"""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 				a :


    def __init__(    self		:     Any      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     List[str]=2      , __lowerCAmelCase		:     int=32      , __lowerCAmelCase		:     str=16      , __lowerCAmelCase		:     Optional[int]=3      , __lowerCAmelCase		:     Tuple=True      , __lowerCAmelCase		:     List[Any]=True      , __lowerCAmelCase		:     Union[str, Any]=32      , __lowerCAmelCase		:     Optional[int]=4      , __lowerCAmelCase		:     Union[str, Any]=[0, 1, 2, 3]      , __lowerCAmelCase		:     Tuple=4      , __lowerCAmelCase		:     str=37      , __lowerCAmelCase		:     Optional[Any]="gelu"      , __lowerCAmelCase		:     str=0.1      , __lowerCAmelCase		:     Optional[Any]=0.1      , __lowerCAmelCase		:     Dict=0.02      , __lowerCAmelCase		:     Dict=3      , __lowerCAmelCase		:     Dict=[1, 384, 24, 24]      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     Any=None      , ):
      _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         =					backbone_featmap_shape
      _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    lowerCAmelCase_     (    self		:     List[str]    ):
      _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    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					{
          '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=__lowerCAmelCase      , initializer_range=self.initializer_range      , is_hybrid=self.is_hybrid      , backbone_config=__lowerCAmelCase      , backbone_featmap_shape=self.backbone_featmap_shape      , )


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


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					self.num_labels
      _UpperCAmelCase         =					DPTForDepthEstimation(__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(__lowerCAmelCase    )
      self.parent.assertEqual(result.predicted_depth.shape      , (self.batch_size, self.image_size, self.image_size)    )


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.num_labels
      _UpperCAmelCase         =					DPTForSemanticSegmentation(__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					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		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.prepare_config_and_inputs()
      _UpperCAmelCase         =					config_and_inputs
      _UpperCAmelCase         =					{'pixel_values': pixel_values}
      return config, inputs_dict



@require_torch
class 				a (						__snake_case		,							__snake_case		,							unittest.TestCase      ):
    _snake_case   :     Optional[Any]   							=							(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    _snake_case   :     List[Any]   							=							(
        {
            """depth-estimation""": DPTForDepthEstimation,
            """feature-extraction""": DPTModel,
            """image-segmentation""": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    _snake_case   :     Any   							=							False
    _snake_case   :     Union[str, Any]   							=							False
    _snake_case   :     List[Any]   							=							False


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					DPTModelTester(self    )
      _UpperCAmelCase         =					ConfigTester(self      , config_class=__lowerCAmelCase      , has_text_modality=__lowerCAmelCase      , hidden_size=37    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      self.config_tester.run_common_tests()


    @unittest.skip(reason="""DPT does not use inputs_embeds"""    )
    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Any    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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


    def    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase         =					model_class(__lowerCAmelCase    )
        _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]      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Any    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase         =					True

        if model_class in get_values(__lowerCAmelCase    ):
          continue

        _UpperCAmelCase         =					model_class(__lowerCAmelCase    )
        model.to(__lowerCAmelCase    )
        model.train()
        _UpperCAmelCase         =					self._prepare_for_class(__lowerCAmelCase      , __lowerCAmelCase      , return_labels=__lowerCAmelCase    )
        _UpperCAmelCase         =					model(**__lowerCAmelCase    ).loss
        loss.backward()


    def    lowerCAmelCase_     (    self		:     Any    ):
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase         =					False
        _UpperCAmelCase         =					True

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


    def    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase         =					_config_zero_init(__lowerCAmelCase    )
      for model_class in self.all_model_classes:
        _UpperCAmelCase         =					model_class(config=__lowerCAmelCase    )
        # 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    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    @slow
    def    lowerCAmelCase_     (    self		:     Any    ):
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
        _UpperCAmelCase         =					DPTModel.from_pretrained(__lowerCAmelCase    )
        self.assertIsNotNone(__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
      _UpperCAmelCase         =					'add'
      with self.assertRaises(__lowerCAmelCase    ):
        _UpperCAmelCase         =					DPTForDepthEstimation(__lowerCAmelCase    )






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
  return image


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


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""    )
      _UpperCAmelCase         =					DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""    ).to(__lowerCAmelCase    )

      _UpperCAmelCase         =					prepare_img()
      _UpperCAmelCase         =					image_processor(images=__lowerCAmelCase      , return_tensors="""pt"""    ).to(__lowerCAmelCase    )

      # forward pass
      with torch.no_grad():
        _UpperCAmelCase         =					model(**__lowerCAmelCase    )
        _UpperCAmelCase         =					outputs.predicted_depth

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

      _UpperCAmelCase         =					torch.tensor(
          [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]    ).to(__lowerCAmelCase    )

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



import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use FlavaImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

