




"""simple docstring"""






from __future__ import annotations

from collections.abc import MutableSequence




class   a__			:





						def __init__(							self      :	List[str]     ,a__      :	int     ,a__      :	MutableSequence[float])						->       None:




											"""simple docstring"""







											if len(a__) != degree + 1:
																raise ValueError(
																    '''The number of coefficients should be equal to the degree + 1.''')

											_lowerCAmelCase:list[float]									=  list(a__)
											_lowerCAmelCase:Optional[Any]									=  degree





						def __add__(							self      :	Any     ,a__      :	Polynomial)						->       Polynomial:




											"""simple docstring"""







											if self.degree > polynomial_a.degree:
																_lowerCAmelCase:Union[str, Any]									=  self.coefficients[:]
																for i in range(polynomial_a.degree + 1):
																					coefficients[i] += polynomial_a.coefficients[i]
																return Polynomial(self.degree     ,a__)
											else:
																_lowerCAmelCase:Tuple									=  polynomial_a.coefficients[:]
																for i in range(self.degree + 1):
																					coefficients[i] += self.coefficients[i]
																return Polynomial(polynomial_a.degree     ,a__)





						def __sub__(							self      :	Optional[int]     ,a__      :	Polynomial)						->       Polynomial:




											"""simple docstring"""







											return self + polynomial_a * Polynomial(0     ,[-1])





						def __neg__(							self      :	Optional[Any])						->       Polynomial:




											"""simple docstring"""







											return Polynomial(self.degree     ,[-c for c in self.coefficients])





						def __mul__(							self      :	Tuple     ,a__      :	Polynomial)						->       Polynomial:




											"""simple docstring"""







											_lowerCAmelCase:list[float]									=  [0] * (self.degree + polynomial_a.degree + 1)
											for i in range(self.degree + 1):
																for j in range(polynomial_a.degree + 1):
																					coefficients[i + j] += (
																					    self.coefficients[i] * polynomial_a.coefficients[j]
																					)

											return Polynomial(self.degree + polynomial_a.degree     ,a__)





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	int | float)						->       int | float:




											"""simple docstring"""







											_lowerCAmelCase:int | float									=  0
											for i in range(self.degree + 1):
																result += self.coefficients[i] * (substitution**i)
											return result





						def __str__(							self      :	Tuple)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  ''''''
											for i in range(self.degree     ,-1     ,-1):
																if self.coefficients[i] == 0:
																					continue
																elif self.coefficients[i] > 0:
																					if polynomial:
																										polynomial += " + "
																else:
																					polynomial += " - "

																if i == 0:
																					polynomial += str(abs(self.coefficients[i]))
																elif i == 1:
																					polynomial += str(abs(self.coefficients[i])) + "x"
																else:
																					polynomial += str(abs(self.coefficients[i])) + "x^" + str(a__)

											return polynomial





						def __repr__(							self      :	Any)						->       str:




											"""simple docstring"""







											return self.__str__()





						def       __UpperCamelCase     (							self      :	int)						->       Polynomial:




											"""simple docstring"""







											_lowerCAmelCase:list[float]									=  [0] * self.degree
											for i in range(self.degree):
																_lowerCAmelCase:Optional[int]									=  self.coefficients[i + 1] * (i + 1)
											return Polynomial(self.degree - 1     ,a__)





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	int | float = 0)						->       Polynomial:




											"""simple docstring"""







											_lowerCAmelCase:list[float]									=  [0] * (self.degree + 2)
											_lowerCAmelCase:List[str]									=  constant
											for i in range(self.degree + 1):
																_lowerCAmelCase:Dict									=  self.coefficients[i] / (i + 1)
											return Polynomial(self.degree + 1     ,a__)





						def __eq__(							self      :	Any     ,a__      :	object)						->       bool:




											"""simple docstring"""







											if not isinstance(a__     ,a__):
																return False

											if self.degree != polynomial_a.degree:
																return False

											for i in range(self.degree + 1):
																if self.coefficients[i] != polynomial_a.coefficients[i]:
																					return False

											return True







						def __ne__(							self      :	Tuple     ,a__      :	object)						->       bool:




											"""simple docstring"""







											return not self.__eq__(a__)









"""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   a__			:





						def __init__(							self      :	List[str]     ,a__      :	List[Any]     ,a__      :	List[str]=2     ,a__      :	Optional[Any]=32     ,a__      :	int=16     ,a__      :	Dict=3     ,a__      :	Optional[int]=True     ,a__      :	int=True     ,a__      :	Optional[Any]=32     ,a__      :	str=4     ,a__      :	Tuple=[0, 1, 2, 3]     ,a__      :	Any=4     ,a__      :	int=37     ,a__      :	int="gelu"     ,a__      :	Optional[int]=0.1     ,a__      :	List[Any]=0.1     ,a__      :	Optional[Any]=0.02     ,a__      :	str=3     ,a__      :	str=[1, 384, 24, 24]     ,a__      :	Optional[Any]=True     ,a__      :	Tuple=None     ,)						->       Optional[Any]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Tuple)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

											_lowerCAmelCase:Optional[Any]									=  self.get_config()

											return config, pixel_values, labels





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




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  {
											    '''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=a__     ,initializer_range=self.initializer_range     ,is_hybrid=self.is_hybrid     ,backbone_config=a__     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Tuple     ,a__      :	Tuple     ,a__      :	Tuple)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  DPTModel(config=a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:Optional[Any]									=  model(a__)
											self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))





						def       __UpperCamelCase     (							self      :	str     ,a__      :	Optional[int]     ,a__      :	Union[str, Any]     ,a__      :	int)						->       Optional[int]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	Dict     ,a__      :	List[Any]     ,a__      :	str)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.num_labels
											_lowerCAmelCase:str									=  DPTForSemanticSegmentation(a__)
											model.to(a__)
											model.eval()
											_lowerCAmelCase:int									=  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      :	Optional[int])						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Any									=  config_and_inputs
											_lowerCAmelCase:Union[str, Any]									=  {'''pixel_values''': pixel_values}
											return config, inputs_dict




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

						snake_case__             = False
						snake_case__             = False
						snake_case__             = False





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  DPTModelTester(self)
											_lowerCAmelCase:List[str]									=  ConfigTester(self     ,config_class=a__     ,has_text_modality=a__     ,hidden_size=37)





						def       __UpperCamelCase     (							self      :	str)						->       Optional[int]:




											"""simple docstring"""







											self.config_tester.run_common_tests()





						@unittest.skip(reason='''DPT does not use inputs_embeds''')
						def       __UpperCamelCase     (							self      :	Optional[int])						->       Dict:




											"""simple docstring"""







											pass





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:Union[str, Any]									=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																_lowerCAmelCase:Union[str, Any]									=  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       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:Any									=  self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																_lowerCAmelCase:int									=  model_class(a__)
																_lowerCAmelCase:Union[str, Any]									=  inspect.signature(model.forward)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_lowerCAmelCase:Any									=  [*signature.parameters.keys()]

																_lowerCAmelCase:Any									=  ['''pixel_values''']
																self.assertListEqual(arg_names[:1]     ,a__)





						def       __UpperCamelCase     (							self      :	Tuple)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*a__)





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




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*a__)





						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







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

																_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  self.model_tester.prepare_config_and_inputs_for_common()
																_lowerCAmelCase:Union[str, Any]									=  True

																if model_class in get_values(a__):
																					continue

																_lowerCAmelCase:Union[str, Any]									=  model_class(a__)
																model.to(a__)
																model.train()
																_lowerCAmelCase:Optional[int]									=  self._prepare_for_class(a__     ,a__     ,return_labels=a__)
																_lowerCAmelCase:Union[str, Any]									=  model(**a__).loss
																loss.backward()





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       List[str]:




											"""simple docstring"""







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

																_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
																_lowerCAmelCase:str									=  False
																_lowerCAmelCase:str									=  True

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





						def       __UpperCamelCase     (							self      :	Any)						->       int:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()

											_lowerCAmelCase:Dict									=  _config_zero_init(a__)
											for model_class in self.all_model_classes:
																_lowerCAmelCase:Tuple									=  model_class(config=a__)
																# Skip the check for the backbone
																_lowerCAmelCase:Tuple									=  []
																for name, module in model.named_modules():
																					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																										_lowerCAmelCase:Tuple									=  [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      :	Dict)						->       Any:




											"""simple docstring"""







											pass





						@slow
						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																_lowerCAmelCase:str									=  DPTModel.from_pretrained(a__)
																self.assertIsNotNone(a__)







						def       __UpperCamelCase     (							self      :	int)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCAmelCase:Any									=  '''add'''
											with self.assertRaises(a__):
																_lowerCAmelCase:Any									=  DPTForDepthEstimation(a__)





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





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





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
											_lowerCAmelCase:Optional[int]									=  DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(a__)

											_lowerCAmelCase:Tuple									=  prepare_img()
											_lowerCAmelCase:List[str]									=  image_processor(images=a__     ,return_tensors='''pt''').to(a__)

											# forward pass
											with torch.no_grad():
																_lowerCAmelCase:Dict									=  model(**a__)
																_lowerCAmelCase:List[str]									=  outputs.predicted_depth

											# verify the predicted depth
											_lowerCAmelCase:str									=  torch.Size((1, 384, 384))
											self.assertEqual(predicted_depth.shape     ,a__)

											_lowerCAmelCase:Any									=  torch.tensor(
											    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]).to(a__)

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






