



'''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 							a__(      unittest.TestCase			):







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







				def     lowercase_ (   self				:						int			):
						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__ (		):
		a		:      int       =  load_dataset('hf-internal-testing/fixtures_ade20k'				,    split='test'       )

		a		:      Any       =  Image.open(dataset[0]['file']       )
		a		:      Optional[Any]       =  Image.open(dataset[1]['file']       )

		return image, map
def    lowerCamelCase__ (		):
		a		:      Optional[Any]       =  load_dataset('hf-internal-testing/fixtures_ade20k'				,    split='test'       )

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

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

@require_torch
@require_vision
class 							a__(      lowerCamelCase__	,				unittest.TestCase			):
				lowercase__				=					BeitImageProcessor if is_vision_available() else None







				def     lowercase_ (   self				:						str			):
						a		:      List[str]       =  BeitImageProcessingTester(self			)







				@property
				def     lowercase_ (   self				:						str			):
						return self.image_processor_tester.prepare_image_processor_dict()







				def     lowercase_ (   self				:						str			):
						a		:      str       =  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     lowercase_ (   self				:						str			):
						a		:      int       =  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		:      str       =  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     lowercase_ (   self				:						Optional[int]			):
						pass







				def     lowercase_ (   self				:						Dict			):
						# Initialize image_processing
						a		:      Any       =  self.image_processing_class(**self.image_processor_dict			)
						# create random PIL images
						a		:      Any       =  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		:      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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )		,    )

						# Test batched
						a		:      List[str]       =  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     lowercase_ (   self				:						Dict			):
						# Initialize image_processing
						a		:      Optional[int]       =  self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						a		:      Optional[int]       =  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		:      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
						a		:      Tuple       =  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     lowercase_ (   self				:						str			):
						# Initialize image_processing
						a		:      Tuple       =  self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						a		:      List[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
						a		:      Optional[int]       =  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		:      Tuple       =  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     lowercase_ (   self				:						Tuple			):
						# Initialize image_processing
						a		:      Any       =  self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						a		:      Optional[int]       =  prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__snake_case		,    torchify=__snake_case			)
						a		:      Dict       =  []
						for image in image_inputs:
								self.assertIsInstance(__snake_case		,    torch.Tensor			)
								maps.append(torch.zeros(image.shape[-2:]			).long()			)

						# Test not batched input
						a		:      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
						a		:      Union[str, Any]       =  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		:      Optional[Any]       =  prepare_semantic_single_inputs()

						a		:      Any       =  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		:      List[Any]       =  prepare_semantic_batch_inputs()

						a		:      Union[str, 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.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     lowercase_ (   self				:						Union[str, Any]			):
						# Initialize image_processing
						a		:      List[Any]       =  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		:      Any       =  prepare_semantic_single_inputs()
						a		:      Tuple       =  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		:      Optional[Any]       =  True
						a		:      Any       =  image_processing(__snake_case		,    __snake_case		,    return_tensors='pt'			)
						self.assertTrue(encoding['labels'].min().item() >= 0			)
						self.assertTrue(encoding['labels'].max().item() <= 2_55			)




'''simple docstring'''






from ..utils import DummyObject, requires_backends

class 							a__(      metaclass=lowerCamelCase__			):
    lowercase__				=					["""torch""", """torchsde"""]







    def __init__(   self				:						Any		,    *__snake_case				:						List[str]		,    **__snake_case				:						Tuple			):
      requires_backends(self		,    ['torch', 'torchsde']			)







    @classmethod
    def     lowercase_ (   cls				:						List[Any]		,    *__snake_case				:						str		,    **__snake_case				:						List[Any]			):
      requires_backends(cls		,    ['torch', 'torchsde']			)







    @classmethod
    def     lowercase_ (   cls				:						Dict		,    *__snake_case				:						Optional[Any]		,    **__snake_case				:						str			):
      requires_backends(cls		,    ['torch', 'torchsde']			)