




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        UpperCAmelCase__				:



     '''simple docstring'''
     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=0.02 ,    _lowerCAmelCase=3 ,    _lowerCAmelCase=False ,    _lowerCAmelCase=None ,    ):
            a			   =parent
            a			   =batch_size
            a			   =image_size
            a			   =patch_size
            a			   =num_channels
            a			   =is_training
            a			   =use_labels
            a			   =hidden_size
            a			   =num_hidden_layers
            a			   =backbone_out_indices
            a			   =num_attention_heads
            a			   =intermediate_size
            a			   =hidden_act
            a			   =hidden_dropout_prob
            a			   =attention_probs_dropout_prob
            a			   =initializer_range
            a			   =num_labels
            a			   =scope
            a			   =is_hybrid
            # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
            a			   =(image_size // patch_size) ** 2
            a			   =num_patches + 1
     def        lowerCAmelCase__  (				self	):
            a			   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

            a			   =None
            if self.use_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
     def        lowerCAmelCase__  (				self	):
            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=_lowerCAmelCase ,    initializer_range=self.initializer_range ,    is_hybrid=self.is_hybrid ,    )
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =DPTModel(config=_lowerCAmelCase	)
            model.to(_lowerCAmelCase	)
            model.eval()
            a			   =model(_lowerCAmelCase	)
            self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.seq_length, self.hidden_size)	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =self.num_labels
            a			   =DPTForDepthEstimation(_lowerCAmelCase	)
            model.to(_lowerCAmelCase	)
            model.eval()
            a			   =model(_lowerCAmelCase	)
            self.parent.assertEqual(result.predicted_depth.shape ,    (self.batch_size, self.image_size, self.image_size)	)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =self.num_labels
            a			   =DPTForSemanticSegmentation(_lowerCAmelCase	)
            model.to(_lowerCAmelCase	)
            model.eval()
            a			   =model(_lowerCAmelCase ,    labels=_lowerCAmelCase	)
            self.parent.assertEqual(
                result.logits.shape ,    (self.batch_size, self.num_labels, self.image_size, self.image_size)	)






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



@require_torch
class        UpperCAmelCase__				(							UpperCAmelCase__      ,		UpperCAmelCase__      ,		unittest.TestCase				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					int     					=    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE      :					List[Any]     					=    (
         {
             "depth-estimation": DPTForDepthEstimation,
             "feature-extraction": DPTModel,
             "image-segmentation": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE      :					Dict     					=    False
     _SCREAMING_SNAKE_CASE      :					Any     					=    False
     _SCREAMING_SNAKE_CASE      :					str     					=    False
     def        lowerCAmelCase__  (				self	):
            a			   =DPTModelTester(self	)
            a			   =ConfigTester(self ,    config_class=_lowerCAmelCase ,    has_text_modality=_lowerCAmelCase ,    hidden_size=37	)
     def        lowerCAmelCase__  (				self	):
            self.config_tester.run_common_tests()
     @unittest.skip(reason="""DPT does not use inputs_embeds"""	)
     def        lowerCAmelCase__  (				self	):
            pass
     def        lowerCAmelCase__  (				self	):
            a   ,      a			   =self.model_tester.prepare_config_and_inputs_for_common()

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

            for model_class in self.all_model_classes:
                   a			   =model_class(_lowerCAmelCase	)
                   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] ,    _lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_depth_estimation(*_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for model_class in self.all_model_classes:
                   if model_class.__name__ == "DPTForDepthEstimation":
                          continue

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

                   if model_class in get_values(_lowerCAmelCase	):
                          continue

                   a			   =model_class(_lowerCAmelCase	)
                   model.to(_lowerCAmelCase	)
                   model.train()
                   a			   =self._prepare_for_class(_lowerCAmelCase ,    _lowerCAmelCase ,    return_labels=_lowerCAmelCase	)
                   a			   =model(**_lowerCAmelCase	).loss
                   loss.backward()
     def        lowerCAmelCase__  (				self	):
            for model_class in self.all_model_classes:
                   if model_class.__name__ == "DPTForDepthEstimation":
                          continue

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

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

            a			   =_config_zero_init(_lowerCAmelCase	)
            for model_class in self.all_model_classes:
                   a			   =model_class(config=_lowerCAmelCase	)
                   # Skip the check for the backbone
                   a			   =[]
                   for name, module in model.named_modules():
                          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                                 a			   =[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        lowerCAmelCase__  (				self	):
            pass






     @slow
     def        lowerCAmelCase__  (				self	):
            for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   a			   =DPTModel.from_pretrained(_lowerCAmelCase	)
                   self.assertIsNotNone(_lowerCAmelCase	)





def 							lowerCamelCase    (							)->			Optional[Any]:

       """simple docstring"""




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






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



     '''simple docstring'''
     def        lowerCAmelCase__  (				self	):
            a			   =DPTImageProcessor.from_pretrained("""Intel/dpt-large"""	)
            a			   =DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""	).to(_lowerCAmelCase	)

            a			   =prepare_img()
            a			   =image_processor(images=_lowerCAmelCase ,    return_tensors="""pt"""	).to(_lowerCAmelCase	)

            # forward pass
            with torch.no_grad():
                   a			   =model(**_lowerCAmelCase	)
                   a			   =outputs.predicted_depth

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

            a			   =torch.tensor(
                [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]	).to(_lowerCAmelCase	)

            self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] ,    _lowerCAmelCase ,    atol=1E-4	)	)
     def        lowerCAmelCase__  (				self	):
            a			   =DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""	)
            a			   =DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""	).to(_lowerCAmelCase	)

            a			   =prepare_img()
            a			   =image_processor(images=_lowerCAmelCase ,    return_tensors="""pt"""	).to(_lowerCAmelCase	)

            # forward pass
            with torch.no_grad():
                   a			   =model(**_lowerCAmelCase	)

            # verify the logits
            a			   =torch.Size((1, 150, 480, 480)	)
            self.assertEqual(outputs.logits.shape ,    _lowerCAmelCase	)

            a			   =torch.tensor(
                [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]	).to(_lowerCAmelCase	)

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






     def        lowerCAmelCase__  (				self	):
            a			   =DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""	)
            a			   =DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""	).to(_lowerCAmelCase	)

            a			   =prepare_img()
            a			   =image_processor(images=_lowerCAmelCase ,    return_tensors="""pt"""	).to(_lowerCAmelCase	)

            # forward pass
            with torch.no_grad():
                   a			   =model(**_lowerCAmelCase	)

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

            a			   =image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase ,    target_sizes=[(500, 300)]	)
            a			   =torch.Size((500, 300)	)
            self.assertEqual(segmentation[0].shape ,    _lowerCAmelCase	)

            a			   =image_processor.post_process_semantic_segmentation(outputs=_lowerCAmelCase	)
            a			   =torch.Size((480, 480)	)
            self.assertEqual(segmentation[0].shape ,    _lowerCAmelCase	)






from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      list[int | float]      )->			int | float:

       """simple docstring"""




       if len(UpperCAmelCase_      ) == 0:
              raise ValueError("""find_min() arg is an empty sequence"""      )
       a			   =nums[0]
       for num in nums:
              a			   =min(UpperCAmelCase_   ,       UpperCAmelCase_      )
       return min_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
