




"""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       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Tuple=2      ,SCREAMING_SNAKE_CASE_   :     List[Any]=3_2      ,SCREAMING_SNAKE_CASE_   :     Any=1_6      ,SCREAMING_SNAKE_CASE_   :     int=3      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_   :     List[str]=True      ,SCREAMING_SNAKE_CASE_   :     List[str]=3_2      ,SCREAMING_SNAKE_CASE_   :     List[str]=4      ,SCREAMING_SNAKE_CASE_   :     Any=[0, 1, 2, 3]      ,SCREAMING_SNAKE_CASE_   :     Dict=4      ,SCREAMING_SNAKE_CASE_   :     List[Any]=3_7      ,SCREAMING_SNAKE_CASE_   :     Any="gelu"      ,SCREAMING_SNAKE_CASE_   :     int=0.1      ,SCREAMING_SNAKE_CASE_   :     int=0.1      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=0.02      ,SCREAMING_SNAKE_CASE_   :     int=3      ,SCREAMING_SNAKE_CASE_   :     Tuple=False      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=None      ,):
										snake_case__       :					List[Any]										=					parent
										snake_case__       :					Optional[Any]										=					batch_size
										snake_case__       :					Dict										=					image_size
										snake_case__       :					Optional[int]										=					patch_size
										snake_case__       :					List[Any]										=					num_channels
										snake_case__       :					int										=					is_training
										snake_case__       :					List[str]										=					use_labels
										snake_case__       :					List[str]										=					hidden_size
										snake_case__       :					Tuple										=					num_hidden_layers
										snake_case__       :					Dict										=					backbone_out_indices
										snake_case__       :					Union[str, Any]										=					num_attention_heads
										snake_case__       :					Optional[Any]										=					intermediate_size
										snake_case__       :					Tuple										=					hidden_act
										snake_case__       :					Optional[Any]										=					hidden_dropout_prob
										snake_case__       :					Dict										=					attention_probs_dropout_prob
										snake_case__       :					Tuple										=					initializer_range
										snake_case__       :					Dict										=					num_labels
										snake_case__       :					Optional[int]										=					scope
										snake_case__       :					Tuple										=					is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										snake_case__       :					Optional[Any]										=					(image_size // patch_size) ** 2
										snake_case__       :					Union[str, Any]										=					num_patches + 1

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										snake_case__       :					List[str]										=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

										snake_case__       :					str										=					self.get_config()

										return config, pixel_values, labels

				def       _lowerCamelCase					(   self   :     int      ):
										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=SCREAMING_SNAKE_CASE_      ,initializer_range=self.initializer_range      ,is_hybrid=self.is_hybrid      ,)

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ):
										snake_case__       :					Optional[Any]										=					DPTModel(config=SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case__       :					Optional[Any]										=					model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)      )

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Tuple      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ):
										snake_case__       :					Optional[Any]										=					self.num_labels
										snake_case__       :					List[Any]										=					DPTForDepthEstimation(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case__       :					str										=					model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.predicted_depth.shape      ,(self.batch_size, self.image_size, self.image_size)      )

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     int      ):
										snake_case__       :					Union[str, Any]										=					self.num_labels
										snake_case__       :					Tuple										=					DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case__       :					Union[str, Any]										=					model(SCREAMING_SNAKE_CASE_      ,labels=SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(
										    result.logits.shape      ,(self.batch_size, self.num_labels, self.image_size, self.image_size)      )





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







@require_torch
class       __lowercase       (			snake_case_						,   snake_case_						,   unittest.TestCase	):


				'''simple docstring'''


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

				_lowerCAmelCase				=False
				_lowerCAmelCase				=False
				_lowerCAmelCase				=False

				def       _lowerCamelCase					(   self   :     Dict      ):
										snake_case__       :					Dict										=					DPTModelTester(self      )
										snake_case__       :					Any										=					ConfigTester(self      ,config_class=SCREAMING_SNAKE_CASE_      ,has_text_modality=SCREAMING_SNAKE_CASE_      ,hidden_size=3_7      )

				def       _lowerCamelCase					(   self   :     Tuple      ):
										self.config_tester.run_common_tests()

				@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
				def       _lowerCamelCase					(   self   :     Tuple      ):
										pass

				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__			,	snake_case__       :					Tuple										=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__       :					Optional[int]										=					model_class(SCREAMING_SNAKE_CASE_      )
																self.assertIsInstance(model.get_input_embeddings()      ,(nn.Module)      )
																snake_case__       :					Tuple										=					model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_      ,nn.Linear      )      )

				def       _lowerCamelCase					(   self   :     str      ):
										snake_case__			,	snake_case__       :					int										=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																snake_case__       :					List[Any]										=					model_class(SCREAMING_SNAKE_CASE_      )
																snake_case__       :					Any										=					inspect.signature(model.forward      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																snake_case__       :					Optional[int]										=					[*signature.parameters.keys()]

																snake_case__       :					Optional[int]										=					['''pixel_values''']
																self.assertListEqual(arg_names[:1]      ,SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Dict      ):
										snake_case__       :					Tuple										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ):
										snake_case__       :					Optional[int]										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										snake_case__       :					int										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_      )

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

																snake_case__			,	snake_case__       :					int										=					self.model_tester.prepare_config_and_inputs_for_common()
																snake_case__       :					Union[str, Any]										=					True

																if model_class in get_values(SCREAMING_SNAKE_CASE_      ):
																						continue

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

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

																snake_case__			,	snake_case__       :					Union[str, Any]										=					self.model_tester.prepare_config_and_inputs_for_common()
																snake_case__       :					List[str]										=					False
																snake_case__       :					int										=					True

																if model_class in get_values(SCREAMING_SNAKE_CASE_      ) or not model_class.supports_gradient_checkpointing:
																						continue
																snake_case__       :					Tuple										=					model_class(SCREAMING_SNAKE_CASE_      )
																model.to(SCREAMING_SNAKE_CASE_      )
																model.gradient_checkpointing_enable()
																model.train()
																snake_case__       :					List[Any]										=					self._prepare_for_class(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,return_labels=SCREAMING_SNAKE_CASE_      )
																snake_case__       :					Union[str, Any]										=					model(**SCREAMING_SNAKE_CASE_      ).loss
																loss.backward()

				def       _lowerCamelCase					(   self   :     int      ):
										snake_case__			,	snake_case__       :					Dict										=					self.model_tester.prepare_config_and_inputs_for_common()

										snake_case__       :					Optional[Any]										=					_config_zero_init(SCREAMING_SNAKE_CASE_      )
										for model_class in self.all_model_classes:
																snake_case__       :					Optional[int]										=					model_class(config=SCREAMING_SNAKE_CASE_      )
																# Skip the check for the backbone
																snake_case__       :					Optional[Any]										=					[]
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																												snake_case__       :					List[Any]										=					[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       _lowerCamelCase					(   self   :     Dict      ):
										pass





				@slow
				def       _lowerCamelCase					(   self   :     Any      ):
										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case__       :					List[Any]										=					DPTModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )







def 		lowerCAmelCase							( ):
						'''simple docstring'''


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

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


				'''simple docstring'''


				def       _lowerCamelCase					(   self   :     List[str]      ):
										snake_case__       :					str										=					DPTImageProcessor.from_pretrained('''Intel/dpt-large'''      )
										snake_case__       :					Dict										=					DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''      ).to(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					List[str]										=					prepare_img()
										snake_case__       :					List[Any]										=					image_processor(images=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

										# forward pass
										with torch.no_grad():
																snake_case__       :					List[Any]										=					model(**SCREAMING_SNAKE_CASE_      )
																snake_case__       :					Any										=					outputs.predicted_depth

										# verify the predicted depth
										snake_case__       :					List[Any]										=					torch.Size((1, 3_8_4, 3_8_4)      )
										self.assertEqual(predicted_depth.shape      ,SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Tuple										=					torch.tensor(
										    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]      ).to(SCREAMING_SNAKE_CASE_      )

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

				def       _lowerCamelCase					(   self   :     Optional[int]      ):
										snake_case__       :					Union[str, Any]										=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''      )
										snake_case__       :					int										=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''      ).to(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					int										=					prepare_img()
										snake_case__       :					Any										=					image_processor(images=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

										# forward pass
										with torch.no_grad():
																snake_case__       :					Union[str, Any]										=					model(**SCREAMING_SNAKE_CASE_      )

										# verify the logits
										snake_case__       :					Dict										=					torch.Size((1, 1_5_0, 4_8_0, 4_8_0)      )
										self.assertEqual(outputs.logits.shape      ,SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Tuple										=					torch.tensor(
										    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]      ).to(SCREAMING_SNAKE_CASE_      )

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





				def       _lowerCamelCase					(   self   :     Tuple      ):
										snake_case__       :					str										=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''      )
										snake_case__       :					Tuple										=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''      ).to(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Tuple										=					prepare_img()
										snake_case__       :					Any										=					image_processor(images=SCREAMING_SNAKE_CASE_      ,return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

										# forward pass
										with torch.no_grad():
																snake_case__       :					List[Any]										=					model(**SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Any										=					outputs.logits.detach().cpu()

										snake_case__       :					Any										=					image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_      ,target_sizes=[(5_0_0, 3_0_0)]      )
										snake_case__       :					List[str]										=					torch.Size((5_0_0, 3_0_0)      )
										self.assertEqual(segmentation[0].shape      ,SCREAMING_SNAKE_CASE_      )

										snake_case__       :					Optional[int]										=					image_processor.post_process_semantic_segmentation(outputs=SCREAMING_SNAKE_CASE_      )
										snake_case__       :					int										=					torch.Size((4_8_0, 4_8_0)      )
										self.assertEqual(segmentation[0].shape      ,SCREAMING_SNAKE_CASE_      )








"""simple docstring"""





def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					len(lowerCamelCase		)
						# Create a stack and push index of fist element to it
						snake_case__       :					str										=					[]
						st.append(0		)

						# Span value of first element is always 1
						snake_case__       :					Union[str, Any]										=					1

						# Calculate span values for rest of the elements
						for i in range(1      ,			lowerCamelCase		):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowerCamelCase		) > 0 and price[st[0]] <= price[i]:
																		st.pop()

												# If stack becomes empty, then price[i] is greater
												# than all elements on left of it, i.e. price[0],
												# price[1], ..price[i-1]. Else the price[i]  is
												# greater than elements after top of stack
												snake_case__       :					Dict										=					i + 1 if len(lowerCamelCase		) <= 0 else (i - st[0])

												# Push this element to stack
												st.append(lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						for i in range(0      ,			lowerCamelCase		):
												print(arr[i]      ,			end=''' '''		)


# Driver program to test above function
_lowercase	      =    [10, 4, 5, 90, 1_20, 80]
_lowercase	      =    [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)

print_array(S, len(price))


