
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 SegformerImageProcessor

class 				_UpperCamelCase (    unittest.TestCase     ):






			'''simple docstring'''

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




			def 					lowerCAmelCase__		(				self       :				Any					):
							return {
							    "do_resize": self.do_resize,
							    "size": self.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__    (  )     ->		List[Any]:
				UpperCamelCase_:							Tuple		=  load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,			split="""test"""  )

				UpperCamelCase_:							List[str]		=  Image.open(dataset[0]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[1]["""file"""]  )

				return image, map







def 				A__    (  )     ->		Union[str, Any]:
				UpperCamelCase_:							int		=  load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,			split="""test"""  )

				UpperCamelCase_:							Optional[Any]		=  Image.open(dataset[0]["""file"""]  )
				UpperCamelCase_:							Union[str, Any]		=  Image.open(dataset[1]["""file"""]  )
				UpperCamelCase_:							int		=  Image.open(dataset[2]["""file"""]  )
				UpperCamelCase_:							str		=  Image.open(dataset[3]["""file"""]  )

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





@require_torch
@require_vision
class 				_UpperCamelCase (    _A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  Union[str, Any]    					=       SegformerImageProcessor if is_vision_available() else None

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							UpperCamelCase_:							List[Any]		=  SegformerImageProcessingTester(self					)

			@property
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return self.image_processor_tester.prepare_image_processor_dict()

			def 					lowerCAmelCase__		(				self       :				str					):
							UpperCamelCase_:							Dict		=  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_normalize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_mean"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_std"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_reduce_labels"""					)					)

			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							int		=  self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size  ,		{"""height""": 30, """width""": 30}					)
							self.assertEqual(image_processor.do_reduce_labels  ,		snake_case_					)

							UpperCamelCase_:							Tuple		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42  ,		reduce_labels=snake_case_					)
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42}					)
							self.assertEqual(image_processor.do_reduce_labels  ,		snake_case_					)

			def 					lowerCAmelCase__		(				self       :				List[str]					):
							pass

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCamelCase_:							Optional[int]		=  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
							UpperCamelCase_:							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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Union[str, Any]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				Tuple					):
							# Initialize image_processing
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCamelCase_:							Union[str, Any]		=  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
							UpperCamelCase_:							Optional[Any]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							List[Any]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				Tuple					):
							# Initialize image_processing
							UpperCamelCase_:							Dict		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							str		=  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
							UpperCamelCase_:							List[Any]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Dict		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				int					):
							# Initialize image_processing
							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							Dict		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		torchify=snake_case_					)
							UpperCamelCase_:							Any		=  []
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		torch.Tensor					)
											maps.append(torch.zeros(image.shape[-2:]					).long()					)

							# Test not batched input
							UpperCamelCase_:							Optional[int]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        1,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.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_:							str		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.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_:							Tuple		=  prepare_semantic_single_inputs()

							UpperCamelCase_:							Dict		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        1,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.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_:							Dict		=  prepare_semantic_batch_inputs()

							UpperCamelCase_:							List[Any]		=  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.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)
							self.assertEqual(
							    encoding["""labels"""].shape  ,		(
							        2,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.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       :				List[str]					):
							# Initialize image_processing
							UpperCamelCase_:							Tuple		=  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_:							int		=  prepare_semantic_single_inputs()
							UpperCamelCase_:							Any		=  image_processing(snake_case_  ,		snake_case_  ,		return_tensors="""pt"""					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 150					)

							UpperCamelCase_:							Dict		=  True
							UpperCamelCase_:							List[str]		=  image_processing(snake_case_  ,		snake_case_  ,		return_tensors="""pt"""					)
							self.assertTrue(encoding["""labels"""].min().item() >= 0					)
							self.assertTrue(encoding["""labels"""].max().item() <= 255					)








import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowerCamelCase_  :       List[Any]					      =     logging.get_logger(__name__)

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				Dict  ,		*snake_case_       :				Optional[int]  ,		**snake_case_       :				List[Any]					):
							warnings.warn(
							    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use MobileNetV2ImageProcessor instead."""  ,		snake_case_  ,		)
							super().__init__(*snake_case_  ,		**snake_case_					)






