

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

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

           SCREAMING_SNAKE_CASE										=       self.get_config()

           return config, pixel_values, labels
      def 	_lowerCAmelCase					(				self					:							str    ):
           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=__SCREAMING_SNAKE_CASE      ,initializer_range=self.initializer_range      ,is_hybrid=self.is_hybrid      ,)
      def 	_lowerCAmelCase					(				self					:							Tuple      ,snake_case					:							str      ,snake_case					:							Tuple      ,snake_case					:							int    ):
           SCREAMING_SNAKE_CASE										=       DPTModel(config=__SCREAMING_SNAKE_CASE    )
           model.to(__SCREAMING_SNAKE_CASE    )
           model.eval()
           SCREAMING_SNAKE_CASE										=       model(__SCREAMING_SNAKE_CASE    )
           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Any      ,snake_case					:							int      ,snake_case					:							Any    ):
           SCREAMING_SNAKE_CASE										=       self.num_labels
           SCREAMING_SNAKE_CASE										=       DPTForDepthEstimation(__SCREAMING_SNAKE_CASE    )
           model.to(__SCREAMING_SNAKE_CASE    )
           model.eval()
           SCREAMING_SNAKE_CASE										=       model(__SCREAMING_SNAKE_CASE    )
           self.parent.assertEqual(result.predicted_depth.shape      ,(self.batch_size, self.image_size, self.image_size)    )
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							str      ,snake_case					:							Dict      ,snake_case					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=       self.num_labels
           SCREAMING_SNAKE_CASE										=       DPTForSemanticSegmentation(__SCREAMING_SNAKE_CASE    )
           model.to(__SCREAMING_SNAKE_CASE    )
           model.eval()
           SCREAMING_SNAKE_CASE										=       model(__SCREAMING_SNAKE_CASE      ,labels=__SCREAMING_SNAKE_CASE    )
           self.parent.assertEqual(
               result.logits.shape      ,(self.batch_size, self.num_labels, self.image_size, self.image_size)    )


      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=       self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=       config_and_inputs
           SCREAMING_SNAKE_CASE										=       {'pixel_values': pixel_values}
           return config, inputs_dict







@require_torch
class 		a_	(							UpperCamelCase_			,      UpperCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







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

      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=       DPTModelTester(self    )
           SCREAMING_SNAKE_CASE										=       ConfigTester(self      ,config_class=__SCREAMING_SNAKE_CASE      ,has_text_modality=__SCREAMING_SNAKE_CASE      ,hidden_size=37    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           self.config_tester.run_common_tests()
      @unittest.skip(reason='DPT does not use inputs_embeds'    )
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           pass
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

                SCREAMING_SNAKE_CASE										=       ['pixel_values']
                self.assertListEqual(arg_names[:1]      ,__SCREAMING_SNAKE_CASE    )
      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE										=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*__SCREAMING_SNAKE_CASE    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

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

                if model_class in get_values(__SCREAMING_SNAKE_CASE    ):
                     continue

                SCREAMING_SNAKE_CASE										=       model_class(__SCREAMING_SNAKE_CASE    )
                model.to(__SCREAMING_SNAKE_CASE    )
                model.train()
                SCREAMING_SNAKE_CASE										=       self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,__SCREAMING_SNAKE_CASE      ,return_labels=__SCREAMING_SNAKE_CASE    )
                SCREAMING_SNAKE_CASE										=       model(**__SCREAMING_SNAKE_CASE    ).loss
                loss.backward()
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

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

                if model_class in get_values(__SCREAMING_SNAKE_CASE    ) or not model_class.supports_gradient_checkpointing:
                     continue
                SCREAMING_SNAKE_CASE										=       model_class(__SCREAMING_SNAKE_CASE    )
                model.to(__SCREAMING_SNAKE_CASE    )
                model.gradient_checkpointing_enable()
                model.train()
                SCREAMING_SNAKE_CASE										=       self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,__SCREAMING_SNAKE_CASE      ,return_labels=__SCREAMING_SNAKE_CASE    )
                SCREAMING_SNAKE_CASE										=       model(**__SCREAMING_SNAKE_CASE    ).loss
                loss.backward()
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=       self.model_tester.prepare_config_and_inputs_for_common()

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


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







def       snake_case__    (       ):





     """simple docstring"""







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



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



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=       DPTImageProcessor.from_pretrained('Intel/dpt-large'    )
           SCREAMING_SNAKE_CASE										=       DPTForDepthEstimation.from_pretrained('Intel/dpt-large'    ).to(__SCREAMING_SNAKE_CASE    )

           SCREAMING_SNAKE_CASE										=       prepare_img()
           SCREAMING_SNAKE_CASE										=       image_processor(images=__SCREAMING_SNAKE_CASE      ,return_tensors='pt'    ).to(__SCREAMING_SNAKE_CASE    )

           # forward pass
           with torch.no_grad():
                SCREAMING_SNAKE_CASE										=       model(**__SCREAMING_SNAKE_CASE    )
                SCREAMING_SNAKE_CASE										=       outputs.predicted_depth

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

           SCREAMING_SNAKE_CASE										=       torch.tensor(
               [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]    ).to(__SCREAMING_SNAKE_CASE    )

           self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]      ,__SCREAMING_SNAKE_CASE      ,atol=1e-4    )    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=       DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'    )
           SCREAMING_SNAKE_CASE										=       DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'    ).to(__SCREAMING_SNAKE_CASE    )

           SCREAMING_SNAKE_CASE										=       prepare_img()
           SCREAMING_SNAKE_CASE										=       image_processor(images=__SCREAMING_SNAKE_CASE      ,return_tensors='pt'    ).to(__SCREAMING_SNAKE_CASE    )

           # forward pass
           with torch.no_grad():
                SCREAMING_SNAKE_CASE										=       model(**__SCREAMING_SNAKE_CASE    )

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

           SCREAMING_SNAKE_CASE										=       torch.tensor(
               [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]    ).to(__SCREAMING_SNAKE_CASE    )

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


      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE										=       DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'    )
           SCREAMING_SNAKE_CASE										=       DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'    ).to(__SCREAMING_SNAKE_CASE    )

           SCREAMING_SNAKE_CASE										=       prepare_img()
           SCREAMING_SNAKE_CASE										=       image_processor(images=__SCREAMING_SNAKE_CASE      ,return_tensors='pt'    ).to(__SCREAMING_SNAKE_CASE    )

           # forward pass
           with torch.no_grad():
                SCREAMING_SNAKE_CASE										=       model(**__SCREAMING_SNAKE_CASE    )

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

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

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




def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     if power < 0:
          return -1
     base %= mod
     SCREAMING_SNAKE_CASE										=1

     while power > 0:
          if power & 1:
               SCREAMING_SNAKE_CASE										=(result * base) % mod
          SCREAMING_SNAKE_CASE										=power >> 1
          SCREAMING_SNAKE_CASE										=(base * base) % mod

     return result







def       snake_case__    (       ):





     """simple docstring"""







     print(modular_exponential(3,   200,   13      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 main()

