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  :

	'''simple docstring'''



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

	def 	__UpperCAmelCase(  self       ):
			__A				:       Tuple			    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

			__A				:       Dict			    = self.get_config()

			return config, pixel_values, labels

	def 	__UpperCAmelCase(  self       ):
			__A				:       str			    = {
			    "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=lowercase_				,					initializer_range=self.initializer_range				,					is_hybrid=self.is_hybrid				,					backbone_config=lowercase_				,					backbone_featmap_shape=self.backbone_featmap_shape				,					)

	def 	__UpperCAmelCase(  self				,					__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase       ):
			__A				:       Dict			    = DPTModel(config=lowercase_       )
			model.to(lowercase_       )
			model.eval()
			__A				:       Tuple			    = model(lowercase_       )
			self.parent.assertEqual(result.last_hidden_state.shape				,					(self.batch_size, self.seq_length, self.hidden_size)       )

	def 	__UpperCAmelCase(  self				,					__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase       ):
			__A				:       List[Any]			    = self.num_labels
			__A				:       Union[str, Any]			    = DPTForDepthEstimation(lowercase_       )
			model.to(lowercase_       )
			model.eval()
			__A				:       Any			    = model(lowercase_       )
			self.parent.assertEqual(result.predicted_depth.shape				,					(self.batch_size, self.image_size, self.image_size)       )

	def 	__UpperCAmelCase(  self				,					__UpperCAmelCase				,					__UpperCAmelCase				,					__UpperCAmelCase       ):
			__A				:       int			    = self.num_labels
			__A				:       Tuple			    = DPTForSemanticSegmentation(lowercase_       )
			model.to(lowercase_       )
			model.eval()
			__A				:       List[Any]			    = model(lowercase_				,					labels=lowercase_       )
			self.parent.assertEqual(
			    result.logits.shape				,					(self.batch_size, self.num_labels, self.image_size, self.image_size)       )







	def 	__UpperCAmelCase(  self       ):
			__A				:       List[str]			    = self.prepare_config_and_inputs()
			__A    ,  __A    ,  __A				:       Optional[int]			    = config_and_inputs
			__A				:       int			    = {"pixel_values": pixel_values}
			return config, inputs_dict


@require_torch
class _a  (			_lowerCamelCase       ,				_lowerCamelCase       ,				unittest.TestCase   ):

	'''simple docstring'''



	lowerCamelCase_       :     Dict          =			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
	lowerCamelCase_       :     Optional[int]          =			(
	    {
	        """depth-estimation""": DPTForDepthEstimation,
	        """feature-extraction""": DPTModel,
	        """image-segmentation""": DPTForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	lowerCamelCase_       :     List[str]          =			False
	lowerCamelCase_       :     List[str]          =			False
	lowerCamelCase_       :     Optional[int]          =			False

	def 	__UpperCAmelCase(  self       ):
			__A				:       List[str]			    = DPTModelTester(self       )
			__A				:       str			    = ConfigTester(self				,					config_class=lowercase_				,					has_text_modality=lowercase_				,					hidden_size=37       )

	def 	__UpperCAmelCase(  self       ):
			self.config_tester.run_common_tests()

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

	def 	__UpperCAmelCase(  self       ):
			__A    ,  __A				:       Any			    = self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A				:       Optional[Any]			    = model_class(lowercase_       )
					self.assertIsInstance(model.get_input_embeddings()				,					(nn.Module)       )
					__A				:       Any			    = model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(lowercase_				,					nn.Linear       )       )

	def 	__UpperCAmelCase(  self       ):
			__A    ,  __A				:       str			    = self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					__A				:       List[str]			    = model_class(lowercase_       )
					__A				:       Optional[int]			    = inspect.signature(model.forward       )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					__A				:       Dict			    = [*signature.parameters.keys()]

					__A				:       str			    = ["pixel_values"]
					self.assertListEqual(arg_names[:1]				,					lowercase_       )

	def 	__UpperCAmelCase(  self       ):
			__A				:       Dict			    = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowercase_       )

	def 	__UpperCAmelCase(  self       ):
			__A				:       Optional[int]			    = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*lowercase_       )

	def 	__UpperCAmelCase(  self       ):
			__A				:       str			    = self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_       )

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

					__A    ,  __A				:       List[Any]			    = self.model_tester.prepare_config_and_inputs_for_common()
					__A				:       Optional[Any]			    = True

					if model_class in get_values(lowercase_       ):
							continue

					__A				:       Optional[Any]			    = model_class(lowercase_       )
					model.to(lowercase_       )
					model.train()
					__A				:       Tuple			    = self._prepare_for_class(lowercase_				,					lowercase_				,					return_labels=lowercase_       )
					__A				:       Tuple			    = model(**lowercase_       ).loss
					loss.backward()

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

					__A    ,  __A				:       int			    = self.model_tester.prepare_config_and_inputs_for_common()
					__A				:       str			    = False
					__A				:       List[str]			    = True

					if model_class in get_values(lowercase_       ) or not model_class.supports_gradient_checkpointing:
							continue
					__A				:       List[str]			    = model_class(lowercase_       )
					model.to(lowercase_       )
					model.gradient_checkpointing_enable()
					model.train()
					__A				:       Any			    = self._prepare_for_class(lowercase_				,					lowercase_				,					return_labels=lowercase_       )
					__A				:       Union[str, Any]			    = model(**lowercase_       ).loss
					loss.backward()

	def 	__UpperCAmelCase(  self       ):
			__A    ,  __A				:       Tuple			    = self.model_tester.prepare_config_and_inputs_for_common()

			__A				:       List[Any]			    = _config_zero_init(lowercase_       )
			for model_class in self.all_model_classes:
					__A				:       List[str]			    = model_class(config=lowercase_       )
					# Skip the check for the backbone
					__A				:       Optional[int]			    = []
					for name, module in model.named_modules():
							if module.__class__.__name__ == "DPTViTHybridEmbeddings":
									__A				:       int			    = [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 	__UpperCAmelCase(  self       ):
			pass

	@slow
	def 	__UpperCAmelCase(  self       ):
			for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
					__A				:       Dict			    = DPTModel.from_pretrained(lowercase_       )
					self.assertIsNotNone(lowercase_       )







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



def        lowerCamelCase_       (    )				->			str:
		__A				:       Union[str, Any]			    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
		return image





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

	'''simple docstring'''



	def 	__UpperCAmelCase(  self       ):
			__A				:       Optional[int]			    = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"       )
			__A				:       int			    = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"       ).to(lowercase_       )

			__A				:       str			    = prepare_img()
			__A				:       Union[str, Any]			    = image_processor(images=lowercase_				,					return_tensors="pt"       ).to(lowercase_       )

			# forward pass
			with torch.no_grad():
					__A				:       List[str]			    = model(**lowercase_       )
					__A				:       Any			    = outputs.predicted_depth

			# verify the predicted depth
			__A				:       Union[str, Any]			    = torch.Size((1, 384, 384)       )
			self.assertEqual(predicted_depth.shape				,					lowercase_       )

			__A				:       Optional[Any]			    = torch.tensor(
			    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]       ).to(lowercase_       )

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











'''simple docstring'''

from PIL import Image


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Image:

					'''simple docstring'''

					def brightness(__UpperCAmelCase      ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''      )
					return img.point(__UpperCAmelCase      )


if __name__ == "__main__":
							# Load image
							with Image.open('image_data/lena.jpg') as img:
														# Change brightness to 100
														a    :							int										=    change_brightness(img, 100)
														brigt_img.save('image_data/lena_brightness.png', format='png')

