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 			UpperCamelCase					:

     '''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_=False					,					A_=None					,					)			->					Dict:






           """simple docstring"""
           _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    )			->					Tuple:






           """simple docstring"""
           _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    )			->					str:






           """simple docstring"""
           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					,					)



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					DPTModel(config=_a    )
           model.to(_a    )
           model.eval()
           _lowerCamelCase           =					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_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.num_labels
           _lowerCamelCase           =					DPTForDepthEstimation(_a    )
           model.to(_a    )
           model.eval()
           _lowerCamelCase           =					model(_a    )
           self.parent.assertEqual(result.predicted_depth.shape					,					(self.batch_size, self.image_size, self.image_size)    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					self.num_labels
           _lowerCamelCase           =					DPTForSemanticSegmentation(_a    )
           model.to(_a    )
           model.eval()
           _lowerCamelCase           =					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    )			->					Any:






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





@require_torch
class 			UpperCamelCase					(			_a ,	_a ,	unittest.TestCase			):

     '''simple docstring'''

     A_					    =	(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     A_					    =	(
         {
             'depth-estimation': DPTForDepthEstimation,
             'feature-extraction': DPTModel,
             'image-segmentation': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     A_					    =	False
     A_					    =	False
     A_					    =	False



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					DPTModelTester(self    )
           _lowerCamelCase           =					ConfigTester(self					,					config_class=_a					,					has_text_modality=_a					,					hidden_size=37    )



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






           """simple docstring"""
           self.config_tester.run_common_tests()



     @unittest.skip(reason='''DPT does not use inputs_embeds'''    )
     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           pass



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






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs_for_common()

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



     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 _lowerCamelCase           =					model_class(_a    )
                 _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]					,					_a    )



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






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_a    )



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






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*_a    )



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






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*_a    )



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






           """simple docstring"""
           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(_a    ):
                       continue

                 _lowerCamelCase           =					model_class(_a    )
                 model.to(_a    )
                 model.train()
                 _lowerCamelCase           =					self._prepare_for_class(_a					,					_a					,					return_labels=_a    )
                 _lowerCamelCase           =					model(**_a    ).loss
                 loss.backward()



     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           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(_a    ) or not model_class.supports_gradient_checkpointing:
                       continue
                 _lowerCamelCase           =					model_class(_a    )
                 model.to(_a    )
                 model.gradient_checkpointing_enable()
                 model.train()
                 _lowerCamelCase           =					self._prepare_for_class(_a					,					_a					,					return_labels=_a    )
                 _lowerCamelCase           =					model(**_a    ).loss
                 loss.backward()



     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					self.model_tester.prepare_config_and_inputs_for_common()

           _lowerCamelCase           =					_config_zero_init(_a    )
           for model_class in self.all_model_classes:
                 _lowerCamelCase           =					model_class(config=_a    )
                 # 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    )			->					Tuple:






           """simple docstring"""
           pass





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






           """simple docstring"""
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _lowerCamelCase           =					DPTModel.from_pretrained(_a    )
                 self.assertIsNotNone(_a    )





def    __magic_name__(	)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
      return image




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

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					DPTImageProcessor.from_pretrained('''Intel/dpt-large'''    )
           _lowerCamelCase           =					DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''    ).to(_a    )

           _lowerCamelCase           =					prepare_img()
           _lowerCamelCase           =					image_processor(images=_a					,					return_tensors='''pt'''    ).to(_a    )

           # forward pass
           with torch.no_grad():
                 _lowerCamelCase           =					model(**_a    )
                 _lowerCamelCase           =					outputs.predicted_depth

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

           _lowerCamelCase           =					torch.tensor(
               [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]    ).to(_a    )

           self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]					,					_a					,					atol=1E-4    )    )



     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''    )
           _lowerCamelCase           =					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''    ).to(_a    )

           _lowerCamelCase           =					prepare_img()
           _lowerCamelCase           =					image_processor(images=_a					,					return_tensors='''pt'''    ).to(_a    )

           # forward pass
           with torch.no_grad():
                 _lowerCamelCase           =					model(**_a    )

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

           _lowerCamelCase           =					torch.tensor(
               [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]    ).to(_a    )

           self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]					,					_a					,					atol=1E-4    )    )





     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''    )
           _lowerCamelCase           =					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''    ).to(_a    )

           _lowerCamelCase           =					prepare_img()
           _lowerCamelCase           =					image_processor(images=_a					,					return_tensors='''pt'''    ).to(_a    )

           # forward pass
           with torch.no_grad():
                 _lowerCamelCase           =					model(**_a    )

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

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

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




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__UpperCAmelCase    	=  logging.getLogger(__name__)


class 						lowerCamelCase__			:


						def __init__(			self    :	Optional[Any]      ):
													a__:							Tuple									=False


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Any   ,							_a    :	Union[str, Any]   ,							_a    :	List[str]   ,							_a    :	Optional[int]      ):
													if not self.initialized:
																				a__:							Any									=RagRetriever(
																				    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)
																				a__:							str									=True


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.retriever.index.init_index()


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													# delete the old self.retriever object before assigning the new index
													del self.retriever
													a__:							Dict									=False







						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	List[Any]   ,							_a    :	Any      ):
													a__				,      a__:							List[str]									=self.retriever._main_retrieve(_a   ,							_a      )
													a__:							List[str]									=self.retriever.index.get_doc_dicts(_a      )
													return doc_ids, retrieved_doc_embeds, doc_dicts


class 						lowerCamelCase__			(		_a ):


						def __init__(			self    :	Optional[int]   ,							_a    :	List[Any]   ,							_a    :	Any   ,							_a    :	Optional[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	str=None      ):
													if index is not None and index.is_initialized() and len(_a      ) > 0:
																				raise ValueError(
																				    "When using Ray for distributed fine-tuning, "
																				    "you'll need to provide the paths instead, "
																				    "as the dataset and the index are loaded "
																				    "separately. More info in examples/rag/use_own_knowledge_dataset.py "      )

													super().__init__(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)

													a__:							Optional[Any]									=retrieval_workers
													a__:							str									=question_encoder_tokenizer
													a__:							Optional[int]									=generator_tokenizer
													if len(self.retrieval_workers      ) > 0:
																				ray.get(
																				    [
																				        worker.create_rag_retriever.remote(_a   ,							_a   ,							_a   ,							_a      )
																				        for worker in self.retrieval_workers
																				    ]      )


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													logger.info("initializing retrieval"      )

													if len(self.retrieval_workers      ) > 0:
																				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
													else:
																				# Non-distributed training. Load index into this same process.
																				self.index.init_index()


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	Union[str, Any]      ):
													if len(self.retrieval_workers      ) > 0:
																				# Select a random retrieval actor.
																				a__:							Tuple									=self.retrieval_workers[random.randint(0   ,							len(self.retrieval_workers      ) - 1      )]
																				a__				,      a__				,      a__:							Union[str, Any]									=ray.get(
																				    random_worker.retrieve.remote(_a   ,							_a      )      )
													else:
																				a__				,      a__:							List[Any]									=self._main_retrieve(_a   ,							_a      )
																				a__:							List[str]									=self.index.get_doc_dicts(_a      )
													return retrieved_doc_embeds, doc_ids, doc_dicts


						@classmethod
						def 	_lowerCamelCase      (			cls    :	str   ,							_a    :	Union[str, Any]   ,							_a    :	Any=None   ,							**_a    :	Dict      ):
													return super(_a   ,							cls      ).get_tokenizers(_a   ,							_a   ,							**_a      )


						@classmethod
						def 	_lowerCamelCase      (			cls    :	Dict   ,							_a    :	Optional[int]   ,							_a    :	Tuple   ,							_a    :	int=None   ,							**_a    :	List[str]      ):
													a__:							Union[str, Any]									=kwargs.pop("config"   ,							_a      ) or RagConfig.from_pretrained(_a   ,							**_a      )
													a__:							List[Any]									=RagTokenizer.from_pretrained(_a   ,							config=_a      )
													a__:							str									=rag_tokenizer.question_encoder
													a__:							Any									=rag_tokenizer.generator

													if indexed_dataset is not None:
																				a__:							Dict									="custom"
																				a__:							Dict									=CustomHFIndex(config.retrieval_vector_size   ,							_a      )
													else:
																				a__:							List[str]									=cls._build_index(_a      )

													return cls(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							retrieval_workers=_a   ,							index=_a   ,							)







						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													logger.info("re-loading the new dataset with embeddings"      )
													# access from the training loop

													ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

													# build the index object again
													a__:							str									=self._build_index(self.config      )

													ray.get(
													    [
													        worker.create_rag_retriever.remote(
													            self.config   ,							self.question_encoder_tokenizer   ,							self.generator_tokenizer   ,							_a      )
													        for worker in self.retrieval_workers
													    ]      )





