






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_    :






 '''simple docstring'''




 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							,  ) ->		Optional[int]:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

     _UpperCAmelCase				:      Dict			  =				self.get_config()

     return config, pixel_values, labels


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

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				{
         '''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  snake_case__				(	self							,  _A							,  _A							,  _A) ->		Optional[int]:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A							,  _A) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				self.num_labels
     _UpperCAmelCase				:      Optional[Any]			  =				DPTForSemanticSegmentation(_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase				:      Any			  =				model(_A							,  labels=_A)
     self.parent.assertEqual(
         result.logits.shape							,  (self.batch_size, self.num_labels, self.image_size, self.image_size))


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				self.prepare_config_and_inputs()
     _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      str			  =				config_and_inputs
     _UpperCAmelCase				:      Optional[Any]			  =				{'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class 							A_    (						__lowercase ,   __lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					(
     {
         "depth-estimation": DPTForDepthEstimation,
         "feature-extraction": DPTModel,
         "image-segmentation": DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 _SCREAMING_SNAKE_CASE							:			Union[str, Any] =					False
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					False
 _SCREAMING_SNAKE_CASE							:			List[Any] =					False


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				DPTModelTester(self)
     _UpperCAmelCase				:      int			  =				ConfigTester(self							,  config_class=_A							,  has_text_modality=_A							,  hidden_size=37)


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

     """simple docstring"""



     self.config_tester.run_common_tests()


 @unittest.skip(reason='''DPT does not use inputs_embeds''')
 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     pass


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      int			  =				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _UpperCAmelCase				:      Dict			  =				model_class(_A)
         self.assertIsInstance(model.get_input_embeddings()							,  (nn.Module))
         _UpperCAmelCase				:      Union[str, Any]			  =				model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_A							,  nn.Linear))


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

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Any			  =				self.model_tester.prepare_config_and_inputs_for_common()

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

         _UpperCAmelCase				:      Optional[Any]			  =				['''pixel_values''']
         self.assertListEqual(arg_names[:1]							,  _A)


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_A)


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*_A)


 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_A)


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

     """simple docstring"""



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

         _UpperCAmelCase  , _UpperCAmelCase				:      Any			  =				self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase				:      Optional[Any]			  =				True

         if model_class in get_values(_A):
             continue

         _UpperCAmelCase				:      Dict			  =				model_class(_A)
         model.to(_A)
         model.train()
         _UpperCAmelCase				:      List[Any]			  =				self._prepare_for_class(_A							,  _A							,  return_labels=_A)
         _UpperCAmelCase				:      List[Any]			  =				model(**_A).loss
         loss.backward()


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

     """simple docstring"""



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

         _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				self.model_tester.prepare_config_and_inputs_for_common()
         _UpperCAmelCase				:      List[str]			  =				False
         _UpperCAmelCase				:      List[Any]			  =				True

         if model_class in get_values(_A) or not model_class.supports_gradient_checkpointing:
             continue
         _UpperCAmelCase				:      List[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				:      Tuple			  =				model(**_A).loss
         loss.backward()


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

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Dict			  =				self.model_tester.prepare_config_and_inputs_for_common()

     _UpperCAmelCase				:      Optional[Any]			  =				_config_zero_init(_A)
     for model_class in self.all_model_classes:
         _UpperCAmelCase				:      Union[str, Any]			  =				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				:      int			  =				[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  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     pass


 @slow
 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
         _UpperCAmelCase				:      List[str]			  =				DPTModel.from_pretrained(_A)
         self.assertIsNotNone(_A)


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

     """simple docstring"""



     _UpperCAmelCase  , _UpperCAmelCase				:      Tuple			  =				self.model_tester.prepare_config_and_inputs_for_common()
     _UpperCAmelCase				:      Union[str, Any]			  =				'''add'''
     with self.assertRaises(_A):
         _UpperCAmelCase				:      Optional[Any]			  =				DPTForDepthEstimation(_A)



def      _lowerCamelCase	(						)      ->					List[str]:
    _UpperCAmelCase				:      str			  =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
    return image


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






 '''simple docstring'''




 def  snake_case__				(	self) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
     _UpperCAmelCase				:      List[Any]			  =				DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(_A)

     _UpperCAmelCase				:      Tuple			  =				prepare_img()
     _UpperCAmelCase				:      Optional[Any]			  =				image_processor(images=_A							,  return_tensors='''pt''').to(_A)

     # forward pass
     with torch.no_grad():
         _UpperCAmelCase				:      int			  =				model(**_A)
         _UpperCAmelCase				:      List[Any]			  =				outputs.predicted_depth

     # verify the predicted depth
     _UpperCAmelCase				:      Optional[Any]			  =				torch.Size((1, 384, 384))
     self.assertEqual(predicted_depth.shape							,  _A)

     _UpperCAmelCase				:      Union[str, Any]			  =				torch.tensor(
         [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]).to(_A)

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












import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class 							A_    (						pl.LightningModule			):






 '''simple docstring'''




 def __init__(	self							,  _A) ->		List[str]:

     """simple docstring"""



     super().__init__()
     _UpperCAmelCase				:      Dict			  =				model
     _UpperCAmelCase				:      Union[str, Any]			  =				2
     _UpperCAmelCase				:      List[Any]			  =				nn.Linear(self.model.config.hidden_size							,  self.num_labels)


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

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		map_location=torch.device('''cpu''' ) )
    lightning_model.load_state_dict(ckpt['''state_dict'''] )

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				LongformerForQuestionAnswering.from_pretrained(__A )

    # transfer weights
    longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
    longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
    longformer_for_qa.eval()

    # save model
    longformer_for_qa.save_pretrained(__A )

    print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--longformer_model',
      default=None,
      type=str,
      required=True,
      help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
  )
  parser.add_argument(
      '--longformer_question_answering_ckpt_path',
      default=None,
      type=str,
      required=True,
      help='Path the official PyTorch Lightning Checkpoint.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




