


"""simple docstring"""





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__						:   int			 = logging.get_logger(__name__)

snake_case__						:   Dict			 = {
    '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}



class 						snake_case_(				a__	):
     __UpperCamelCase     =    '''mvp'''
     __UpperCamelCase     =    ['''past_key_values''']
     __UpperCamelCase     =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






     def __init__(		self     :    Dict    , UpperCamelCase_     :    Union[str, Any]=5_0_2_6_7    , UpperCamelCase_     :    Tuple=1_0_2_4    , UpperCamelCase_     :    int=1_2    , UpperCamelCase_     :    Optional[int]=4_0_9_6    , UpperCamelCase_     :    Union[str, Any]=1_6    , UpperCamelCase_     :    List[Any]=1_2    , UpperCamelCase_     :    Dict=4_0_9_6    , UpperCamelCase_     :    Union[str, Any]=1_6    , UpperCamelCase_     :    List[str]=0.0    , UpperCamelCase_     :    Optional[int]=0.0    , UpperCamelCase_     :    List[str]="gelu"    , UpperCamelCase_     :    List[Any]=1_0_2_4    , UpperCamelCase_     :    List[Any]=0.1    , UpperCamelCase_     :    List[str]=0.0    , UpperCamelCase_     :    List[str]=0.0    , UpperCamelCase_     :    List[Any]=0.02    , UpperCamelCase_     :    List[str]=0.0    , UpperCamelCase_     :    List[str]=False    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    str=1    , UpperCamelCase_     :    Tuple=0    , UpperCamelCase_     :    List[Any]=2    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    Union[str, Any]=2    , UpperCamelCase_     :    Tuple=2    , UpperCamelCase_     :    List[Any]=False    , UpperCamelCase_     :    Optional[int]=1_0_0    , UpperCamelCase_     :    Dict=8_0_0    , **UpperCamelCase_     :    Any    , ):
           lowerCAmelCase    :		str				=      vocab_size
           lowerCAmelCase    :		str				=      max_position_embeddings
           lowerCAmelCase    :		Union[str, Any]				=      d_model
           lowerCAmelCase    :		List[Any]				=      encoder_ffn_dim
           lowerCAmelCase    :		Tuple				=      encoder_layers
           lowerCAmelCase    :		Tuple				=      encoder_attention_heads
           lowerCAmelCase    :		Any				=      decoder_ffn_dim
           lowerCAmelCase    :		Optional[int]				=      decoder_layers
           lowerCAmelCase    :		Any				=      decoder_attention_heads
           lowerCAmelCase    :		Optional[Any]				=      dropout
           lowerCAmelCase    :		Tuple				=      attention_dropout
           lowerCAmelCase    :		Union[str, Any]				=      activation_dropout
           lowerCAmelCase    :		Union[str, Any]				=      activation_function
           lowerCAmelCase    :		int				=      init_std
           lowerCAmelCase    :		str				=      encoder_layerdrop
           lowerCAmelCase    :		str				=      decoder_layerdrop
           lowerCAmelCase    :		Dict				=      classifier_dropout
           lowerCAmelCase    :		List[str]				=      use_cache
           lowerCAmelCase    :		Optional[Any]				=      encoder_layers
           lowerCAmelCase    :		Union[str, Any]				=      scale_embedding  # scale factor will be sqrt(d_model) if True
           lowerCAmelCase    :		Optional[Any]				=      use_prompt
           lowerCAmelCase    :		Tuple				=      prompt_length
           lowerCAmelCase    :		int				=      prompt_mid_dim

           super().__init__(
               pad_token_id=UpperCamelCase_    , bos_token_id=UpperCamelCase_    , eos_token_id=UpperCamelCase_    , is_encoder_decoder=UpperCamelCase_    , decoder_start_token_id=UpperCamelCase_    , forced_eos_token_id=UpperCamelCase_    , **UpperCamelCase_    , )

           if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''    , UpperCamelCase_ ):
                 lowerCAmelCase    :		int				=      self.bos_token_id
                 warnings.warn(
                     F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                     '''The config can simply be saved and uploaded again to be fixed.''' )










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						:    str,  lowerCamelCase						:    Dict,  lowerCamelCase						:    List[str]=2,  lowerCamelCase						:    List[str]=32,  lowerCamelCase						:    Tuple=16,  lowerCamelCase						:    List[Any]=3,  lowerCamelCase						:    List[Any]=True,  lowerCamelCase						:    str=True,  lowerCamelCase						:    Tuple=32,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    Dict=[0, 1, 2, 3],  lowerCamelCase						:    Union[str, Any]=4,  lowerCamelCase						:    Optional[int]=37,  lowerCamelCase						:    Optional[Any]="gelu",  lowerCamelCase						:    List[Any]=0.1,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    Optional[int]=0.02,  lowerCamelCase						:    str=3,  lowerCamelCase						:    str=[1, 384, 24, 24],  lowerCamelCase						:    Optional[int]=True,  lowerCamelCase						:    List[str]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			image_size
								lowercase__    =			patch_size
								lowercase__    =			num_channels
								lowercase__    =			is_training
								lowercase__    =			use_labels
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			backbone_out_indices
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			backbone_featmap_shape
								lowercase__    =			scope
								lowercase__    =			is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								lowercase__    =			(image_size // patch_size) ** 2
								lowercase__    =			num_patches + 1




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

								lowercase__    =			self.get_config()

								return config, pixel_values, labels




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			{
								    '''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=lowerCamelCase,  initializer_range=self.initializer_range,  is_hybrid=self.is_hybrid,  backbone_config=lowerCamelCase,  backbone_featmap_shape=self.backbone_featmap_shape,  )




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			DPTModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			DPTForDepthEstimation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.predicted_depth.shape,  (self.batch_size, self.image_size, self.image_size)  )




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Any,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			DPTForSemanticSegmentation(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(
								    result.logits.shape,  (self.batch_size, self.num_labels, self.image_size, self.image_size)  )




				def 				lowercase__   (     self						:    Dict  ):

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



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				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 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								lowercase__    =			DPTModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase,  hidden_size=37  )




				def 				lowercase__   (     self						:    int  ):

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




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

								'''simple docstring'''
								pass




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

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




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

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

												lowercase__    =			['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase  )




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

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str]  ):

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

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

												if model_class in get_values(lowerCamelCase  ):
																continue

												lowercase__    =			model_class(lowerCamelCase  )
												model.to(lowerCamelCase  )
												model.train()
												lowercase__    =			self._prepare_for_class(lowerCamelCase,  lowerCamelCase,  return_labels=lowerCamelCase  )
												lowercase__    =			model(**lowerCamelCase  ).loss
												loss.backward()




				def 				lowercase__   (     self						:    List[str]  ):

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

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

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




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()

								lowercase__    =			_config_zero_init(lowerCamelCase  )
								for model_class in self.all_model_classes:
												lowercase__    =			model_class(config=lowerCamelCase  )
												# Skip the check for the backbone
												lowercase__    =			[]
												for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																				lowercase__    =			[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 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												lowercase__    =			DPTModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




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

								'''simple docstring'''
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								lowercase__ ,						lowercase__    =			self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__    =			'''add'''
								with self.assertRaises(lowerCamelCase  ):
												lowercase__    =			DPTForDepthEstimation(lowerCamelCase  )






def   a			(						):






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




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

				"""simple docstring"""




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''  )
								lowercase__    =			DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''  ).to(lowerCamelCase  )

								lowercase__    =			prepare_img()
								lowercase__    =			image_processor(images=lowerCamelCase,  return_tensors='''pt'''  ).to(lowerCamelCase  )

								# forward pass
								with torch.no_grad():
												lowercase__    =			model(**lowerCamelCase  )
												lowercase__    =			outputs.predicted_depth

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

								lowercase__    =			torch.tensor(
								    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]  ).to(lowerCamelCase  )

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




