


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 	snake_case_       (unittest.TestCase				):
					def __init__(				self       :Optional[Any]      ,__snake_case       :str      ,__snake_case       :Any=7      ,__snake_case       :Dict=3      ,__snake_case       :str=18      ,__snake_case       :int=30      ,__snake_case       :Tuple=4_00      ,__snake_case       :Optional[Any]=True      ,__snake_case       :Any=None      ,__snake_case       :int=True      ,__snake_case       :Tuple=None      ,__snake_case       :List[str]=True      ,__snake_case       :Any=[0.5, 0.5, 0.5]      ,__snake_case       :Union[str, Any]=[0.5, 0.5, 0.5]      ,__snake_case       :List[Any]=False      ,) ->   Tuple:
												a__					=					size if size is not None else {'height': 20, 'width': 20}
												a__					=					crop_size if crop_size is not None else {'height': 18, 'width': 18}
												a__					=					parent
												a__					=					batch_size
												a__					=					num_channels
												a__					=					image_size
												a__					=					min_resolution
												a__					=					max_resolution
												a__					=					do_resize
												a__					=					size
												a__					=					do_center_crop
												a__					=					crop_size
												a__					=					do_normalize
												a__					=					image_mean
												a__					=					image_std
												a__					=					do_reduce_labels



					def 			lowerCamelCase__(				self       :Any				) ->   Dict:
												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       (			):
							a__					=					load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

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

							return image, map






def 							__lowercase       (			):
							a__					=					load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

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

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







@require_torch
@require_vision
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      BeitImageProcessor if is_vision_available() else None
					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[int]:
												a__					=					BeitImageProcessingTester(self				)
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   Optional[int]:
												return self.image_processor_tester.prepare_image_processor_dict()
					def 			lowerCamelCase__(				self       :int				) ->   List[str]:
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												self.assertTrue(hasattr(__snake_case      ,'do_resize'				)				)
												self.assertTrue(hasattr(__snake_case      ,'size'				)				)
												self.assertTrue(hasattr(__snake_case      ,'do_center_crop'				)				)
												self.assertTrue(hasattr(__snake_case      ,'center_crop'				)				)
												self.assertTrue(hasattr(__snake_case      ,'do_normalize'				)				)
												self.assertTrue(hasattr(__snake_case      ,'image_mean'				)				)
												self.assertTrue(hasattr(__snake_case      ,'image_std'				)				)
					def 			lowerCamelCase__(				self       :List[str]				) ->   List[Any]:
												a__					=					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      ,__snake_case				)

												a__					=					self.image_processing_class.from_dict(
												    self.image_processor_dict      ,size=42      ,crop_size=84      ,reduce_labels=__snake_case				)
												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      ,__snake_case				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[Any]:
												pass
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Union[str, Any]:
												# Initialize image_processing
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												# create random PIL images
												a__					=					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
												a__					=					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
												a__					=					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.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )      ,)
					def 			lowerCamelCase__(				self       :Tuple				) ->   List[Any]:
												# Initialize image_processing
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												a__					=					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
												a__					=					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
												a__					=					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.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )      ,)
					def 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												# Initialize image_processing
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												a__					=					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
												a__					=					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
												a__					=					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.crop_size['height'],
												        self.image_processor_tester.crop_size['width'],
												    )      ,)
					def 			lowerCamelCase__(				self       :Dict				) ->   List[str]:
												# Initialize image_processing
												a__					=					self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												a__					=					prepare_image_inputs(self.image_processor_tester      ,equal_resolution=__snake_case      ,torchify=__snake_case				)
												a__					=					[]
												for image in image_inputs:
																			self.assertIsInstance(__snake_case      ,torch.Tensor				)
																			maps.append(torch.zeros(image.shape[-2:]				).long()				)

												# Test not batched input
												a__					=					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
												a__					=					image_processing(__snake_case      ,__snake_case      ,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)
												a__				, a__					=					prepare_semantic_single_inputs()

												a__					=					image_processing(__snake_case      ,__snake_case      ,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)
												a__				, a__					=					prepare_semantic_batch_inputs()

												a__					=					image_processing(__snake_case      ,__snake_case      ,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 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
												# Initialize image_processing
												a__					=					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
												a__				, a__					=					prepare_semantic_single_inputs()
												a__					=					image_processing(__snake_case      ,__snake_case      ,return_tensors='pt'				)
												self.assertTrue(encoding['labels'].min().item() >= 0				)
												self.assertTrue(encoding['labels'].max().item() <= 1_50				)

												a__					=					True
												a__					=					image_processing(__snake_case      ,__snake_case      ,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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


snake_case							:	str					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					b.T
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=1       )
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=0       )
							a__					=					np.matmul(__lowerCAmelCase ,   __lowerCAmelCase       )
							a__					=					aa[:, None] - 2 * ab + ba[None, :]
							return d






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    str       ):
							a__					=					x.reshape(-1 ,   3       )
							a__					=					squared_euclidean_distance(__lowerCAmelCase ,   __lowerCAmelCase       )
							return np.argmin(__lowerCAmelCase ,   axis=1       )







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[Any]   				=      ['''pixel_values''']
					def __init__(				self       :Dict      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :bool = True      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,**__snake_case       :Optional[int]      ,) ->   None:
												super().__init__(**__snake_case				)
												a__					=					size if size is not None else {'height': 2_56, 'width': 2_56}
												a__					=					get_size_dict(__snake_case				)
												a__					=					np.array(__snake_case				) if clusters is not None else None
												a__					=					do_resize
												a__					=					size
												a__					=					resample
												a__					=					do_normalize
												a__					=					do_color_quantize
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Dict[str, int]      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,**__snake_case       :Any      ,) ->   np.ndarray:
												a__					=					get_size_dict(__snake_case				)
												if "height" not in size or "width" not in size:
																			raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'				)
												return resize(
												    __snake_case      ,size=(size['height'], size['width'])      ,resample=__snake_case      ,data_format=__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,) ->   np.ndarray:
												a__					=					rescale(image=__snake_case      ,scale=1 / 1_27.5      ,data_format=__snake_case				)
												a__					=					image - 1
												return image



					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :ImageInput      ,__snake_case       :bool = None      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = None      ,__snake_case       :bool = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,**__snake_case       :List[str]      ,) ->   PIL.Image.Image:
												a__					=					do_resize if do_resize is not None else self.do_resize
												a__					=					size if size is not None else self.size
												a__					=					get_size_dict(__snake_case				)
												a__					=					resample if resample is not None else self.resample
												a__					=					do_normalize if do_normalize is not None else self.do_normalize
												a__					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												a__					=					clusters if clusters is not None else self.clusters
												a__					=					np.array(__snake_case				)

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

												if do_color_quantize and clusters is None:
																			raise ValueError('Clusters must be specified if do_color_quantize is True.'				)

												# All transformations expect numpy arrays.
												a__					=					[to_numpy_array(__snake_case				) for image in images]

												if do_resize:
																			a__					=					[self.resize(image=__snake_case      ,size=__snake_case      ,resample=__snake_case				) for image in images]

												if do_normalize:
																			a__					=					[self.normalize(image=__snake_case				) for image in images]

												if do_color_quantize:
																			a__					=					[to_channel_dimension_format(__snake_case      ,ChannelDimension.LAST				) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			a__					=					np.array(__snake_case				)
																			a__					=					color_quantize(__snake_case      ,__snake_case				).reshape(images.shape[:-1]				)

																			# flatten to (batch_size, height*width)
																			a__					=					images.shape[0]
																			a__					=					images.reshape(__snake_case      ,-1				)

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			a__					=					list(__snake_case				)
												else:
																			a__					=					[to_channel_dimension_format(__snake_case      ,__snake_case				) for image in images]

												a__					=					{'input_ids': images}
												return BatchFeature(data=__snake_case      ,tensor_type=__snake_case				)




