



import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
 from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor

class 					_A (			__UpperCAmelCase					):



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


           '''simple docstring'''

           __a		 =       self.config_class(**self.inputs_dict)
           self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''hidden_sizes'''))
           self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''neck_hidden_sizes'''))
           self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''num_attention_heads'''))


class 					_A :



       def __init__(	self							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  List[str]=13  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=32  ,	__SCREAMING_SNAKE_CASE							:  int=2  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=3  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=640  ,	__SCREAMING_SNAKE_CASE							:  str=4  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]="silu"  ,	__SCREAMING_SNAKE_CASE							:  int=3  ,	__SCREAMING_SNAKE_CASE							:  Tuple=32  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0.1  ,	__SCREAMING_SNAKE_CASE							:  str=0.1  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0.1  ,	__SCREAMING_SNAKE_CASE							:  Any=0.02  ,	__SCREAMING_SNAKE_CASE							:  Dict=True  ,	__SCREAMING_SNAKE_CASE							:  Dict=True  ,	__SCREAMING_SNAKE_CASE							:  Dict=10  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	):


           '''simple docstring'''

           __a		 =       parent
           __a		 =       batch_size
           __a		 =       image_size
           __a		 =       patch_size
           __a		 =       num_channels
           __a		 =       last_hidden_size
           __a		 =       num_attention_heads
           __a		 =       hidden_act
           __a		 =       conv_kernel_size
           __a		 =       output_stride
           __a		 =       hidden_dropout_prob
           __a		 =       attention_probs_dropout_prob
           __a		 =       classifier_dropout_prob
           __a		 =       use_labels
           __a		 =       is_training
           __a		 =       num_labels
           __a		 =       initializer_range
           __a		 =       scope



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

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

           __a		 =       None
           __a		 =       None
           if self.use_labels:
               __a		 =       ids_tensor([self.batch_size]  ,	self.num_labels)
               __a		 =       ids_tensor([self.batch_size, self.image_size, self.image_size]  ,	self.num_labels)

           __a		 =       self.get_config()

           return config, pixel_values, labels, pixel_labels



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           return MobileViTConfig(
               image_size=self.image_size  ,	patch_size=self.patch_size  ,	num_channels=self.num_channels  ,	num_attention_heads=self.num_attention_heads  ,	hidden_act=self.hidden_act  ,	conv_kernel_size=self.conv_kernel_size  ,	output_stride=self.output_stride  ,	hidden_dropout_prob=self.hidden_dropout_prob  ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,	classifier_dropout_prob=self.classifier_dropout_prob  ,	initializer_range=self.initializer_range  ,	)



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


           '''simple docstring'''

           __a		 =       MobileViTModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.last_hidden_state.shape  ,	(
                   self.batch_size,
                   self.last_hidden_size,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )  ,	)



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           __a		 =       self.num_labels
           __a		 =       MobileViTForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.num_labels))



       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           __a		 =       self.num_labels
           __a		 =       MobileViTForSemanticSegmentation(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a		 =       model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.logits.shape  ,	(
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )  ,	)
           __a		 =       model(__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(
               result.logits.shape  ,	(
                   self.batch_size,
                   self.num_labels,
                   self.image_size // self.output_stride,
                   self.image_size // self.output_stride,
               )  ,	)





       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

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


@require_torch
class 					_A (			__UpperCAmelCase   ,__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					str		      =		(
           (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       UpperCamelCase__						:					List[Any]		      =		(
           {
               '''feature-extraction''': MobileViTModel,
               '''image-classification''': MobileViTForImageClassification,
               '''image-segmentation''': MobileViTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       UpperCamelCase__						:					Tuple		      =		False
       UpperCamelCase__						:					Any		      =		False
       UpperCamelCase__						:					Tuple		      =		False
       UpperCamelCase__						:					Optional[Any]		      =		False



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       MobileViTModelTester(self)
           __a		 =       MobileViTConfigTester(self  ,	config_class=__SCREAMING_SNAKE_CASE  ,	has_text_modality=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       @unittest.skip(reason='''MobileViT does not use inputs_embeds''')
       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           pass



       @unittest.skip(reason='''MobileViT does not support input and output embeddings''')
       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           pass



       @unittest.skip(reason='''MobileViT does not output attentions''')
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''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)



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


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           def check_hidden_states_output(__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):
               __a		 =       model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()

               with torch.no_grad():
                   __a		 =       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE))

               __a		 =       outputs.hidden_states

               __a		 =       5
               self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	__SCREAMING_SNAKE_CASE)

               # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
               # with the width and height being successively divided by 2.
               __a		 =       2
               for i in range(len(__SCREAMING_SNAKE_CASE)):
                   self.assertListEqual(
                       list(hidden_states[i].shape[-2:])  ,	[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]  ,	)
                   divisor *= 2

               self.assertEqual(self.model_tester.output_stride  ,	divisor // 2)

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a		 =       True
               check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               __a		 =       True

               check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE)





       @slow
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a		 =       MobileViTModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





def   __snake_case			(    ):
    __a		 =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
    return image

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



       @cached_property
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''') if is_vision_available() else None



       @slow
       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''').to(__SCREAMING_SNAKE_CASE)

           __a		 =       self.default_image_processor
           __a		 =       prepare_img()
           __a		 =       image_processor(images=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a		 =       model(**__SCREAMING_SNAKE_CASE)

           # verify the logits
           __a		 =       torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       torch.tensor([-1.93_64, -1.23_27, -0.46_53]).to(__SCREAMING_SNAKE_CASE)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,	__SCREAMING_SNAKE_CASE  ,	atol=1E-4))



       @slow
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
           __a		 =       model.to(__SCREAMING_SNAKE_CASE)

           __a		 =       MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

           __a		 =       prepare_img()
           __a		 =       image_processor(images=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a		 =       model(**__SCREAMING_SNAKE_CASE)
           __a		 =       outputs.logits

           # verify the logits
           __a		 =       torch.Size((1, 21, 32, 32))
           self.assertEqual(logits.shape  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       torch.tensor(
               [
                   [[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
                   [[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
                   [[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
               ]  ,	device=__SCREAMING_SNAKE_CASE  ,	)

           self.assertTrue(torch.allclose(logits[0, :3, :3, :3]  ,	__SCREAMING_SNAKE_CASE  ,	atol=1E-4))





       @slow
       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')
           __a		 =       model.to(__SCREAMING_SNAKE_CASE)

           __a		 =       MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''')

           __a		 =       prepare_img()
           __a		 =       image_processor(images=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a		 =       model(**__SCREAMING_SNAKE_CASE)

           __a		 =       outputs.logits.detach().cpu()

           __a		 =       image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE  ,	target_sizes=[(50, 60)])
           __a		 =       torch.Size((50, 60))
           self.assertEqual(segmentation[0].shape  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE)
           __a		 =       torch.Size((32, 32))
           self.assertEqual(segmentation[0].shape  ,	__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)





