
'''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__ :



  '''simple docstring'''






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

  def 		_lowerCamelCase						(self	:			str     ) ->							int:
        snake_case_    							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        snake_case_    							=				self.get_config()

        return config, pixel_values, labels

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Dict:
        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	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Any     ) ->							Union[str, Any]:
        snake_case_    							=				DPTModel(config=lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				model(lowerCAmelCase_     )
        self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)     )

  def 		_lowerCamelCase						(self	:			Dict						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			List[Any]     ) ->							List[Any]:
        snake_case_    							=				self.num_labels
        snake_case_    							=				DPTForDepthEstimation(lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				model(lowerCAmelCase_     )
        self.parent.assertEqual(result.predicted_depth.shape						,			(self.batch_size, self.image_size, self.image_size)     )

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Dict     ) ->							Dict:
        snake_case_    							=				self.num_labels
        snake_case_    							=				DPTForSemanticSegmentation(lowerCAmelCase_     )
        model.to(lowerCAmelCase_     )
        model.eval()
        snake_case_    							=				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	:			int     ) ->							Optional[int]:
        snake_case_    							=				self.prepare_config_and_inputs()
        snake_case_							,						snake_case_							,						snake_case_    							=				config_and_inputs
        snake_case_    							=				{"pixel_values": pixel_values}
        return config, inputs_dict



@require_torch
class 			lowerCAmelCase__ (							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	:			Tuple     ) ->							Optional[Any]:
        snake_case_    							=				DPTModelTester(self     )
        snake_case_    							=				ConfigTester(self						,			config_class=lowerCAmelCase_						,			has_text_modality=lowerCAmelCase_						,			hidden_size=37     )

  def 		_lowerCamelCase						(self	:			str     ) ->							Dict:
        self.config_tester.run_common_tests()

  @unittest.skip(reason="DPT does not use inputs_embeds"     )
  def 		_lowerCamelCase						(self	:			int     ) ->							List[Any]:
        pass

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Dict:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_common()

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

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Optional[int]:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case_    							=				["pixel_values"]
              self.assertListEqual(arg_names[:1]						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Tuple:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Optional[int]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Optional[Any]:
        snake_case_    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							int:
        for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                    continue

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

              if model_class in get_values(lowerCAmelCase_     ):
                    continue

              snake_case_    							=				model_class(lowerCAmelCase_     )
              model.to(lowerCAmelCase_     )
              model.train()
              snake_case_    							=				self._prepare_for_class(lowerCAmelCase_						,			lowerCAmelCase_						,			return_labels=lowerCAmelCase_     )
              snake_case_    							=				model(**lowerCAmelCase_     ).loss
              loss.backward()

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Dict:
        for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                    continue

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

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

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Optional[Any]:
        snake_case_							,						snake_case_    							=				self.model_tester.prepare_config_and_inputs_for_common()

        snake_case_    							=				_config_zero_init(lowerCAmelCase_     )
        for model_class in self.all_model_classes:
              snake_case_    							=				model_class(config=lowerCAmelCase_     )
              # Skip the check for the backbone
              snake_case_    							=				[]
              for name, module in model.named_modules():
                    if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          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]     ) ->							Any:
        pass


  @slow
  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							str:
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case_    							=				DPTModel.from_pretrained(lowerCAmelCase_     )
              self.assertIsNotNone(lowerCAmelCase_     )

def 	snake_case_							(				):
      snake_case_    							=				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	:			str     ) ->							int:
        snake_case_    							=				DPTImageProcessor.from_pretrained("Intel/dpt-large"     )
        snake_case_    							=				DPTForDepthEstimation.from_pretrained("Intel/dpt-large"     ).to(lowerCAmelCase_     )

        snake_case_    							=				prepare_img()
        snake_case_    							=				image_processor(images=lowerCAmelCase_						,			return_tensors="pt"     ).to(lowerCAmelCase_     )

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

        # verify the predicted depth
        snake_case_    							=				torch.Size((1, 3_84, 3_84)     )
        self.assertEqual(predicted_depth.shape						,			lowerCAmelCase_     )

        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(lowerCAmelCase_     )

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

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							List[str]:
        snake_case_    							=				DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"     )
        snake_case_    							=				DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"     ).to(lowerCAmelCase_     )

        snake_case_    							=				prepare_img()
        snake_case_    							=				image_processor(images=lowerCAmelCase_						,			return_tensors="pt"     ).to(lowerCAmelCase_     )

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

        # verify the logits
        snake_case_    							=				torch.Size((1, 1_50, 4_80, 4_80)     )
        self.assertEqual(outputs.logits.shape						,			lowerCAmelCase_     )

        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(lowerCAmelCase_     )

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


  def 		_lowerCamelCase						(self	:			int     ) ->							Any:
        snake_case_    							=				DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"     )
        snake_case_    							=				DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"     ).to(lowerCAmelCase_     )

        snake_case_    							=				prepare_img()
        snake_case_    							=				image_processor(images=lowerCAmelCase_						,			return_tensors="pt"     ).to(lowerCAmelCase_     )

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

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

        snake_case_    							=				image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_						,			target_sizes=[(5_00, 3_00)]     )
        snake_case_    							=				torch.Size((5_00, 3_00)     )
        self.assertEqual(segmentation[0].shape						,			lowerCAmelCase_     )

        snake_case_    							=				image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_     )
        snake_case_    							=				torch.Size((4_80, 4_80)     )
        self.assertEqual(segmentation[0].shape						,			lowerCAmelCase_     )



'''simple docstring'''





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int     ) ->							None:
        snake_case_    							=				word
        snake_case_    							=				count

  def __eq__(self	:			List[Any]						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count == other.count


  def __lt__(self	:			Tuple						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count < other.count

def 	snake_case_							(				_a  :				list[str]			,       _a  :				int      ):
      snake_case_    							=				Heap()
      snake_case_    							=				Counter(_a      )
      heap.build_max_heap(
          [WordCount(_a			,       _a      ) for word, count in count_by_word.items()]      )
      return [heap.extract_max().word for _ in range(min(_a			,       len(_a      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

