





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     lowerCamelCase:
     '''simple docstring'''


     def __init__(						self							,    snake_case_							,    snake_case_=2							,    snake_case_=32							,    snake_case_=16							,    snake_case_=3							,    snake_case_=True							,    snake_case_=True							,    snake_case_=32							,    snake_case_=4							,    snake_case_=[0, 1, 2, 3]							,    snake_case_=4							,    snake_case_=37							,    snake_case_="gelu"							,    snake_case_=0.1							,    snake_case_=0.1							,    snake_case_=0.02							,    snake_case_=3							,    snake_case_=False							,    snake_case_=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=snake_case_							,    initializer_range=self.initializer_range							,    is_hybrid=self.is_hybrid							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       DPTModel(config=snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       model(snake_case_       )
         self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       self.num_labels
         _A    =       DPTForDepthEstimation(snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       model(snake_case_       )
         self.parent.assertEqual(result.predicted_depth.shape							,    (self.batch_size, self.image_size, self.image_size)       )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_       ):
         _A    =       self.num_labels
         _A    =       DPTForSemanticSegmentation(snake_case_       )
         model.to(snake_case_       )
         model.eval()
         _A    =       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 					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     lowerCamelCase(	__snake_case ,   __snake_case ,   unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     __magic_name__        = (
         {
             'depth-estimation': DPTForDepthEstimation,
             'feature-extraction': DPTModel,
             'image-segmentation': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __magic_name__        = False
     __magic_name__        = False
     __magic_name__        = False




     def 					lowerCAmelCase__		(						self       ):
         _A    =       DPTModelTester(self       )
         _A    =       ConfigTester(self							,    config_class=snake_case_							,    has_text_modality=snake_case_							,    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(snake_case_       )
             self.assertIsInstance(model.get_input_embeddings()							,    (nn.Module)       )
             _A    =       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case_							,    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(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]							,    snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*snake_case_       )




     def 					lowerCAmelCase__		(						self       ):
         _A    =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_       )




     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(snake_case_       ):
                 continue

             _A    =       model_class(snake_case_       )
             model.to(snake_case_       )
             model.train()
             _A    =       self._prepare_for_class(snake_case_							,    snake_case_							,    return_labels=snake_case_       )
             _A    =       model(**snake_case_       ).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(snake_case_       ) or not model_class.supports_gradient_checkpointing:
                 continue
             _A    =       model_class(snake_case_       )
             model.to(snake_case_       )
             model.gradient_checkpointing_enable()
             model.train()
             _A    =       self._prepare_for_class(snake_case_							,    snake_case_							,    return_labels=snake_case_       )
             _A    =       model(**snake_case_       ).loss
             loss.backward()




     def 					lowerCAmelCase__		(						self       ):
         _A, _A    =       self.model_tester.prepare_config_and_inputs_for_common()

         _A    =       _config_zero_init(snake_case_       )
         for model_class in self.all_model_classes:
             _A    =       model_class(config=snake_case_       )
             # 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(snake_case_       )
             self.assertIsNotNone(snake_case_       )





def 							__lowerCAmelCase(					)       ->   int:




    """simple docstring"""




    _A    =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
    return image







@require_torch
@require_vision
@slow
class     lowerCamelCase(	unittest.TestCase  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         _A    =       DPTImageProcessor.from_pretrained('Intel/dpt-large'       )
         _A    =       DPTForDepthEstimation.from_pretrained('Intel/dpt-large'       ).to(snake_case_       )

         _A    =       prepare_img()
         _A    =       image_processor(images=snake_case_							,    return_tensors='pt'       ).to(snake_case_       )

         # forward pass
         with torch.no_grad():
             _A    =       model(**snake_case_       )
             _A    =       outputs.predicted_depth

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

         _A    =       torch.tensor(
             [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]       ).to(snake_case_       )

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




     def 					lowerCAmelCase__		(						self       ):
         _A    =       DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'       )
         _A    =       DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'       ).to(snake_case_       )

         _A    =       prepare_img()
         _A    =       image_processor(images=snake_case_							,    return_tensors='pt'       ).to(snake_case_       )

         # forward pass
         with torch.no_grad():
             _A    =       model(**snake_case_       )

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

         _A    =       torch.tensor(
             [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]       ).to(snake_case_       )

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




     def 					lowerCAmelCase__		(						self       ):
         _A    =       DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'       )
         _A    =       DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'       ).to(snake_case_       )

         _A    =       prepare_img()
         _A    =       image_processor(images=snake_case_							,    return_tensors='pt'       ).to(snake_case_       )

         # forward pass
         with torch.no_grad():
             _A    =       model(**snake_case_       )

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

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

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












import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       abs(predict - actual							)
    _A    =       difference.mean()

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)

    _A    =       square_diff.mean()
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()
    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       np.log(predict + 1							)
    _A    =       np.log(actual + 1							)

    _A    =       log_predict - log_actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()

    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    # print(numerator, denumerator)
    _A    =       float(_SCREAMING_SNAKE_CASE							) / denumerator * 100

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




    return np.mean(np.array(_SCREAMING_SNAKE_CASE							) == np.array(_SCREAMING_SNAKE_CASE							)							)





