpperCamelCase      )
						__magic_name__							=    []
						for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase  , torch.Tensor      )
											maps.append(torch.zeros(image.shape[-2:]      ).long()      )

						# Test not batched input
						__magic_name__							=    image_processing(image_inputs[0]  , maps[0]  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        1,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )

						# Test batched
						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )

						# Test not batched input (PIL images)
						__magic_name__,	__magic_name__							=    prepare_semantic_single_inputs()

						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        1,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )

						# Test batched input (PIL images)
						__magic_name__,	__magic_name__							=    prepare_semantic_batch_inputs()

						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertEqual(
						    encoding['pixel_values'].shape  , (
						        2,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(
						    encoding['labels'].shape  , (
						        2,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )  , )
						self.assertEqual(encoding['labels'].dtype  , torch.long      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    self.image_processing_class(**self.image_processor_dict      )

						# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
						__magic_name__,	__magic_name__							=    prepare_semantic_single_inputs()
						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 1_50      )

						__magic_name__							=    True
						__magic_name__							=    image_processing(__UpperCamelCase  , __UpperCamelCase  , return_tensors='pt'      )
						self.assertTrue(encoding['labels'].min().item() >= 0      )
						self.assertTrue(encoding['labels'].max().item() <= 2_55      )







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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


A__						=							logging.get_logger(__name__)

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Tuple       				=  ["pixel_values"]







	def __init__(   self: Optional[Any]  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: int = 0.9  , __UpperCamelCase: PILImageResampling = PILImageResampling.BICUBIC  , __UpperCamelCase: bool = True  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: Union[int, float] = 1 / 2_55  , __UpperCamelCase: bool = True  , __UpperCamelCase: bool = True  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						super().__init__(**__UpperCamelCase      )
						__magic_name__							=    size if size is not None else {'shortest_edge': 2_24}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    do_resize
						__magic_name__							=    size
						__magic_name__							=    crop_pct
						__magic_name__							=    resample
						__magic_name__							=    do_center_crop
						__magic_name__							=    crop_size
						__magic_name__							=    do_rescale
						__magic_name__							=    rescale_factor
						__magic_name__							=    do_normalize
						__magic_name__							=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__magic_name__							=    image_std if image_std is not None else IMAGENET_DEFAULT_STD







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: Optional[float] = None  , __UpperCamelCase: PILImageResampling = PILImageResampling.BICUBIC  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						if "shortest_edge" not in size and ("height" not in size or "width" not in size):
											raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'      )
						if crop_pct is not None:
											if "shortest_edge" in size:
																__magic_name__							=    int(size['shortest_edge'] / crop_pct      )
											elif "height" in size and "width" in size:
																if size["height"] == size["width"]:
																					__magic_name__							=    int(size['height'] / crop_pct      )
																else:
																					__magic_name__							=    (int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
											else:
																raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase      )      )

											__magic_name__							=    get_resize_output_image_size(__UpperCamelCase  , size=__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						else:
											if "shortest_edge" in size:
																__magic_name__							=    get_resize_output_image_size(__UpperCamelCase  , size=size['shortest_edge']  , default_to_square=__UpperCamelCase      )
											elif "height" in size and "width" in size:
																__magic_name__							=    (size['height'], size['width'])
											else:
																raise ValueError('Invalid size for resize: {}'.format(__UpperCamelCase      )      )

						return resize(__UpperCamelCase  , size=__UpperCamelCase  , resample=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Dict[str, int]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: List[str]  , ):

						'''simple docstring'''





						__magic_name__							=    get_size_dict(__UpperCamelCase      )
						if "height" not in size or "width" not in size:
											raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'      )
						return center_crop(__UpperCamelCase  , size=(size['height'], size['width'])  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[int, float]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						return rescale(__UpperCamelCase  , scale=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: np.ndarray  , __UpperCamelCase: Union[float, List[float]]  , __UpperCamelCase: Union[float, List[float]]  , __UpperCamelCase: Optional[Union[str, ChannelDimension]] = None  , **__UpperCamelCase: Optional[int]  , ):

						'''simple docstring'''





						return normalize(__UpperCamelCase  , mean=__UpperCamelCase  , std=__UpperCamelCase  , data_format=__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: ImageInput  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: int = None  , __UpperCamelCase: PILImageResampling = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Dict[str, int] = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: float = None  , __UpperCamelCase: bool = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[float, List[float]]] = None  , __UpperCamelCase: Optional[Union[str, TensorType]] = None  , __UpperCamelCase: ChannelDimension = ChannelDimension.FIRST  , **__UpperCamelCase: Any  , ):

						'''simple docstring'''





						__magic_name__							=    do_resize if do_resize is not None else self.do_resize
						__magic_name__							=    crop_pct if crop_pct is not None else self.crop_pct
						__magic_name__							=    resample if resample is not None else self.resample
						__magic_name__							=    do_center_crop if do_center_crop is not None else self.do_center_crop
						__magic_name__							=    do_rescale if do_rescale is not None else self.do_rescale
						__magic_name__							=    rescale_factor if rescale_factor is not None else self.rescale_factor
						__magic_name__							=    do_normalize if do_normalize is not None else self.do_normalize
						__magic_name__							=    image_mean if image_mean is not None else self.image_mean
						__magic_name__							=    image_std if image_std is not None else self.image_std

						__magic_name__							=    size if size is not None else self.size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , default_to_square=__UpperCamelCase      )
						__magic_name__							=    crop_size if crop_size is not None else self.crop_size
						__magic_name__							=    get_size_dict(__UpperCamelCase  , param_name='crop_size'      )

						__magic_name__							=    make_list_of_images(__UpperCamelCase      )

						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 or resample is None:
											raise ValueError('Size and resample must be specified if do_resize is True.'      )

						if do_center_crop and crop_pct is None:
											raise ValueError('Crop_pct 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.'      )

						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.
						__magic_name__							=    [to_numpy_array(__UpperCamelCase      ) for image in images]

						if do_resize:
											__magic_name__							=    [self.resize(image=__UpperCamelCase  , size=__UpperCamelCase  , crop_pct=__UpperCamelCase  , resample=__UpperCamelCase      ) for image in images]

						if do_center_crop:
											__magic_name__							=    [self.center_crop(image=__UpperCamelCase  , size=__UpperCamelCase      ) for image in images]

						if do_rescale:
											__magic_name__							=    [self.rescale(image=__UpperCamelCase  , scale=__UpperCamelCase      ) for image in images]

						if do_normalize:
											__magic_name__							=    [self.normalize(image=__UpperCamelCase  , mean=__UpperCamelCase  , std=__UpperCamelCase      ) for image in images]

						__magic_name__							=    [to_channel_dimension_format(__UpperCamelCase  , __UpperCamelCase      ) for image in images]

						__magic_name__							=    {'pixel_values': images}
						return BatchFeature(data=__UpperCamelCase  , tensor_type=__UpperCamelCase      )




