






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



        """simple docstring"""






        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 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        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 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        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=_SCREAMING_SNAKE_CASE    ,	initializer_range=self.initializer_range    ,	is_hybrid=self.is_hybrid    ,	backbone_config=_SCREAMING_SNAKE_CASE    ,	backbone_featmap_shape=self.backbone_featmap_shape    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Optional[int]:



        """simple docstring"""






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


  def 						A__		(   self   )						->   str:



        """simple docstring"""






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






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  lowercase  	=   (
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

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



        """simple docstring"""






        UpperCamelCase       						=      DPTModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE    ,	hidden_size=37   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        self.config_tester.run_common_tests()

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



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              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]    ,	_SCREAMING_SNAKE_CASE   )

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



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*_SCREAMING_SNAKE_CASE   )

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



        """simple docstring"""






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

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

              if model_class in get_values(_SCREAMING_SNAKE_CASE   ):
                    continue

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

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



        """simple docstring"""






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

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

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

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



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        UpperCamelCase       						=      _config_zero_init(_SCREAMING_SNAKE_CASE   )
        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(config=_SCREAMING_SNAKE_CASE   )
              # 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 						A__		(   self   )						->   Any:



        """simple docstring"""






        pass

  @slow
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






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


  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()
        UpperCamelCase       						=      """add"""
        with self.assertRaises(_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      DPTForDepthEstimation(_SCREAMING_SNAKE_CASE   )







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






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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""   )
        UpperCamelCase       						=      DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""   ).to(_SCREAMING_SNAKE_CASE   )

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

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

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

        UpperCamelCase       						=      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(_SCREAMING_SNAKE_CASE   )

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













'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,		__UpperCamelCase					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase						,UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(__UpperCamelCase	,		__UpperCamelCase	,		bias=__UpperCamelCase					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      torch.load(__UpperCamelCase	,		map_location="""cpu"""					)
      UpperCamelCase       						=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
      UpperCamelCase       						=      mam_aaa["""model"""]
      remove_ignore_keys_(__UpperCamelCase					)
      UpperCamelCase       						=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

      UpperCamelCase       						=      MaMaaaConfig(
          vocab_size=__UpperCamelCase	,		max_position_embeddings=1024	,		encoder_layers=args.encoder_layers	,		decoder_layers=args.decoder_layers	,		encoder_attention_heads=args.encoder_attention_heads	,		decoder_attention_heads=args.decoder_attention_heads	,		encoder_ffn_dim=args.encoder_ffn_embed_dim	,		decoder_ffn_dim=args.decoder_ffn_embed_dim	,		d_model=args.encoder_embed_dim	,		encoder_layerdrop=args.encoder_layerdrop	,		decoder_layerdrop=args.decoder_layerdrop	,		dropout=args.dropout	,		attention_dropout=args.attention_dropout	,		activation_dropout=args.activation_dropout	,		activation_function="""relu"""	,		)

      UpperCamelCase       						=      state_dict["""decoder.embed_tokens.weight"""]
      UpperCamelCase       						=      MaMaaaForConditionalGeneration(__UpperCamelCase					)
      model.model.load_state_dict(__UpperCamelCase	,		strict=__UpperCamelCase					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.shared					)

      return model


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     SCREAMING_SNAKE_CASE__											=     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)
