





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 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

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





										'''simple docstring'''







										_snake_case :			str  						=   size if size is not None else {'height': 20, 'width': 20}
										_snake_case :			Optional[int]  						=   crop_size if crop_size is not None else {'height': 18, 'width': 18}
										_snake_case :			List[Any]  						=   parent
										_snake_case :			Union[str, Any]  						=   batch_size
										_snake_case :			List[Any]  						=   num_channels
										_snake_case :			Optional[int]  						=   image_size
										_snake_case :			Any  						=   min_resolution
										_snake_case :			str  						=   max_resolution
										_snake_case :			int  						=   do_resize
										_snake_case :			Tuple  						=   size
										_snake_case :			Union[str, Any]  						=   do_center_crop
										_snake_case :			int  						=   crop_size
										_snake_case :			Optional[int]  						=   do_normalize
										_snake_case :			Tuple  						=   image_mean
										_snake_case :			Any  						=   image_std
										_snake_case :			Union[str, Any]  						=   do_reduce_labels







						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''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       A__(		):
				_snake_case :			Union[str, Any]  						=   load_dataset('hf-internal-testing/fixtures_ade20k'	,			split='test' )

				_snake_case :			Tuple  						=   Image.open(dataset[0]['file'] )
				_snake_case :			Union[str, Any]  						=   Image.open(dataset[1]['file'] )

				return image, map

def       A__(		):
				_snake_case :			Tuple  						=   load_dataset('hf-internal-testing/fixtures_ade20k'	,			split='test' )

				_snake_case :			Union[str, Any]  						=   Image.open(ds[0]['file'] )
				_snake_case :			Dict  						=   Image.open(ds[1]['file'] )
				_snake_case :			Optional[Any]  						=   Image.open(ds[2]['file'] )
				_snake_case :			Union[str, Any]  						=   Image.open(ds[3]['file'] )

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


@require_torch
@require_vision
class 			lowercase				(							a_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	str								=	BeitImageProcessor if is_vision_available() else None







						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			int  						=   BeitImageProcessingTester(self					)







						@property
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										return self.image_processor_tester.prepare_image_processor_dict()







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   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 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   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_					)

										_snake_case :			int  						=   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 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										pass







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			Dict  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										_snake_case :			Union[str, Any]  						=   prepare_image_inputs(self.image_processor_tester ,				equal_resolution=lowerCamelCase_					)
										for image in image_inputs:
														self.assertIsInstance(lowerCamelCase_ ,				Image.Image					)

										# Test not batched input
										_snake_case :			Dict  						=   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
										_snake_case :			List[Any]  						=   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 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random numpy tensors
										_snake_case :			List[Any]  						=   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
										_snake_case :			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    ) ,				)

										# Test batched
										_snake_case :			Optional[int]  						=   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 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										_snake_case :			Tuple  						=   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
										_snake_case :			Tuple  						=   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
										_snake_case :			List[str]  						=   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 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										_snake_case :			Union[str, Any]  						=   prepare_image_inputs(self.image_processor_tester ,				equal_resolution=lowerCamelCase_ ,				torchify=lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   []
										for image in image_inputs:
														self.assertIsInstance(lowerCamelCase_ ,				torch.Tensor					)
														maps.append(torch.zeros(image.shape[-2:]					).long()					)

										# Test not batched input
										_snake_case :			str  						=   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
										_snake_case :			Optional[int]  						=   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() <= 2_55					)

										# Test not batched input (PIL images)
										_snake_case :			Any  						=   prepare_semantic_single_inputs()

										_snake_case :			Optional[int]  						=   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() <= 2_55					)

										# Test batched input (PIL images)
										_snake_case :			Optional[int]  						=   prepare_semantic_batch_inputs()

										_snake_case :			Any  						=   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() <= 2_55					)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   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
										_snake_case :			str  						=   prepare_semantic_single_inputs()
										_snake_case :			List[Any]  						=   image_processing(lowerCamelCase_ ,				lowerCamelCase_ ,				return_tensors='pt'					)
										self.assertTrue(encoding['labels'].min().item() >= 0					)
										self.assertTrue(encoding['labels'].max().item() <= 1_50					)

										_snake_case :			int  						=   True
										_snake_case :			List[str]  						=   image_processing(lowerCamelCase_ ,				lowerCamelCase_ ,				return_tensors='pt'					)
										self.assertTrue(encoding['labels'].min().item() >= 0					)
										self.assertTrue(encoding['labels'].max().item() <= 2_55					)













lowercase_		:				Tuple				=						'''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_		:				Optional[int]				=						[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_		:				str				=						{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






