





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_=0.02 ,							A_=3 ,							A_=[1, 384, 24, 24] ,							A_=True ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     parent
       UpperCamelCase			:      Optional[int] 			=     batch_size
       UpperCamelCase			:      Tuple 			=     image_size
       UpperCamelCase			:      Optional[int] 			=     patch_size
       UpperCamelCase			:      str 			=     num_channels
       UpperCamelCase			:      Tuple 			=     is_training
       UpperCamelCase			:      Dict 			=     use_labels
       UpperCamelCase			:      Union[str, Any] 			=     hidden_size
       UpperCamelCase			:      Optional[int] 			=     num_hidden_layers
       UpperCamelCase			:      str 			=     backbone_out_indices
       UpperCamelCase			:      Tuple 			=     num_attention_heads
       UpperCamelCase			:      int 			=     intermediate_size
       UpperCamelCase			:      str 			=     hidden_act
       UpperCamelCase			:      List[Any] 			=     hidden_dropout_prob
       UpperCamelCase			:      Any 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Dict 			=     initializer_range
       UpperCamelCase			:      Dict 			=     num_labels
       UpperCamelCase			:      Union[str, Any] 			=     backbone_featmap_shape
       UpperCamelCase			:      Dict 			=     scope
       UpperCamelCase			:      Any 			=     is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase			:      Any 			=     (image_size // patch_size) ** 2
       UpperCamelCase			:      Optional[int] 			=     num_patches + 1





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

       UpperCamelCase			:      Optional[int] 			=     None
       if self.use_labels:
        UpperCamelCase			:      str 			=     ids_tensor([self.batch_size, self.image_size, self.image_size] ,							self.num_labels     )

       UpperCamelCase			:      Tuple 			=     self.get_config()

       return config, pixel_values, labels





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     {
           "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  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     DPTModel(config=A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      List[Any] 			=     model(A_     )
       self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.num_labels
       UpperCamelCase			:      Dict 			=     DPTForDepthEstimation(A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      Dict 			=     model(A_     )
       self.parent.assertEqual(result.predicted_depth.shape ,							(self.batch_size, self.image_size, self.image_size)     )





      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.num_labels
       UpperCamelCase			:      int 			=     DPTForSemanticSegmentation(A_     )
       model.to(A_     )
       model.eval()
       UpperCamelCase			:      Dict 			=     model(A_ ,							labels=A_     )
       self.parent.assertEqual(
           result.logits.shape ,							(self.batch_size, self.num_labels, self.image_size, self.image_size)     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.prepare_config_and_inputs()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     config_and_inputs
       UpperCamelCase			:      List[Any] 			=     {"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class       A__     ( __snake_case ,     __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:Tuple							=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      _UpperCAmelCase			:List[str]							=     (
          {
              'depth-estimation': DPTForDepthEstimation,
              'feature-extraction': DPTModel,
              'image-segmentation': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      _UpperCAmelCase			:Optional[Any]							=     False
      _UpperCAmelCase			:str							=     False
      _UpperCAmelCase			:Dict							=     False





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     DPTModelTester(self     )
       UpperCamelCase			:      str 			=     ConfigTester(self ,							config_class=A_ ,							has_text_modality=A_ ,							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			:      List[Any] 			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        UpperCamelCase			:      Tuple 			=     model_class(A_     )
        self.assertIsInstance(model.get_input_embeddings() ,							(nn.Module)     )
        UpperCamelCase			:      Optional[int] 			=     model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(A_ ,							nn.Linear     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase			:      Optional[int] 			=     ["pixel_values"]
        self.assertListEqual(arg_names[:1] ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

        UpperCamelCase			,					UpperCamelCase			:      Optional[int] 			=     self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase			:      List[Any] 			=     True

        if model_class in get_values(A_     ):
         continue

        UpperCamelCase			:      Optional[int] 			=     model_class(A_     )
        model.to(A_     )
        model.train()
        UpperCamelCase			:      Optional[int] 			=     self._prepare_for_class(A_ ,							A_ ,							return_labels=A_     )
        UpperCamelCase			:      Optional[int] 			=     model(**A_     ).loss
        loss.backward()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

        UpperCamelCase			,					UpperCamelCase			:      Optional[Any] 			=     self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase			:      Any 			=     False
        UpperCamelCase			:      Optional[int] 			=     True

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      str 			=     self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase			:      List[Any] 			=     _config_zero_init(A_     )
       for model_class in self.all_model_classes:
        UpperCamelCase			:      List[str] 			=     model_class(config=A_     )
        # Skip the check for the backbone
        UpperCamelCase			:      List[Any] 			=     []
        for name, module in model.named_modules():
         if module.__class__.__name__ == "DPTViTHybridEmbeddings":
          UpperCamelCase			:      Optional[Any] 			=     [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			:      Dict 			=     DPTModel.from_pretrained(A_     )
        self.assertIsNotNone(A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      int 			=     self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase			:      Dict 			=     "add"
       with self.assertRaises(A_     ):
        UpperCamelCase			:      Optional[int] 			=     DPTForDepthEstimation(A_     )



def 	A_       (  )		->				Optional[Any]:
 UpperCamelCase			:      List[Any] 			=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
 return image



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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"     )
       UpperCamelCase			:      Optional[Any] 			=     DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"     ).to(A_     )

       UpperCamelCase			:      int 			=     prepare_img()
       UpperCamelCase			:      Tuple 			=     image_processor(images=A_ ,							return_tensors="pt"     ).to(A_     )

       # forward pass
       with torch.no_grad():
        UpperCamelCase			:      List[str] 			=     model(**A_     )
        UpperCamelCase			:      int 			=     outputs.predicted_depth

       # verify the predicted depth
       UpperCamelCase			:      str 			=     torch.Size((1, 384, 384)     )
       self.assertEqual(predicted_depth.shape ,							A_     )

       UpperCamelCase			:      str 			=     torch.tensor(
           [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]     ).to(A_     )

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







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      Tuple 			=     ArgumentParser(
     description=(
         "PyTorch TPU distributed training launch "
         "helper utility that will spawn up "
         "multiple distributed processes"
     )  )

 # Optional arguments for the launch helper
 parser.add_argument("--num_cores"							,     type=_lowerCAmelCase							,     default=1							,     help="Number of TPU cores to use (1 or 8)."  )

 # positional
 parser.add_argument(
     "training_script"							,     type=_lowerCAmelCase							,     help=(
         "The full path to the single TPU training "
         "program/script to be launched in parallel, "
         "followed by all the arguments for the "
         "training script"
     )							,     )

 # rest from the training program
 parser.add_argument("training_script_args"							,     nargs=_lowerCAmelCase  )

 return parser.parse_args()



def 	A_       (  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     parse_args()

 # Import training_script as a module.
 UpperCamelCase			:      Union[str, Any] 			=     Path(args.training_script  )
 sys.path.append(str(script_fpath.parent.resolve()  )  )
 UpperCamelCase			:      List[Any] 			=     script_fpath.stem
 UpperCamelCase			:      Optional[Any] 			=     importlib.import_module(_lowerCAmelCase  )

 # Patch sys.argv
 UpperCamelCase			:      List[Any] 			=     [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores  )]
 xmp.spawn(mod._mp_fn							,     args=()							,     nprocs=args.num_cores  )


if __name__ == "__main__":
    main()
