
"""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 __lowerCAmelCase						:





     def __init__(      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=2      ,	__UpperCAmelCase=32      ,	__UpperCAmelCase=16      ,	__UpperCAmelCase=3      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=True      ,	__UpperCAmelCase=32      ,	__UpperCAmelCase=4      ,	__UpperCAmelCase=[0, 1, 2, 3]      ,	__UpperCAmelCase=4      ,	__UpperCAmelCase=37      ,	__UpperCAmelCase="gelu"      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=0.1      ,	__UpperCAmelCase=0.0_2      ,	__UpperCAmelCase=3      ,	__UpperCAmelCase=False      ,	__UpperCAmelCase=None      ,	):

      '''simple docstring'''




      __UpperCamelCase							    =			parent
      __UpperCamelCase							    =			batch_size
      __UpperCamelCase							    =			image_size
      __UpperCamelCase							    =			patch_size
      __UpperCamelCase							    =			num_channels
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			use_labels
      __UpperCamelCase							    =			hidden_size
      __UpperCamelCase							    =			num_hidden_layers
      __UpperCamelCase							    =			backbone_out_indices
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			intermediate_size
      __UpperCamelCase							    =			hidden_act
      __UpperCamelCase							    =			hidden_dropout_prob
      __UpperCamelCase							    =			attention_probs_dropout_prob
      __UpperCamelCase							    =			initializer_range
      __UpperCamelCase							    =			num_labels
      __UpperCamelCase							    =			scope
      __UpperCamelCase							    =			is_hybrid
      # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
      __UpperCamelCase							    =			(image_size // patch_size) ** 2
      __UpperCamelCase							    =			num_patches + 1





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

      __UpperCamelCase							    =			self.get_config()

      return config, pixel_values, labels





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      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=__UpperCAmelCase      ,	initializer_range=self.initializer_range      ,	is_hybrid=self.is_hybrid      ,	)





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			DPTModel(config=__UpperCAmelCase )
      model.to(__UpperCAmelCase )
      model.eval()
      __UpperCamelCase							    =			model(__UpperCAmelCase )
      self.parent.assertEqual(result.last_hidden_state.shape      ,	(self.batch_size, self.seq_length, self.hidden_size) )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.num_labels
      __UpperCamelCase							    =			DPTForDepthEstimation(__UpperCAmelCase )
      model.to(__UpperCAmelCase )
      model.eval()
      __UpperCamelCase							    =			model(__UpperCAmelCase )
      self.parent.assertEqual(result.predicted_depth.shape      ,	(self.batch_size, self.image_size, self.image_size) )





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.num_labels
      __UpperCamelCase							    =			DPTForSemanticSegmentation(__UpperCAmelCase )
      model.to(__UpperCAmelCase )
      model.eval()
      __UpperCamelCase							    =			model(__UpperCAmelCase      ,	labels=__UpperCAmelCase )
      self.parent.assertEqual(
          result.logits.shape      ,	(self.batch_size, self.num_labels, self.image_size, self.image_size) )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.prepare_config_and_inputs()
      __UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase							    =			config_and_inputs
      __UpperCamelCase							    =			{'pixel_values': pixel_values}
      return config, inputs_dict


@require_torch
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     lowercase		  =		(
         {
             "depth-estimation": DPTForDepthEstimation,
             "feature-extraction": DPTModel,
             "image-segmentation": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowercase		  =		False
     lowercase		  =		False
     lowercase		  =		False





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			DPTModelTester(self )
      __UpperCamelCase							    =			ConfigTester(self      ,	config_class=__UpperCAmelCase      ,	has_text_modality=__UpperCAmelCase      ,	hidden_size=37 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





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

      '''simple docstring'''




      pass





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase							    =			model_class(__UpperCAmelCase )
       self.assertIsInstance(model.get_input_embeddings()      ,	(nn.Module) )
       __UpperCamelCase							    =			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__UpperCAmelCase      ,	nn.Linear ) )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs_for_common()

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

       __UpperCamelCase							    =			['pixel_values']
       self.assertListEqual(arg_names[:1]      ,	__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

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

       if model_class in get_values(__UpperCAmelCase ):
        continue

       __UpperCamelCase							    =			model_class(__UpperCAmelCase )
       model.to(__UpperCAmelCase )
       model.train()
       __UpperCamelCase							    =			self._prepare_for_class(__UpperCAmelCase      ,	__UpperCAmelCase      ,	return_labels=__UpperCAmelCase )
       __UpperCamelCase							    =			model(**__UpperCAmelCase ).loss
       loss.backward()





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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

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

       if model_class in get_values(__UpperCAmelCase ) or not model_class.supports_gradient_checkpointing:
        continue
       __UpperCamelCase							    =			model_class(__UpperCAmelCase )
       model.to(__UpperCAmelCase )
       model.gradient_checkpointing_enable()
       model.train()
       __UpperCamelCase							    =			self._prepare_for_class(__UpperCAmelCase      ,	__UpperCAmelCase      ,	return_labels=__UpperCAmelCase )
       __UpperCamelCase							    =			model(**__UpperCAmelCase ).loss
       loss.backward()





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.model_tester.prepare_config_and_inputs_for_common()

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

      '''simple docstring'''




      pass







     @slow
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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






def 				A		(							) ->      Optional[Any]:
 __UpperCamelCase							    =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
 return image





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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			DPTImageProcessor.from_pretrained('Intel/dpt-large' )
      __UpperCamelCase							    =			DPTForDepthEstimation.from_pretrained('Intel/dpt-large' ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			prepare_img()
      __UpperCamelCase							    =			image_processor(images=__UpperCAmelCase      ,	return_tensors='pt' ).to(__UpperCAmelCase )

      # forward pass
      with torch.no_grad():
       __UpperCamelCase							    =			model(**__UpperCAmelCase )
       __UpperCamelCase							    =			outputs.predicted_depth

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

      __UpperCamelCase							    =			torch.tensor(
          [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] ).to(__UpperCAmelCase )

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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			DPTImageProcessor.from_pretrained('Intel/dpt-large-ade' )
      __UpperCamelCase							    =			DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade' ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			prepare_img()
      __UpperCamelCase							    =			image_processor(images=__UpperCAmelCase      ,	return_tensors='pt' ).to(__UpperCAmelCase )

      # forward pass
      with torch.no_grad():
       __UpperCamelCase							    =			model(**__UpperCAmelCase )

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

      __UpperCamelCase							    =			torch.tensor(
          [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] ).to(__UpperCAmelCase )

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







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			DPTImageProcessor.from_pretrained('Intel/dpt-large-ade' )
      __UpperCamelCase							    =			DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade' ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			prepare_img()
      __UpperCamelCase							    =			image_processor(images=__UpperCAmelCase      ,	return_tensors='pt' ).to(__UpperCAmelCase )

      # forward pass
      with torch.no_grad():
       __UpperCamelCase							    =			model(**__UpperCAmelCase )

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

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

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





"""simple docstring"""

from sklearn.neural_network import MLPClassifier

UpperCamelCase      :							Any          =					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase      :							Any          =					[0, 1, 0, 0]


UpperCamelCase      :							Optional[Any]          =					MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase      :							List[Any]          =					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase      :							Optional[Any]          =					clf.predict(test)






def 				A		(							snake_case :int						) ->      int:
 return list(snake_case						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



