




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase__				:      List[Any]	   = {
    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				:      int	   = ["VisionTextDualEncoderModel"]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				:      int	   = ["FlaxVisionTextDualEncoderModel"]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				:      Any	   = ["TFVisionTextDualEncoderModel"]


if TYPE_CHECKING:
   from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
   from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
   import sys

   lowercase__				:      int	   = _LazyModule(__name__, globals()["__file__"], _import_structure)







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  ,		A  ,		A=2  ,		A=32  ,		A=16  ,		A=3  ,		A=True  ,		A=True  ,		A=32  ,		A=4  ,		A=[0, 1, 2, 3]  ,		A=4  ,		A=37  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=0.0_2  ,		A=3  ,		A=[1, 384, 24, 24]  ,		A=True  ,		A=None  ,		)  ->   Any:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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

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

          a     =				self.get_config()

          return config, pixel_values, labels







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				{
              "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=A  ,		initializer_range=self.initializer_range  ,		is_hybrid=self.is_hybrid  ,		backbone_config=A  ,		backbone_featmap_shape=self.backbone_featmap_shape  ,		)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Dict:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.prepare_config_and_inputs()
          a  ,				a  ,				a     =				config_and_inputs
          a     =				{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class       a__	( UpperCamelCase__       , UpperCamelCase__       , unittest.TestCase				):
     a		:      Union[str, Any]		 =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     a		:      Union[str, Any]		 =     (
         {
             """depth-estimation""": DPTForDepthEstimation,
             """feature-extraction""": DPTModel,
             """image-segmentation""": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     a		:      Optional[int]		 =     False
     a		:      List[Any]		 =     False
     a		:      int		 =     False







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				DPTModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=A  ,		has_text_modality=A  ,		hidden_size=37			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     @unittest.skip(reason="DPT does not use inputs_embeds"			)
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

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

               a     =				["pixel_values"]
               self.assertListEqual(arg_names[:1]  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*A			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*A			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



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

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

               if model_class in get_values(A			):
                    continue

               a     =				model_class(A			)
               model.to(A			)
               model.train()
               a     =				self._prepare_for_class(A  ,		A  ,		return_labels=A			)
               a     =				model(**A			).loss
               loss.backward()







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



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

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

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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

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



          '''simple docstring'''



          pass







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()
          a     =				"add"
          with self.assertRaises(A			):
               a     =				DPTForDepthEstimation(A			)







def  SCREAMING_SNAKE_CASE							(       )      ->					str:
     a     =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     return image





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







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"			)
          a     =				DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"			).to(A			)

          a     =				prepare_img()
          a     =				image_processor(images=A  ,		return_tensors="pt"			).to(A			)

          # forward pass
          with torch.no_grad():
               a     =				model(**A			)
               a     =				outputs.predicted_depth

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

          a     =				torch.tensor(
              [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]			).to(A			)

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

