"""simple docstring"""




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
			import torch
			from torch import nn

			from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
			from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DPTImageProcessor

class    a_   :




					def __init__(						self    :  List[Any]	,    snake_case__    :  Tuple	,    snake_case__    :  Optional[Any]=2	,    snake_case__    :  Union[str, Any]=32	,    snake_case__    :  int=16	,    snake_case__    :  Union[str, Any]=3	,    snake_case__    :  Tuple=True	,    snake_case__    :  int=True	,    snake_case__    :  Optional[Any]=32	,    snake_case__    :  int=4	,    snake_case__    :  Dict=[0, 1, 2, 3]	,    snake_case__    :  List[Any]=4	,    snake_case__    :  Tuple=37	,    snake_case__    :  Dict="gelu"	,    snake_case__    :  str=0.1	,    snake_case__    :  Dict=0.1	,    snake_case__    :  Optional[int]=0.02	,    snake_case__    :  Tuple=3	,    snake_case__    :  Any=[1, 384, 24, 24]	,    snake_case__    :  Optional[int]=True	,    snake_case__    :  Any=None	,    ):
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = patch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = is_training
							lowerCAmelCase__   = use_labels
							lowerCAmelCase__   = hidden_size
							lowerCAmelCase__   = num_hidden_layers
							lowerCAmelCase__   = backbone_out_indices
							lowerCAmelCase__   = num_attention_heads
							lowerCAmelCase__   = intermediate_size
							lowerCAmelCase__   = hidden_act
							lowerCAmelCase__   = hidden_dropout_prob
							lowerCAmelCase__   = attention_probs_dropout_prob
							lowerCAmelCase__   = initializer_range
							lowerCAmelCase__   = num_labels
							lowerCAmelCase__   = backbone_featmap_shape
							lowerCAmelCase__   = scope
							lowerCAmelCase__   = is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase__   = (image_size // patch_size) ** 2
							lowerCAmelCase__   = num_patches + 1




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

							lowerCAmelCase__   = self.get_config()

							return config, pixel_values, labels




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = {
							    """global_padding""": """same""",
							    """layer_type""": """bottleneck""",
							    """depths""": [3, 4, 9],
							    """out_features""": ["""stage1""", """stage2""", """stage3"""],
							    """embedding_dynamic_padding""": True,
							    """hidden_sizes""": [96, 192, 384, 768],
							    """num_groups""": 2,
							}

							return DPTConfig(
							    image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    backbone_out_indices=self.backbone_out_indices	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    is_decoder=snake_case__	,    initializer_range=self.initializer_range	,    is_hybrid=self.is_hybrid	,    backbone_config=snake_case__	,    backbone_featmap_shape=self.backbone_featmap_shape	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Any	,    snake_case__    :  int	,    snake_case__    :  Union[str, Any]  ):
							lowerCAmelCase__   = DPTModel(config=snake_case__  )
							model.to(snake_case__  )
							model.eval()
							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  List[Any]	,    snake_case__    :  Any	,    snake_case__    :  List[str]  ):
							lowerCAmelCase__   = self.num_labels
							lowerCAmelCase__   = DPTForDepthEstimation(snake_case__  )
							model.to(snake_case__  )
							model.eval()
							lowerCAmelCase__   = model(snake_case__  )
							self.parent.assertEqual(result.predicted_depth.shape	,    (self.batch_size, self.image_size, self.image_size)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]	,    snake_case__    :  str	,    snake_case__    :  Dict	,    snake_case__    :  Any  ):
							lowerCAmelCase__   = self.num_labels
							lowerCAmelCase__   = DPTForSemanticSegmentation(snake_case__  )
							model.to(snake_case__  )
							model.eval()
							lowerCAmelCase__   = model(snake_case__	,    labels=snake_case__  )
							self.parent.assertEqual(
							    result.logits.shape	,    (self.batch_size, self.num_labels, self.image_size, self.image_size)  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.prepare_config_and_inputs()
							lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = config_and_inputs
							lowerCAmelCase__   = {"""pixel_values""": pixel_values}
							return config, inputs_dict

@require_torch
class    a_   (  __UpperCamelCase			,		__UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[Any]      =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					UpperCamelCase_			:						Union[str, Any]      =     (
					    {
					        "depth-estimation": DPTForDepthEstimation,
					        "feature-extraction": DPTModel,
					        "image-segmentation": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase_			:						Optional[int]      =     False
					UpperCamelCase_			:						Any      =     False
					UpperCamelCase_			:						List[Any]      =     False




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__   = DPTModelTester(self  )
							lowerCAmelCase__   = ConfigTester(self	,    config_class=snake_case__	,    has_text_modality=snake_case__	,    hidden_size=37  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							self.config_tester.run_common_tests()




					@unittest.skip(reason="""DPT does not use inputs_embeds"""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							pass




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							lowerCAmelCase__ ,       lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowerCAmelCase__   = model_class(snake_case__  )
									self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)  )
									lowerCAmelCase__   = model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(snake_case__	,    nn.Linear  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__ ,       lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs_for_common()

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

									lowerCAmelCase__   = ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]	,    snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							for model_class in self.all_model_classes:
									if model_class.__name__ == "DPTForDepthEstimation":
											continue

									lowerCAmelCase__ ,       lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase__   = True

									if model_class in get_values(snake_case__  ):
											continue

									lowerCAmelCase__   = model_class(snake_case__  )
									model.to(snake_case__  )
									model.train()
									lowerCAmelCase__   = self._prepare_for_class(snake_case__	,    snake_case__	,    return_labels=snake_case__  )
									lowerCAmelCase__   = model(**snake_case__  ).loss
									loss.backward()




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							for model_class in self.all_model_classes:
									if model_class.__name__ == "DPTForDepthEstimation":
											continue

									lowerCAmelCase__ ,       lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase__   = False
									lowerCAmelCase__   = True

									if model_class in get_values(snake_case__  ) or not model_class.supports_gradient_checkpointing:
											continue
									lowerCAmelCase__   = model_class(snake_case__  )
									model.to(snake_case__  )
									model.gradient_checkpointing_enable()
									model.train()
									lowerCAmelCase__   = self._prepare_for_class(snake_case__	,    snake_case__	,    return_labels=snake_case__  )
									lowerCAmelCase__   = model(**snake_case__  ).loss
									loss.backward()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__ ,       lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs_for_common()

							lowerCAmelCase__   = _config_zero_init(snake_case__  )
							for model_class in self.all_model_classes:
									lowerCAmelCase__   = model_class(config=snake_case__  )
									# Skip the check for the backbone
									lowerCAmelCase__   = []
									for name, module in model.named_modules():
											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
													lowerCAmelCase__   = [F"""{name}.{key}""" for key in module.state_dict().keys()]
													break

									for name, param in model.named_parameters():
											if param.requires_grad:
													if name in backbone_params:
															continue
													self.assertIn(
													    ((param.data.mean() * 1E9).round() / 1E9).item()	,    [0.0, 1.0]	,    msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""	,    )




					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							pass




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
									lowerCAmelCase__   = DPTModel.from_pretrained(snake_case__  )
									self.assertIsNotNone(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[Any]  ):
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							lowerCAmelCase__ ,       lowerCAmelCase__   = self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__   = """add"""
							with self.assertRaises(snake_case__  ):
									lowerCAmelCase__   = DPTForDepthEstimation(snake_case__  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__   = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
		return image






@require_torch
@require_vision
@slow
class    a_   (  unittest.TestCase      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""  )
							lowerCAmelCase__   = DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""  ).to(snake_case__  )

							lowerCAmelCase__   = prepare_img()
							lowerCAmelCase__   = image_processor(images=snake_case__	,    return_tensors="""pt"""  ).to(snake_case__  )

							# forward pass
							with torch.no_grad():
									lowerCAmelCase__   = model(**snake_case__  )
									lowerCAmelCase__   = outputs.predicted_depth

							# verify the predicted depth
							lowerCAmelCase__   = torch.Size((1, 384, 384)  )
							self.assertEqual(predicted_depth.shape	,    snake_case__  )

							lowerCAmelCase__   = torch.tensor(
							    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]  ).to(snake_case__  )

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100	,    snake_case__	,    atol=1E-4  )  )





"""simple docstring"""




import unittest

import numpy as np
import requests

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

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			__lowerCAmelCase     :				int    				=    False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class    a_   (  unittest.TestCase      ):




					def __init__(						self    :  Tuple	,    snake_case__    :  Any	,    snake_case__    :  Union[str, Any]=7	,    snake_case__    :  Optional[int]=3	,    snake_case__    :  int=18	,    snake_case__    :  Dict=30	,    snake_case__    :  Optional[Any]=400	,    snake_case__    :  Tuple=None	,    snake_case__    :  List[Any]=True	,    snake_case__    :  Any=True	,    snake_case__    :  Dict=None	,    ):
							lowerCAmelCase__   = size if size is not None else {"""height""": 20, """width""": 20}
							lowerCAmelCase__   = parent
							lowerCAmelCase__   = batch_size
							lowerCAmelCase__   = num_channels
							lowerCAmelCase__   = image_size
							lowerCAmelCase__   = min_resolution
							lowerCAmelCase__   = max_resolution
							lowerCAmelCase__   = size
							lowerCAmelCase__   = do_normalize
							lowerCAmelCase__   = do_convert_rgb
							lowerCAmelCase__   = [512, 1024, 2048, 4096]
							lowerCAmelCase__   = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
							lowerCAmelCase__   = Image.open(requests.get(snake_case__	,    stream=snake_case__  ).raw  ).convert("""RGB"""  )
							return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						Optional[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = self.image_processor_tester.prepare_dummy_image()

							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							lowerCAmelCase__   = 2048

							lowerCAmelCase__   = image_processor(snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  )
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,    torch.tensor(0.0606  )	,    atol=1E-3	,    rtol=1E-3  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							lowerCAmelCase__   = True

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(snake_case__  ):
											lowerCAmelCase__   = image_processor(
											    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches

									lowerCAmelCase__   = """Hello"""

									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__	,    header_text=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowerCAmelCase__   = 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  )

							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * self.image_processor_tester.num_channels
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,		reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,		)
@require_torch
@require_vision
class    a_   (  __UpperCamelCase			,		unittest.TestCase      ):
					UpperCamelCase_			:						List[Any]      =     PixaStructImageProcessor if is_vision_available() else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = PixaStructImageProcessingTester(self	,    num_channels=4  )
							lowerCAmelCase__   = 3




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							return self.image_processor_tester.prepare_image_processor_dict()




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(snake_case__	,    """do_normalize"""  )  )
							self.assertTrue(hasattr(snake_case__	,    """do_convert_rgb"""  )  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							# Initialize image_processor
							lowerCAmelCase__   = self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowerCAmelCase__   = 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
							lowerCAmelCase__   = (
							    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
							    * (self.image_processor_tester.num_channels - 1)
							) + 2

							for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCAmelCase__   = image_processor(
									    image_inputs[0]	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (1, max_patch, expected_hidden_dim)	,    )

									# Test batched
									lowerCAmelCase__   = image_processor(
									    snake_case__	,    return_tensors="""pt"""	,    max_patches=snake_case__  ).flattened_patches
									self.assertEqual(
									    encoded_images.shape	,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,    )




