
'''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 		snake_case_       :



   """simple docstring"""





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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

          lowerCamelCase__				   =      self.get_config()

          return config, pixel_values, labels







   def     __UpperCAmelCase      ( self):
          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):
          lowerCamelCase__				   =      DPTModel(config=UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()
          lowerCamelCase__				   =      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):
          lowerCamelCase__				   =      self.num_labels
          lowerCamelCase__				   =      DPTForDepthEstimation(UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()
          lowerCamelCase__				   =      model(UpperCamelCase)
          self.parent.assertEqual(result.predicted_depth.shape			,   (self.batch_size, self.image_size, self.image_size))







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      self.num_labels
          lowerCamelCase__				   =      DPTForSemanticSegmentation(UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()
          lowerCamelCase__				   =      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):
          lowerCamelCase__				   =      self.prepare_config_and_inputs()
          lowerCamelCase__				   =      config_and_inputs
          lowerCamelCase__				   =      {'pixel_values': pixel_values}
          return config, inputs_dict







@require_torch
class 		snake_case_       (						A__		,	A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							List[str]								=(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   __lowerCAmelCase	:							Dict								=(
       {
           '''depth-estimation''': DPTForDepthEstimation,
           '''feature-extraction''': DPTModel,
           '''image-segmentation''': DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __lowerCAmelCase	:							Any								=False
   __lowerCAmelCase	:							Optional[int]								=False
   __lowerCAmelCase	:							Tuple								=False







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      DPTModelTester(self)
          lowerCamelCase__				   =      ConfigTester(self			,   config_class=UpperCamelCase			,   has_text_modality=UpperCamelCase			,   hidden_size=37)







   def     __UpperCAmelCase      ( self):
          self.config_tester.run_common_tests()







   @unittest.skip(reason="DPT does not use inputs_embeds")
   def     __UpperCAmelCase      ( self):
          pass







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs_for_common()

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCamelCase__				   =      ['pixel_values']
                 self.assertListEqual(arg_names[:1]			,   UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase)







   def     __UpperCAmelCase      ( self):







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

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

                 if model_class in get_values(UpperCamelCase):
                        continue

                 lowerCamelCase__				   =      model_class(UpperCamelCase)
                 model.to(UpperCamelCase)
                 model.train()
                 lowerCamelCase__				   =      self._prepare_for_class(UpperCamelCase			,   UpperCamelCase			,   return_labels=UpperCamelCase)
                 lowerCamelCase__				   =      model(**UpperCamelCase).loss
                 loss.backward()







   def     __UpperCAmelCase      ( self):







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

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

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







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      self.model_tester.prepare_config_and_inputs_for_common()

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







   @slow
   def     __UpperCAmelCase      ( self):







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







def   lowerCAmelCase(  ):







       '''simple docstring'''






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







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



   """simple docstring"""





   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      DPTImageProcessor.from_pretrained("Intel/dpt-large")
          lowerCamelCase__				   =      DPTForDepthEstimation.from_pretrained("Intel/dpt-large").to(UpperCamelCase)

          lowerCamelCase__				   =      prepare_img()
          lowerCamelCase__				   =      image_processor(images=UpperCamelCase			,   return_tensors="pt").to(UpperCamelCase)

          # forward pass
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)
                 lowerCamelCase__				   =      outputs.predicted_depth

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

          lowerCamelCase__				   =      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):
          lowerCamelCase__				   =      DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
          lowerCamelCase__				   =      DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(UpperCamelCase)

          lowerCamelCase__				   =      prepare_img()
          lowerCamelCase__				   =      image_processor(images=UpperCamelCase			,   return_tensors="pt").to(UpperCamelCase)

          # forward pass
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)

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

          lowerCamelCase__				   =      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):
          lowerCamelCase__				   =      DPTImageProcessor.from_pretrained("Intel/dpt-large-ade")
          lowerCamelCase__				   =      DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade").to(UpperCamelCase)

          lowerCamelCase__				   =      prepare_img()
          lowerCamelCase__				   =      image_processor(images=UpperCamelCase			,   return_tensors="pt").to(UpperCamelCase)

          # forward pass
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)

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

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

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



from typing import TYPE_CHECKING

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


lowerCamelCase__        =						{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RegNetForImageClassification""",
					    """RegNetModel""",
					    """RegNetPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRegNetForImageClassification""",
					    """TFRegNetModel""",
					    """TFRegNetPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """FlaxRegNetForImageClassification""",
					    """FlaxRegNetModel""",
					    """FlaxRegNetPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_regnet import (
										    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RegNetForImageClassification,
										    RegNetModel,
										    RegNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_regnet import (
										    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRegNetForImageClassification,
										    TFRegNetModel,
										    TFRegNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_regnet import (
										    FlaxRegNetForImageClassification,
										    FlaxRegNetModel,
										    FlaxRegNetPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase__        =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)