


'''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  UpperCamelCase__(     unittest.TestCase ):




				def __init__(	self    :				Tuple   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				int=7   ,					lowerCAmelCase    :				List[str]=3   ,					lowerCAmelCase    :				Optional[int]=18   ,					lowerCAmelCase    :				Union[str, Any]=30   ,					lowerCAmelCase    :				List[Any]=400   ,					lowerCAmelCase    :				Any=True   ,					lowerCAmelCase    :				int=None   ,					lowerCAmelCase    :				Union[str, Any]=True   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				Any=True   ,					lowerCAmelCase    :				Optional[int]=[0.5, 0.5, 0.5]   ,					lowerCAmelCase    :				int=[0.5, 0.5, 0.5]   ,					lowerCAmelCase    :				Optional[Any]=False   ,					)->  Union[str, Any]:

										"""simple docstring"""




										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   a__(	self    :				Any   )->  Tuple:

										"""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    lowerCamelCase__   ( ):




						'''simple docstring'''






						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    lowerCamelCase__   ( ):




						'''simple docstring'''






						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  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Union[str, Any]							= BeitImageProcessor if is_vision_available() else None




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			BeitImageProcessingTester(self   )




				@property
				def   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										return self.image_processor_tester.prepare_image_processor_dict()




				def   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										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   a__(	self    :				str   )->  int:

										"""simple docstring"""




										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   a__(	self    :				Tuple   )->  str:

										"""simple docstring"""




										pass




				def   a__(	self    :				Optional[int]   )->  Optional[int]:

										"""simple docstring"""




										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   a__(	self    :				Optional[int]   )->  Tuple:

										"""simple docstring"""




										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   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										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   a__(	self    :				Union[str, Any]   )->  List[str]:

										"""simple docstring"""




										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   a__(	self    :				Dict   )->  int:

										"""simple docstring"""




										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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.generation import DisjunctiveConstraint






@require_torch
class  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 4], [1, 2, 3, 4]]
										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )
										self.assertTrue(isinstance(dc.token_ids   ,					lowerCAmelCase   )   )

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )




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

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2], [1, 2, 3, 4]]

										with self.assertRaises(lowerCAmelCase   ):
																DisjunctiveConstraint(lowerCAmelCase   )  # fails here




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										UpperCAmelCase         =			stepped is True and completed is False and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(3   )
										UpperCAmelCase         =			stepped is True and completed is True and reset is False
										self.assertTrue(lowerCAmelCase   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 3]   )





				def   a__(	self    :				int   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(4   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.current_seq == [1, 2, 4]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(5   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

										dc.reset()

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 3   )
										self.assertTrue(dc.current_seq == [1]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										self.assertTrue(not dc.completed   )
										self.assertTrue(dc.remaining() == 2   )
										self.assertTrue(dc.current_seq == [1, 2]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(5   )
										self.assertTrue(dc.completed   )  # Completed!
										self.assertTrue(dc.remaining() == 0   )
										self.assertTrue(dc.current_seq == [1, 2, 5]   )





