



"""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       :




  def __init__( self     :						str						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						str=2						,					__lowerCAmelCase     :						List[str]=3_2						,					__lowerCAmelCase     :						List[Any]=1_6						,					__lowerCAmelCase     :						Any=3						,					__lowerCAmelCase     :						str=True						,					__lowerCAmelCase     :						Union[str, Any]=True						,					__lowerCAmelCase     :						Union[str, Any]=3_2						,					__lowerCAmelCase     :						Optional[Any]=4						,					__lowerCAmelCase     :						Any=[0, 1, 2, 3]						,					__lowerCAmelCase     :						Any=4						,					__lowerCAmelCase     :						Tuple=3_7						,					__lowerCAmelCase     :						List[str]="gelu"						,					__lowerCAmelCase     :						Optional[int]=0.1						,					__lowerCAmelCase     :						str=0.1						,					__lowerCAmelCase     :						int=0.02						,					__lowerCAmelCase     :						Optional[int]=3						,					__lowerCAmelCase     :						str=[1, 3_8_4, 2_4, 2_4]						,					__lowerCAmelCase     :						str=True						,					__lowerCAmelCase     :						Tuple=None						,					):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






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

         _lowerCamelCase						:				Dict    			= None
         if self.use_labels:
                _lowerCamelCase						:				Union[str, Any]    			= ids_tensor([self.batch_size, self.image_size, self.image_size]						,					self.num_labels		)

         _lowerCamelCase						:				str    			= self.get_config()

         return config, pixel_values, labels




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= {
             "global_padding": "same",
             "layer_type": "bottleneck",
             "depths": [3, 4, 9],
             "out_features": ["stage1", "stage2", "stage3"],
             "embedding_dynamic_padding": True,
             "hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
             "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 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						Optional[int]						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= DPTModel(config=_A		)
         model.to(_A		)
         model.eval()
         _lowerCamelCase						:				Any    			= model(_A		)
         self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict						,					__lowerCAmelCase     :						List[Any]						,					__lowerCAmelCase     :						str						,					__lowerCAmelCase     :						int		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= self.num_labels
         _lowerCamelCase						:				Dict    			= DPTForSemanticSegmentation(_A		)
         model.to(_A		)
         model.eval()
         _lowerCamelCase						:				Any    			= model(_A						,					labels=_A		)
         self.parent.assertEqual(
             result.logits.shape						,					(self.batch_size, self.num_labels, self.image_size, self.image_size)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				List[str]    			= self.prepare_config_and_inputs()
         _lowerCamelCase						:				str    			= config_and_inputs
         _lowerCamelCase						:				int    			= {"pixel_values": pixel_values}
         return config, inputs_dict






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

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= DPTModelTester(self		)
         _lowerCamelCase						:				int    			= ConfigTester(self						,					config_class=_A						,					has_text_modality=_A						,					hidden_size=3_7		)




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         self.config_tester.run_common_tests()




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






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= self.model_tester.prepare_config_and_inputs_for_common()

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




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= self.model_tester.prepare_config_and_inputs_for_common()

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

                _lowerCamelCase						:				Dict    			= ["pixel_values"]
                self.assertListEqual(arg_names[:1]						,					_A		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*_A		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[Any]    			= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*_A		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






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

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

                if model_class in get_values(_A		):
                       continue

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






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

                _lowerCamelCase						:				Dict    			= self.model_tester.prepare_config_and_inputs_for_common()
                _lowerCamelCase						:				List[str]    			= False
                _lowerCamelCase						:				Optional[int]    			= True

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= self.model_tester.prepare_config_and_inputs_for_common()

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






         """simple docstring"""






         pass




  @slow
  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                _lowerCamelCase						:				Any    			= DPTModel.from_pretrained(_A		)
                self.assertIsNotNone(_A		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= self.model_tester.prepare_config_and_inputs_for_common()
         _lowerCamelCase						:				Dict    			= "add"
         with self.assertRaises(_A		):
                _lowerCamelCase						:				Any    			= DPTForDepthEstimation(_A		)






def        snake_case_  (      ):


       '''simple docstring'''





       _lowerCamelCase						:				Union[str, Any]    			= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       return image







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




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''		)
         _lowerCamelCase						:				Dict    			= DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''		).to(_A		)

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

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

         # verify the predicted depth
         _lowerCamelCase						:				int    			= torch.Size((1, 3_8_4, 3_8_4)		)
         self.assertEqual(predicted_depth.shape						,					_A		)

         _lowerCamelCase						:				Any    			= 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] / 1_0_0						,					_A						,					atol=1E-4		)		)



import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Path   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , lowerCAmelCase							:   str = None   , ):
		"""simple docstring"""







		if config_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

		if generator_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    generator_name_or_path

		if question_encoder_tokenizer_name_or_path is None:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    question_encoder_name_or_path

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

		# Save model.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    RagConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    AutoConfig.from_pretrained(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		int				      =    AutoConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    gen_config
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    question_encoder_config

		SCREAMING_SNAKE_CASE_  :		Dict				      =    model_class.from_pretrained_question_encoder_generator(
		    lowerCAmelCase   , lowerCAmelCase   , config=lowerCAmelCase	)
		rag_model.save_pretrained(lowerCAmelCase	)

		# Sanity check.
		model_class.from_pretrained(lowerCAmelCase	)

		# Save tokenizers.
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoTokenizer.from_pretrained(lowerCAmelCase	)
		question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"	)


if __name__ == "__main__":
		__lowerCamelCase					:	List[Any]       =  argparse.ArgumentParser()
		parser.add_argument(
		    '''--model_type''',
		    choices=['''rag_sequence''', '''rag_token'''],
		    required=True,
		    type=str,
		    help='''RAG model type: rag_sequence, rag_token''',
		)
		parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
		parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
		parser.add_argument(
		    '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
		)

		parser.add_argument(
		    '''--generator_tokenizer_name_or_path''',
		    type=str,
		    help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
		)
		parser.add_argument(
		    '''--question_encoder_tokenizer_name_or_path''',
		    type=str,
		    help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
		)
		parser.add_argument(
		    '''--config_name_or_path''',
		    type=str,
		    help=(
		        '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
		        ''' ``model_type``'''
		    ),
		)

		__lowerCamelCase					:	str       =  parser.parse_args()

		__lowerCamelCase					:	int       =  Path(args.dest)
		dest_dir.mkdir(exist_ok=True)

		consolidate(
		    args.model_type,
		    args.generator_name_or_path,
		    args.question_encoder_name_or_path,
		    dest_dir,
		    args.config_name_or_path,
		    args.generator_tokenizer_name_or_path,
		    args.question_encoder_tokenizer_name_or_path,
		)
