



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
 import torch

 from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin

class 					_A :



       def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	__SCREAMING_SNAKE_CASE							:  str=None  ,	__SCREAMING_SNAKE_CASE							:  str=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]="resnet50"  ,	__SCREAMING_SNAKE_CASE							:  str=3  ,	__SCREAMING_SNAKE_CASE							:  int=32  ,	__SCREAMING_SNAKE_CASE							:  Tuple=3  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Tuple=True  ,	):


           '''simple docstring'''

           __a		 =       parent
           __a		 =       out_indices if out_indices is not None else [4]
           __a		 =       stage_names
           __a		 =       out_features
           __a		 =       backbone
           __a		 =       batch_size
           __a		 =       image_size
           __a		 =       num_channels
           __a		 =       use_pretrained_backbone
           __a		 =       is_training



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
           __a		 =       self.get_config()

           return config, pixel_values



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           return TimmBackboneConfig(
               image_size=self.image_size  ,	num_channels=self.num_channels  ,	out_features=self.out_features  ,	out_indices=self.out_indices  ,	stage_names=self.stage_names  ,	use_pretrained_backbone=self.use_pretrained_backbone  ,	backbone=self.backbone  ,	)



       def 	_lowerCamelCase    (	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           __a		 =       TimmBackbone(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           with torch.no_grad():
               __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.feature_map[-1].shape  ,	(self.batch_size, model.channels[-1], 14, 14)  ,	)





       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.prepare_config_and_inputs()
           __a ,     __a		 =       config_and_inputs
           __a		 =       {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
@require_timm
class 					_A (			__UpperCAmelCase   ,__UpperCAmelCase   ,__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					Optional[int]		      =		(TimmBackbone,) if is_torch_available() else ()
       UpperCamelCase__						:					Optional[int]		      =		{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
       UpperCamelCase__						:					Optional[int]		      =		False
       UpperCamelCase__						:					List[str]		      =		False
       UpperCamelCase__						:					str		      =		False
       UpperCamelCase__						:					str		      =		False



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       TimmBackboneModelTester(self)
           __a		 =       ConfigTester(self  ,	config_class=__SCREAMING_SNAKE_CASE  ,	has_text_modality=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       '''resnet18'''
           __a		 =       '''microsoft/resnet-18'''

           __a		 =       AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE  ,	use_timm_backbone=__SCREAMING_SNAKE_CASE)
           __a		 =       AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE)

           self.assertEqual(len(timm_model.out_features)  ,	len(transformers_model.out_features))
           self.assertEqual(len(timm_model.stage_names)  ,	len(transformers_model.stage_names))
           self.assertEqual(timm_model.channels  ,	transformers_model.channels)
           # Out indices are set to the last layer by default. For timm models, we don't know
           # the number of layers in advance, so we set it to (-1,), whereas for transformers
           # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
           self.assertEqual(timm_model.out_indices  ,	(-1,))
           self.assertEqual(transformers_model.out_indices  ,	[len(timm_model.stage_names) - 1])

           __a		 =       AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE  ,	use_timm_backbone=__SCREAMING_SNAKE_CASE  ,	out_indices=[1, 2, 3])
           __a		 =       AutoBackbone.from_pretrained(__SCREAMING_SNAKE_CASE  ,	out_indices=[1, 2, 3])

           self.assertEqual(timm_model.out_indices  ,	transformers_model.out_indices)
           self.assertEqual(len(timm_model.out_features)  ,	len(transformers_model.out_features))
           self.assertEqual(timm_model.channels  ,	transformers_model.channels)



       @unittest.skip('''TimmBackbone doesn\'t support feed forward chunking''')
       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           pass



       @unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''')
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           pass



       @unittest.skip('''TimmBackbone initialization is managed on the timm side''')
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           pass



       @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           pass



       @unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           pass



       @unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint''')
       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           pass



       @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           pass



       @unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           pass



       @unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           pass



       @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           pass



       @unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           pass



       @unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.''')
       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           pass



       @unittest.skip('''TimmBackbone doesn\'t support output_attentions.''')
       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           pass



       @unittest.skip('''Safetensors is not supported by timm.''')
       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           pass



       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               __a		 =       inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __a		 =       [*signature.parameters.keys()]

               __a		 =       ['''pixel_values''']
               self.assertListEqual(arg_names[:1]  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()
           __a		 =       True
           __a		 =       self.has_attentions

           # no need to test all models as different heads yield the same functionality
           __a		 =       self.all_model_classes[0]
           __a		 =       model_class(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)

           __a		 =       self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
           __a		 =       model(**__SCREAMING_SNAKE_CASE)
           __a		 =       outputs[0][-1]

           # Encoder-/Decoder-only models
           __a		 =       outputs.hidden_states[0]
           hidden_states.retain_grad()

           if self.has_attentions:
               __a		 =       outputs.attentions[0]
               attentions.retain_grad()

           output.flatten()[0].backward(retain_graph=__SCREAMING_SNAKE_CASE)

           self.assertIsNotNone(hidden_states.grad)

           if self.has_attentions:
               self.assertIsNotNone(attentions.grad)





       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               __a		 =       model(**__SCREAMING_SNAKE_CASE)

               self.assertEqual(len(result.feature_maps)  ,	len(config.out_indices))
               self.assertEqual(len(model.channels)  ,	len(config.out_indices))

               # Check output of last stage is taken if out_features=None, out_indices=None
               __a		 =       copy.deepcopy(__SCREAMING_SNAKE_CASE)
               __a		 =       None
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               __a		 =       model(**__SCREAMING_SNAKE_CASE)

               self.assertEqual(len(result.feature_maps)  ,	1)
               self.assertEqual(len(model.channels)  ,	1)

               # Check backbone can be initialized with fresh weights
               __a		 =       copy.deepcopy(__SCREAMING_SNAKE_CASE)
               __a		 =       False
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()
               __a		 =       model(**__SCREAMING_SNAKE_CASE)










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]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  int=7  ,	__SCREAMING_SNAKE_CASE							:  Tuple=3  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=18  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=30  ,	__SCREAMING_SNAKE_CASE							:  int=400  ,	__SCREAMING_SNAKE_CASE							:  Dict=True  ,	__SCREAMING_SNAKE_CASE							:  str=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=[0.5, 0.5, 0.5]  ,	__SCREAMING_SNAKE_CASE							:  Any=[0.5, 0.5, 0.5]  ,	__SCREAMING_SNAKE_CASE							:  List[str]=False  ,	):


           '''simple docstring'''

           __a		 =       size if size is not None else {'''height''': 20, '''width''': 20}
           __a		 =       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
           __a		 =       parent
           __a		 =       batch_size
           __a		 =       num_channels
           __a		 =       image_size
           __a		 =       min_resolution
           __a		 =       max_resolution
           __a		 =       do_resize
           __a		 =       size
           __a		 =       do_center_crop
           __a		 =       crop_size
           __a		 =       do_normalize
           __a		 =       image_mean
           __a		 =       image_std
           __a		 =       do_reduce_labels





       def 	_lowerCamelCase    (	self							:  str):


           '''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   __snake_case			(    ):
    __a		 =       load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''       )

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

    return image, map





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

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

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

@require_torch
@require_vision
class 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		BeitImageProcessor if is_vision_available() else None



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       BeitImageProcessingTester(self)



       @property
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_resize'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''size'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_center_crop'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''center_crop'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_normalize'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_mean'''))
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_std'''))



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       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  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       self.image_processing_class.from_dict(
               self.image_processor_dict  ,	size=42  ,	crop_size=84  ,	reduce_labels=__SCREAMING_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  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	Image.Image)

           # Test not batched input
           __a		 =       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		 =       image_processing(__SCREAMING_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 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	numpify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	np.ndarray)

           # Test not batched input
           __a		 =       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		 =       image_processing(__SCREAMING_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 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	torchify=__SCREAMING_SNAKE_CASE)
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor)

           # Test not batched input
           __a		 =       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		 =       image_processing(__SCREAMING_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 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           __a		 =       prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	torchify=__SCREAMING_SNAKE_CASE)
           __a		 =       []
           for image in image_inputs:
               self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor)
               maps.append(torch.zeros(image.shape[-2:]).long())

           # Test not batched input
           __a		 =       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
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_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() <= 255)

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

           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_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() <= 255)

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

           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_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() <= 255)





       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       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 ,     __a		 =       prepare_semantic_single_inputs()
           __a		 =       image_processing(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''')
           self.assertTrue(encoding['''labels'''].min().item() >= 0)
           self.assertTrue(encoding['''labels'''].max().item() <= 150)

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





