


'''simple docstring'''







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      ,	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__=[1, 384, 24, 24]      ,	snake_case__=True      ,	snake_case__=None      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       parent
            _lowerCAmelCase    :							Any   			=       batch_size
            _lowerCAmelCase    :							Optional[Any]   			=       image_size
            _lowerCAmelCase    :							str   			=       patch_size
            _lowerCAmelCase    :							Optional[Any]   			=       num_channels
            _lowerCAmelCase    :							Tuple   			=       is_training
            _lowerCAmelCase    :							Dict   			=       use_labels
            _lowerCAmelCase    :							str   			=       hidden_size
            _lowerCAmelCase    :							Any   			=       num_hidden_layers
            _lowerCAmelCase    :							Dict   			=       backbone_out_indices
            _lowerCAmelCase    :							str   			=       num_attention_heads
            _lowerCAmelCase    :							str   			=       intermediate_size
            _lowerCAmelCase    :							int   			=       hidden_act
            _lowerCAmelCase    :							Dict   			=       hidden_dropout_prob
            _lowerCAmelCase    :							str   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							Dict   			=       initializer_range
            _lowerCAmelCase    :							Union[str, Any]   			=       num_labels
            _lowerCAmelCase    :							Optional[Any]   			=       backbone_featmap_shape
            _lowerCAmelCase    :							Union[str, Any]   			=       scope
            _lowerCAmelCase    :							Any   			=       is_hybrid
            # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
            _lowerCAmelCase    :							Any   			=       (image_size // patch_size) ** 2
            _lowerCAmelCase    :							Optional[Any]   			=       num_patches + 1







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            _lowerCAmelCase    :							Optional[Any]   			=       None
            if self.use_labels:
                   _lowerCAmelCase    :							List[Any]   			=       ids_tensor([self.batch_size, self.image_size, self.image_size]      ,	self.num_labels   )

            _lowerCAmelCase    :							List[str]   			=       self.get_config()

            return config, pixel_values, labels







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[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=snake_case__      ,	initializer_range=self.initializer_range      ,	is_hybrid=self.is_hybrid      ,	backbone_config=snake_case__      ,	backbone_featmap_shape=self.backbone_featmap_shape      ,	)







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       DPTModel(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							List[str]   			=       model(snake_case__   )
            self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size)   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       self.num_labels
            _lowerCAmelCase    :							int   			=       DPTForDepthEstimation(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Optional[Any]   			=       model(snake_case__   )
            self.parent.assertEqual(result.predicted_depth.shape      ,	(self.batch_size, self.image_size, self.image_size)   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.num_labels
            _lowerCAmelCase    :							str   			=       DPTForSemanticSegmentation(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							str   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase    :							Dict   			=       config_and_inputs
            _lowerCAmelCase    :							Optional[Any]   			=       {'pixel_values': pixel_values}
            return config, inputs_dict
@require_torch
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       DPTModelTester(self   )
            _lowerCAmelCase    :							Optional[int]   			=       ConfigTester(self      ,	config_class=snake_case__      ,	has_text_modality=snake_case__      ,	hidden_size=37   )







     def a	( self   ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @unittest.skip(reason='DPT does not use inputs_embeds'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase    :							Any   			=       model_class(snake_case__   )
                   self.assertIsInstance(model.get_input_embeddings()      ,	(nn.Module)   )
                   _lowerCAmelCase    :							List[str]   			=       model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(snake_case__      ,	nn.Linear   )   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.model_tester.prepare_config_and_inputs_for_common()

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

                   _lowerCAmelCase    :							int   			=       ['pixel_values']
                   self.assertListEqual(arg_names[:1]      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_depth_estimation(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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

                   _lowerCAmelCase    :							int   			=       self.model_tester.prepare_config_and_inputs_for_common()
                   _lowerCAmelCase    :							Tuple   			=       True

                   if model_class in get_values(snake_case__   ):
                          continue

                   _lowerCAmelCase    :							Any   			=       model_class(snake_case__   )
                   model.to(snake_case__   )
                   model.train()
                   _lowerCAmelCase    :							int   			=       self._prepare_for_class(snake_case__      ,	snake_case__      ,	return_labels=snake_case__   )
                   _lowerCAmelCase    :							Optional[Any]   			=       model(**snake_case__   ).loss
                   loss.backward()







     def a	( self   ):

            '''simple docstring'''

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

                   _lowerCAmelCase    :							Optional[Any]   			=       self.model_tester.prepare_config_and_inputs_for_common()
                   _lowerCAmelCase    :							Tuple   			=       False
                   _lowerCAmelCase    :							Optional[int]   			=       True

                   if model_class in get_values(snake_case__   ) or not model_class.supports_gradient_checkpointing:
                          continue
                   _lowerCAmelCase    :							List[Any]   			=       model_class(snake_case__   )
                   model.to(snake_case__   )
                   model.gradient_checkpointing_enable()
                   model.train()
                   _lowerCAmelCase    :							Optional[int]   			=       self._prepare_for_class(snake_case__      ,	snake_case__      ,	return_labels=snake_case__   )
                   _lowerCAmelCase    :							Any   			=       model(**snake_case__   ).loss
                   loss.backward()







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

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

            '''simple docstring'''

            pass







     @slow
     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       self.model_tester.prepare_config_and_inputs_for_common()
            _lowerCAmelCase    :							List[str]   			=       'add'
            with self.assertRaises(snake_case__   ):
                   _lowerCAmelCase    :							Dict   			=       DPTForDepthEstimation(snake_case__   )






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image
@require_torch
@require_vision
@slow
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
            _lowerCAmelCase    :							List[Any]   			=       DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(snake_case__   )

            _lowerCAmelCase    :							int   			=       prepare_img()
            _lowerCAmelCase    :							Tuple   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

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

            # verify the predicted depth
            _lowerCAmelCase    :							List[str]   			=       torch.Size((1, 384, 384)   )
            self.assertEqual(predicted_depth.shape      ,	snake_case__   )

            _lowerCAmelCase    :							str   			=       torch.tensor(
                [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(snake_case__   )

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





'''simple docstring'''







def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=       (boundary[1] - boundary[0]) / steps
       _lowerCAmelCase    :							Any   			=       boundary[0]
       _lowerCAmelCase    :							List[str]   			=       boundary[1]
       _lowerCAmelCase    :							Tuple   			=       make_points(_A   ,						_A   ,						_A					)
       _lowerCAmelCase    :							Tuple   			=       0.0
       y += (h / 2.0) * f(_A					)
       for i in x_i:
              # print(i)
              y += h * f(_A					)
       y += (h / 2.0) * f(_A					)
       return y






def 			lowercase				(_A   ,						_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       a + h
       while x < (b - h):
              yield x
              _lowerCAmelCase    :							Any   			=       x + h






def 			lowercase				(_A					):  # enter your function here







       """simple docstring"""







       _lowerCAmelCase    :							int   			=       (x - 0) * (x - 0)
       return y






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=       0.0  # Lower bound of integration
       _lowerCAmelCase    :							Dict   			=       1.0  # Upper bound of integration
       _lowerCAmelCase    :							Optional[Any]   			=       10.0  # define number of steps or resolution
       _lowerCAmelCase    :							Optional[int]   			=       [a, b]  # define boundary of integration
       _lowerCAmelCase    :							List[Any]   			=       method_a(_A   ,						_A					)
       print(f'y = {y}'					)


if __name__ == "__main__":
       main()

