



'''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 	__magic_name__						:







							def __init__(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       Tuple=2	,_UpperCAmelCase					:       Optional[int]=32	,_UpperCAmelCase					:       Any=16	,_UpperCAmelCase					:       Tuple=3	,_UpperCAmelCase					:       Any=True	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       Union[str, Any]=32	,_UpperCAmelCase					:       Optional[int]=4	,_UpperCAmelCase					:       str=[0, 1, 2, 3]	,_UpperCAmelCase					:       Optional[Any]=4	,_UpperCAmelCase					:       int=37	,_UpperCAmelCase					:       int="gelu"	,_UpperCAmelCase					:       Tuple=0.1	,_UpperCAmelCase					:       Dict=0.1	,_UpperCAmelCase					:       List[Any]=0.02	,_UpperCAmelCase					:       str=3	,_UpperCAmelCase					:       Dict=[1, 384, 24, 24]	,_UpperCAmelCase					:       str=True	,_UpperCAmelCase					:       Any=None	,):
									_a     :      Optional[Any]        =							parent
									_a     :      Any        =							batch_size
									_a     :      str        =							image_size
									_a     :      Any        =							patch_size
									_a     :      Dict        =							num_channels
									_a     :      int        =							is_training
									_a     :      str        =							use_labels
									_a     :      List[Any]        =							hidden_size
									_a     :      Dict        =							num_hidden_layers
									_a     :      int        =							backbone_out_indices
									_a     :      Any        =							num_attention_heads
									_a     :      Dict        =							intermediate_size
									_a     :      Dict        =							hidden_act
									_a     :      Optional[int]        =							hidden_dropout_prob
									_a     :      Dict        =							attention_probs_dropout_prob
									_a     :      Union[str, Any]        =							initializer_range
									_a     :      Tuple        =							num_labels
									_a     :      Any        =							backbone_featmap_shape
									_a     :      Any        =							scope
									_a     :      Dict        =							is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									_a     :      List[str]        =							(image_size // patch_size) ** 2
									_a     :      Tuple        =							num_patches + 1







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Dict        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a     :      Dict        =							None
									if self.use_labels:
											_a     :      Optional[int]        =							ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels		)

									_a     :      Any        =							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      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=_UpperCAmelCase	,initializer_range=self.initializer_range	,is_hybrid=self.is_hybrid	,backbone_config=_UpperCAmelCase	,backbone_featmap_shape=self.backbone_featmap_shape	,)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Tuple        =							DPTModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Optional[int]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Union[str, Any]		):
									_a     :      Dict        =							self.num_labels
									_a     :      List[Any]        =							DPTForDepthEstimation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Union[str, Any]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       str		):
									_a     :      Union[str, Any]        =							self.num_labels
									_a     :      Union[str, Any]        =							DPTForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Tuple        =							model(_UpperCAmelCase	,labels=_UpperCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape	,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Dict        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      Any        =							config_and_inputs
									_a     :      Optional[Any]        =							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    List[Any]   				=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							lowerCAmelCase       :    Tuple   				=					(
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    Dict   				=					False
							lowerCAmelCase       :    Any   				=					False
							lowerCAmelCase       :    Optional[Any]   				=					False







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Union[str, Any]        =							DPTModelTester(self		)
									_a     :      List[Any]        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase	,hidden_size=37		)







							def 							__lowercase			(							self					:       Dict		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='DPT does not use inputs_embeds'		)
							def 							__lowercase			(							self					:       List[Any]		):
									pass







							def 							__lowercase			(							self					:       str		):
									_a       ,					_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

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







							def 							__lowercase			(							self					:       Dict		):
									_a       ,					_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

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

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







							def 							__lowercase			(							self					:       Any		):
									_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







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

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

											if model_class in get_values(_UpperCAmelCase		):
													continue

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







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

											_a       ,					_a     :      Tuple        =							self.model_tester.prepare_config_and_inputs_for_common()
											_a     :      Optional[int]        =							False
											_a     :      Optional[int]        =							True

											if model_class in get_values(_UpperCAmelCase		) or not model_class.supports_gradient_checkpointing:
													continue
											_a     :      Any        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a     :      Optional[Any]        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      str        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a       ,					_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

									_a     :      List[str]        =							_config_zero_init(_UpperCAmelCase		)
									for model_class in self.all_model_classes:
											_a     :      Optional[Any]        =							model_class(config=_UpperCAmelCase		)
											# Skip the check for the backbone
											_a     :      Optional[Any]        =							[]
											for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
															_a     :      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 							__lowercase			(							self					:       Tuple		):
									pass







							@slow
							def 							__lowercase			(							self					:       Tuple		):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_a     :      int        =							DPTModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									_a       ,					_a     :      Any        =							self.model_tester.prepare_config_and_inputs_for_common()
									_a     :      int        =							'add'
									with self.assertRaises(_UpperCAmelCase		):
											_a     :      Dict        =							DPTForDepthEstimation(_UpperCAmelCase		)



def      __lowerCamelCase     (      )			->      Tuple:
		_a     :      List[Any]        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

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







							def 							__lowercase			(							self					:       str		):
									_a     :      Optional[int]        =							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_a     :      List[Any]        =							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_UpperCAmelCase		)

									_a     :      Optional[int]        =							prepare_img()
									_a     :      str        =							image_processor(images=_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a     :      int        =							model(**_UpperCAmelCase		)
											_a     :      Union[str, Any]        =							outputs.predicted_depth

									# verify the predicted depth
									_a     :      List[str]        =							torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape	,_UpperCAmelCase		)

									_a     :      Optional[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(_UpperCAmelCase		)

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











"""simple docstring"""





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__A											=		TypeVar("""KEY""")
__A											=		TypeVar("""VAL""")






@dataclass(frozen=a  ,   slots=a       )
class 					_lowerCAmelCase   (     Generic[KEY, VAL]       ):
       """simple docstring"""




       __magic_name__     :KEY
       __magic_name__     :VAL






class 					_lowerCAmelCase   (     _Item       ):
       """simple docstring"""




       def __init__(    self      ):



         '''simple docstring'''

         super().__init__(__UpperCAmelCase    , __UpperCAmelCase      )






       def __bool__(    self      ):



         '''simple docstring'''

         return False






__A											=		_DeletedItem()






class 					_lowerCAmelCase   (     MutableMapping[KEY, VAL]       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase = 8    , __UpperCAmelCase = 0.75      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							initial_block_size
         lowerCAmelCase__			:list[_Item | None]											=							[None] * initial_block_size
         assert 0.0 < capacity_factor < 1.0
         lowerCAmelCase__			:Tuple											=							capacity_factor
         lowerCAmelCase__			:str											=							0






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return hash(__UpperCAmelCase      ) % len(self._buckets      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         return (ind + 1) % len(self._buckets      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							self._buckets[ind]
         if not stored:
           lowerCAmelCase__			:Dict											=							_Item(__UpperCAmelCase    , __UpperCAmelCase      )
           self._len += 1
           return True
         elif stored.key == key:
           lowerCAmelCase__			:Optional[Any]											=							_Item(__UpperCAmelCase    , __UpperCAmelCase      )
           return True
         else:
           return False






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							len(self._buckets      ) * self._capacity_factor
         return len(self      ) >= int(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         if len(self._buckets      ) <= self._initial_block_size:
           return False
         lowerCAmelCase__			:Optional[Any]											=							len(self._buckets      ) * self._capacity_factor / 2
         return len(self      ) < limit






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							self._buckets
         lowerCAmelCase__			:Tuple											=							[None] * new_size
         lowerCAmelCase__			:List[Any]											=							0
         for item in old_buckets:
           if item:
             self._add_item(item.key    , item.val      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self._resize(len(self._buckets      ) * 2      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self._resize(len(self._buckets      ) // 2      )






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							self._get_bucket_index(__UpperCAmelCase      )
         for _ in range(len(self._buckets      )      ):
           yield ind
           lowerCAmelCase__			:Tuple											=							self._get_next_ind(__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         for ind in self._iterate_buckets(__UpperCAmelCase      ):
           if self._try_set(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):
             break






       def __setitem__(    self    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

         if self._is_full():
           self._size_up()

         self._add_item(__UpperCAmelCase    , __UpperCAmelCase      )






       def __delitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for ind in self._iterate_buckets(__UpperCAmelCase      ):
           lowerCAmelCase__			:int											=							self._buckets[ind]
           if item is None:
             raise KeyError(__UpperCAmelCase      )
           if item is _deleted:
             continue
           if item.key == key:
             lowerCAmelCase__			:List[str]											=							_deleted
             self._len -= 1
             break
         if self._is_sparse():
           self._size_down()






       def __getitem__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         for ind in self._iterate_buckets(__UpperCAmelCase      ):
           lowerCAmelCase__			:str											=							self._buckets[ind]
           if item is None:
             break
           if item is _deleted:
             continue
           if item.key == key:
             return item.val
         raise KeyError(__UpperCAmelCase      )






       def __len__(    self      ):



         '''simple docstring'''

         return self._len






       def __iter__(    self      ):



         '''simple docstring'''

         yield from (item.key for item in self._buckets if item)






       def __repr__(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							' ,'.join(
             F"{item.key}: {item.val}" for item in self._buckets if item      )
         return F"HashMap({val_string})"
