


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       lowerCamelCase__   :





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=2		,     UpperCAmelCase=3_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=3		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=3_2		,     UpperCAmelCase=4		,     UpperCAmelCase=[0, 1, 2, 3]		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.02		,     UpperCAmelCase=3		,     UpperCAmelCase=[1, 3_8_4, 2_4, 2_4]		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     )     ->   List[str]:
      _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      __A					(self       )     ->   int:
      _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      __A					(self       )     ->   Any:
      _lowercase							      ={
          '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=_UpperCamelCase		,     initializer_range=self.initializer_range		,     is_hybrid=self.is_hybrid		,     backbone_config=_UpperCamelCase		,     backbone_featmap_shape=self.backbone_featmap_shape		,     )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =DPTModel(config=_UpperCamelCase       )
      model.to(_UpperCamelCase       )
      model.eval()
      _lowercase							      =model(_UpperCamelCase       )
      self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Tuple:
      _lowercase							      =self.num_labels
      _lowercase							      =DPTForDepthEstimation(_UpperCamelCase       )
      model.to(_UpperCamelCase       )
      model.eval()
      _lowercase							      =model(_UpperCamelCase       )
      self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[Any]:
      _lowercase							      =self.num_labels
      _lowercase							      =DPTForSemanticSegmentation(_UpperCamelCase       )
      model.to(_UpperCamelCase       )
      model.eval()
      _lowercase							      =model(_UpperCamelCase		,     labels=_UpperCamelCase       )
      self.parent.assertEqual(
          result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size, self.image_size)       )





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =self.prepare_config_and_inputs()
      _lowercase							      =config_and_inputs
      _lowercase							      ={'pixel_values': pixel_values}
      return config, inputs_dict







@require_torch
class       lowerCamelCase__   (       _snake_case   ,							_snake_case   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''depth-estimation''': DPTForDepthEstimation,
             '''feature-extraction''': DPTModel,
             '''image-segmentation''': DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =DPTModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=_UpperCamelCase		,     has_text_modality=_UpperCamelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   Tuple:
      self.config_tester.run_common_tests()





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





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

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





     def      __A					(self       )     ->   Dict:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase							      =model_class(_UpperCamelCase       )
       _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]		,     _UpperCamelCase       )





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCamelCase       )





     def      __A					(self       )     ->   int:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*_UpperCamelCase       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase       )





     def      __A					(self       )     ->   Optional[Any]:
      for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
        continue

       _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
       _lowercase							      =True

       if model_class in get_values(_UpperCamelCase       ):
        continue

       _lowercase							      =model_class(_UpperCamelCase       )
       model.to(_UpperCamelCase       )
       model.train()
       _lowercase							      =self._prepare_for_class(_UpperCamelCase		,     _UpperCamelCase		,     return_labels=_UpperCamelCase       )
       _lowercase							      =model(**_UpperCamelCase       ).loss
       loss.backward()





     def      __A					(self       )     ->   str:
      for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
        continue

       _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
       _lowercase							      =False
       _lowercase							      =True

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





     def      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()

      _lowercase							      =_config_zero_init(_UpperCamelCase       )
      for model_class in self.all_model_classes:
       _lowercase							      =model_class(config=_UpperCamelCase       )
       # 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      __A					(self       )     ->   int:
      pass





     @slow
     def      __A					(self       )     ->   Optional[int]:
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       _lowercase							      =DPTModel.from_pretrained(_UpperCamelCase       )
       self.assertIsNotNone(_UpperCamelCase       )





     def      __A					(self       )     ->   str:
      # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
      _lowercase							      =self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase							      ='add'
      with self.assertRaises(_UpperCamelCase       ):
       _lowercase							      =DPTForDepthEstimation(_UpperCamelCase       )



def 				UpperCAmelCase_					(							)     ->			str:







 """simple docstring"""


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







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





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''       )
      _lowercase							      =DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''       ).to(_UpperCamelCase       )

      _lowercase							      =prepare_img()
      _lowercase							      =image_processor(images=_UpperCamelCase		,     return_tensors='''pt'''       ).to(_UpperCamelCase       )

      # forward pass
      with torch.no_grad():
       _lowercase							      =model(**_UpperCamelCase       )
       _lowercase							      =outputs.predicted_depth

      # verify the predicted depth
      _lowercase							      =torch.Size((1, 3_8_4, 3_8_4)       )
      self.assertEqual(predicted_depth.shape		,     _UpperCamelCase       )

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

      self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0		,     _UpperCamelCase		,     atol=1e-4       )       )









from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__UpperCAmelCase								=						logging.get_logger(__name__)



@add_end_docstrings(_snake_case			)
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							int:
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )
							self.check_model_type(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						{}, {}
							if padding is not None:
												UpperCAmelCase_    :      List[str]            =						padding
							if truncation is not None:
												UpperCAmelCase_    :      Tuple            =						truncation
							if top_k is not None:
												UpperCAmelCase_    :      Dict            =						top_k
							return preprocess_params, {}, postprocess_params



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							int:
							if isinstance(_UpperCamelCase				,		(Image.Image, str)   ) and isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[Any]            =						{'image': image, 'question': question}
							else:
												UpperCAmelCase_    :      List[str]            =						image
							UpperCAmelCase_    :      Optional[Any]            =						super().__call__(_UpperCamelCase				,		**_UpperCamelCase   )
							return results



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=False   )	->							Optional[Any]:
							UpperCAmelCase_    :      List[Any]            =						load_image(inputs['image']   )
							UpperCAmelCase_    :      Dict            =						self.tokenizer(
							    inputs['question']				,		return_tensors=self.framework				,		padding=_UpperCamelCase				,		truncation=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						self.image_processor(images=_UpperCamelCase				,		return_tensors=self.framework   )
							model_inputs.update(_UpperCamelCase   )
							return model_inputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							UpperCAmelCase_    :      Any            =						self.model(**_UpperCamelCase   )
							return model_outputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=5   )	->							str:
							if top_k > self.model.config.num_labels:
												UpperCAmelCase_    :      Union[str, Any]            =						self.model.config.num_labels

							if self.framework == "pt":
												UpperCAmelCase_    :      List[str]            =						model_outputs.logits.sigmoid()[0]
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						probs.topk(_UpperCamelCase   )
							else:
												raise ValueError(f"Unsupported framework: {self.framework}"   )

							UpperCAmelCase_    :      Optional[Any]            =						scores.tolist()
							UpperCAmelCase_    :      Tuple            =						ids.tolist()
							return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase				,		_UpperCamelCase   )]






