"""simple docstring"""






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				):




					"""simple docstring"""

					def __init__(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	Optional[Any]=7    ,lowerCamelCase__   :	Union[str, Any]=3    ,lowerCamelCase__   :	int=18    ,lowerCamelCase__   :	Any=30    ,lowerCamelCase__   :	str=400    ,lowerCamelCase__   :	Any=True    ,lowerCamelCase__   :	Tuple=None    ,lowerCamelCase__   :	List[Any]=True    ,lowerCamelCase__   :	List[Any]=None    ,lowerCamelCase__   :	Dict=True    ,lowerCamelCase__   :	int=[0.5, 0.5, 0.5]    ,lowerCamelCase__   :	Tuple=[0.5, 0.5, 0.5]    ,lowerCamelCase__   :	Optional[Any]=False    ,):
									UpperCAmelCase__             =							size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__             =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							num_channels
									UpperCAmelCase__             =							image_size
									UpperCAmelCase__             =							min_resolution
									UpperCAmelCase__             =							max_resolution
									UpperCAmelCase__             =							do_resize
									UpperCAmelCase__             =							size
									UpperCAmelCase__             =							do_center_crop
									UpperCAmelCase__             =							crop_size
									UpperCAmelCase__             =							do_normalize
									UpperCAmelCase__             =							image_mean
									UpperCAmelCase__             =							image_std
									UpperCAmelCase__             =							do_reduce_labels







					def 		__lowerCAmelCase						(				self   :	str			):
									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   a_						(					):
				UpperCAmelCase__             =							load_dataset('hf-internal-testing/fixtures_ade20k'					,			split='test'       )

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

				return image, map



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

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

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







@require_torch
@require_vision
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = BeitImageProcessor if is_vision_available() else None







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							BeitImageProcessingTester(self			)







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'do_resize'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'size'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'do_center_crop'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'center_crop'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'do_normalize'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'image_mean'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'image_std'			)			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							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    ,lowerCamelCase__			)

									UpperCAmelCase__             =							self.image_processing_class.from_dict(
									    self.image_processor_dict    ,size=42    ,crop_size=84    ,reduce_labels=lowerCamelCase__			)
									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    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									pass







					def 		__lowerCAmelCase						(				self   :	str			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__			)
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,Image.Image			)

									# Test not batched input
									UpperCAmelCase__             =							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
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,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			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__    ,numpify=lowerCamelCase__			)
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,np.ndarray			)

									# Test not batched input
									UpperCAmelCase__             =							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
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,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   :	Any			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__    ,torchify=lowerCamelCase__			)
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,torch.Tensor			)

									# Test not batched input
									UpperCAmelCase__             =							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
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,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   :	Optional[int]			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__    ,torchify=lowerCamelCase__			)
									UpperCAmelCase__             =							[]
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,torch.Tensor			)
													maps.append(torch.zeros(image.shape[-2:]			).long()			)

									# Test not batched input
									UpperCAmelCase__             =							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() <= 255			)

									# Test batched
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,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() <= 255			)

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

									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,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() <= 255			)

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

									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,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() <= 255			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									# Initialize image_processing
									UpperCAmelCase__             =							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
									UpperCAmelCase__      ,	UpperCAmelCase__             =							prepare_semantic_single_inputs()
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,return_tensors='pt'			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 150			)

									UpperCAmelCase__             =							True
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,return_tensors='pt'			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 255			)




"""simple docstring"""






import argparse


lowerCAmelCase__  :  List[str]						     =	'docs/source/_static/js/custom.js'



def   a_						(					lowerCamelCase       ):
				with open(lowerCamelCase					,			encoding='utf-8'					,			newline='\n'       ) as f:
								UpperCAmelCase__             =							f.readlines()
				UpperCAmelCase__             =							0

				# First let's put the right version
				while not lines[index].startswith('const stableVersion ='       ):
								index += 1
				UpperCAmelCase__             =							f'''const stableVersion = "v{version}"\n'''

				# Then update the dictionary
				while not lines[index].startswith('const versionMapping = {'       ):
								index += 1

				# We go until the end
				while not lines[index].startswith('}'       ):
								index += 1
				# We add the new version at the end
				lines[index - 1] += f'''    "v{version}": "v{version}",\n'''

				with open(lowerCamelCase					,			'w'					,			encoding='utf-8'					,			newline='\n'       ) as f:
								f.writelines(lowerCamelCase       )


if __name__ == "__main__":
						lowerCAmelCase__  :  List[Any]						     =	argparse.ArgumentParser()
						parser.add_argument('--version', help='Release version.')
						lowerCAmelCase__  :  Optional[int]						     =	parser.parse_args()
						update_custom_js(args.version)



