
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 					_SCREAMING_SNAKE_CASE  :
    def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=2		,	lowerCamelCase=32		,	lowerCamelCase=16		,	lowerCamelCase=3		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase=32		,	lowerCamelCase=4		,	lowerCamelCase=[0, 1, 2, 3]		,	lowerCamelCase=4		,	lowerCamelCase=37		,	lowerCamelCase="gelu"		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=0.0_2		,	lowerCamelCase=3		,	lowerCamelCase=[1, 3_84, 24, 24]		,	lowerCamelCase=True		,	lowerCamelCase=None		,	):
        snake_case__						=				parent
        snake_case__						=				batch_size
        snake_case__						=				image_size
        snake_case__						=				patch_size
        snake_case__						=				num_channels
        snake_case__						=				is_training
        snake_case__						=				use_labels
        snake_case__						=				hidden_size
        snake_case__						=				num_hidden_layers
        snake_case__						=				backbone_out_indices
        snake_case__						=				num_attention_heads
        snake_case__						=				intermediate_size
        snake_case__						=				hidden_act
        snake_case__						=				hidden_dropout_prob
        snake_case__						=				attention_probs_dropout_prob
        snake_case__						=				initializer_range
        snake_case__						=				num_labels
        snake_case__						=				backbone_featmap_shape
        snake_case__						=				scope
        snake_case__						=				is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        snake_case__						=				(image_size // patch_size) ** 2
        snake_case__						=				num_patches + 1
    def A_			(			self ):
        snake_case__						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

        snake_case__						=				self.get_config()

        return config, pixel_values, labels
    def A_			(			self ):
        snake_case__						=				{
            "global_padding": "same",
            "layer_type": "bottleneck",
            "depths": [3, 4, 9],
            "out_features": ["stage1", "stage2", "stage3"],
            "embedding_dynamic_padding": True,
            "hidden_sizes": [96, 1_92, 3_84, 7_68],
            "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=A_		,	initializer_range=self.initializer_range		,	is_hybrid=self.is_hybrid		,	backbone_config=A_		,	backbone_featmap_shape=self.backbone_featmap_shape		,	)
    def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
        snake_case__						=				DPTModel(config=A_ )
        model.to(A_ )
        model.eval()
        snake_case__						=				model(A_ )
        self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size) )
    def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
        snake_case__						=				self.num_labels
        snake_case__						=				DPTForDepthEstimation(A_ )
        model.to(A_ )
        model.eval()
        snake_case__						=				model(A_ )
        self.parent.assertEqual(result.predicted_depth.shape		,	(self.batch_size, self.image_size, self.image_size) )
    def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
        snake_case__						=				self.num_labels
        snake_case__						=				DPTForSemanticSegmentation(A_ )
        model.to(A_ )
        model.eval()
        snake_case__						=				model(A_		,	labels=A_ )
        self.parent.assertEqual(
            result.logits.shape		,	(self.batch_size, self.num_labels, self.image_size, self.image_size) )



    def A_			(			self ):
        snake_case__						=				self.prepare_config_and_inputs()
        snake_case__						=				config_and_inputs
        snake_case__						=				{"pixel_values": pixel_values}
        return config, inputs_dict





@require_torch
class 					_SCREAMING_SNAKE_CASE  (      __snake_case       ,					__snake_case       ,					unittest.TestCase ):
    _A						:		Tuple    	=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    _A						:		List[str]    	=     (
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    _A						:		Optional[Any]    	=     False
    _A						:		str    	=     False
    _A						:		Dict    	=     False
    def A_			(			self ):
        snake_case__						=				DPTModelTester(self )
        snake_case__						=				ConfigTester(self		,	config_class=A_		,	has_text_modality=A_		,	hidden_size=37 )
    def A_			(			self ):
        self.config_tester.run_common_tests()
    @unittest.skip(reason="DPT does not use inputs_embeds" )
    def A_			(			self ):
        pass
    def A_			(			self ):
        snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

            snake_case__						=				["pixel_values"]
            self.assertListEqual(arg_names[:1]		,	A_ )
    def A_			(			self ):
        snake_case__						=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_ )
    def A_			(			self ):
        snake_case__						=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*A_ )
    def A_			(			self ):
        snake_case__						=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*A_ )
    def A_			(			self ):




        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__						=				True

            if model_class in get_values(A_ ):
                continue

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




        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__						=				False
            snake_case__						=				True

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

        snake_case__						=				_config_zero_init(A_ )
        for model_class in self.all_model_classes:
            snake_case__						=				model_class(config=A_ )
            # Skip the check for the backbone
            snake_case__						=				[]
            for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                    snake_case__						=				[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 A_			(			self ):
        pass
    @slow
    def A_			(			self ):




        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
            snake_case__						=				DPTModel.from_pretrained(A_ )
            self.assertIsNotNone(A_ )



    def A_			(			self ):
        snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
        snake_case__						=				"add"
        with self.assertRaises(A_ ):
            snake_case__						=				DPTForDepthEstimation(A_ )





def 	SCREAMING_SNAKE_CASE__    (      ):
    snake_case__						=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
    return image





@require_torch
@require_vision
@slow
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
    def A_			(			self ):
        snake_case__						=				DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
        snake_case__						=				DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(A_ )

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

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

        # verify the predicted depth
        snake_case__						=				torch.Size((1, 3_84, 3_84) )
        self.assertEqual(predicted_depth.shape		,	A_ )

        snake_case__						=				torch.tensor(
            [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]] ).to(A_ )

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00		,	A_		,	atol=1e-4 ) )





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[False] * len(__lowerCAmelCase							)
    snake_case__						=				[-1] * len(__lowerCAmelCase							)

    def dfs(__lowerCAmelCase    ,		__lowerCAmelCase							):
        snake_case__						=				True
        snake_case__						=				c
        for u in graph[v]:
            if not visited[u]:
                dfs(__lowerCAmelCase    ,		1 - c							)

    for i in range(len(__lowerCAmelCase							)							):
        if not visited[i]:
            dfs(__lowerCAmelCase    ,		0							)

    for i in range(len(__lowerCAmelCase							)							):
        for j in graph[i]:
            if color[i] == color[j]:
                return False

    return True


# Adjacency list of graph
__magic_name__            =  {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}





print(check_bipartite_dfs(graph))



