




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__       :


				"""simple docstring"""





				def __init__(     self					:	List[str]    ,   lowercase__					:	int    ,   lowercase__					:	Tuple=2    ,   lowercase__					:	List[Any]=3_2    ,   lowercase__					:	str=1_6    ,   lowercase__					:	Optional[Any]=3    ,   lowercase__					:	Tuple=True    ,   lowercase__					:	Dict=True    ,   lowercase__					:	Optional[int]=3_2    ,   lowercase__					:	Optional[int]=4    ,   lowercase__					:	Optional[int]=[0, 1, 2, 3]    ,   lowercase__					:	int=4    ,   lowercase__					:	Optional[int]=3_7    ,   lowercase__					:	List[Any]="gelu"    ,   lowercase__					:	List[Any]=0.1    ,   lowercase__					:	Dict=0.1    ,   lowercase__					:	int=0.0_2    ,   lowercase__					:	Optional[Any]=3    ,   lowercase__					:	Dict=[1, 3_8_4, 2_4, 2_4]    ,   lowercase__					:	Tuple=True    ,   lowercase__					:	List[str]=None    ,   ):
						__lowercase				:		Optional[Any]												=				parent
						__lowercase				:		Any												=				batch_size
						__lowercase				:		Tuple												=				image_size
						__lowercase				:		Any												=				patch_size
						__lowercase				:		Optional[int]												=				num_channels
						__lowercase				:		Union[str, Any]												=				is_training
						__lowercase				:		Union[str, Any]												=				use_labels
						__lowercase				:		Union[str, Any]												=				hidden_size
						__lowercase				:		Optional[int]												=				num_hidden_layers
						__lowercase				:		Tuple												=				backbone_out_indices
						__lowercase				:		str												=				num_attention_heads
						__lowercase				:		Optional[Any]												=				intermediate_size
						__lowercase				:		Optional[int]												=				hidden_act
						__lowercase				:		Optional[int]												=				hidden_dropout_prob
						__lowercase				:		Union[str, Any]												=				attention_probs_dropout_prob
						__lowercase				:		Optional[int]												=				initializer_range
						__lowercase				:		Tuple												=				num_labels
						__lowercase				:		Dict												=				backbone_featmap_shape
						__lowercase				:		Any												=				scope
						__lowercase				:		Any												=				is_hybrid
						# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
						__lowercase				:		Optional[Any]												=				(image_size // patch_size) ** 2
						__lowercase				:		List[Any]												=				num_patches + 1



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Dict												=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

						__lowercase				:		List[Any]												=				None
						if self.use_labels:
								__lowercase				:		int												=				ids_tensor([self.batch_size, self.image_size, self.image_size]    ,   self.num_labels						)

						__lowercase				:		List[Any]												=				self.get_config()

						return config, pixel_values, labels



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Dict												=				{
						    "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=_lowercase    ,   initializer_range=self.initializer_range    ,   is_hybrid=self.is_hybrid    ,   backbone_config=_lowercase    ,   backbone_featmap_shape=self.backbone_featmap_shape    ,   )



				def       snake_case    (     self					:	int    ,   lowercase__					:	List[Any]    ,   lowercase__					:	str    ,   lowercase__					:	Any						):
						__lowercase				:		Optional[int]												=				DPTModel(config=_lowercase						)
						model.to(_lowercase						)
						model.eval()
						__lowercase				:		Optional[Any]												=				model(_lowercase						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)						)



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



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	Union[str, Any]    ,   lowercase__					:	Tuple    ,   lowercase__					:	List[Any]						):
						__lowercase				:		Union[str, Any]												=				self.num_labels
						__lowercase				:		List[str]												=				DPTForSemanticSegmentation(_lowercase						)
						model.to(_lowercase						)
						model.eval()
						__lowercase				:		int												=				model(_lowercase    ,   labels=_lowercase						)
						self.parent.assertEqual(
						    result.logits.shape    ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)						)



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		List[Any]												=				self.prepare_config_and_inputs()
						__lowercase						,__lowercase						,__lowercase				:		Optional[Any]												=				config_and_inputs
						__lowercase				:		Optional[Any]												=				{"pixel_values": pixel_values}
						return config, inputs_dict





@require_torch
class       lowerCAmelCase__       (			UpperCAmelCase_  ,			UpperCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     str       =      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				__UpperCAmelCase :     Optional[int]       =      (
				    {
				        "depth-estimation": DPTForDepthEstimation,
				        "feature-extraction": DPTModel,
				        "image-segmentation": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__UpperCAmelCase :     List[Any]       =      False
				__UpperCAmelCase :     List[Any]       =      False
				__UpperCAmelCase :     Any       =      False



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		List[Any]												=				DPTModelTester(self						)
						__lowercase				:		Dict												=				ConfigTester(self    ,   config_class=_lowercase    ,   has_text_modality=_lowercase    ,   hidden_size=3_7						)



				def       snake_case    (     self					:	Union[str, Any]						):
						self.config_tester.run_common_tests()



				@unittest.skip(reason="DPT does not use inputs_embeds"						)
				def       snake_case    (     self					:	Optional[Any]						):
						pass



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase						,__lowercase				:		Union[str, Any]												=				self.model_tester.prepare_config_and_inputs_for_common()

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



				def       snake_case    (     self					:	Dict						):
						__lowercase						,__lowercase				:		Tuple												=				self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase				:		Union[str, Any]												=				["pixel_values"]
								self.assertListEqual(arg_names[:1]    ,   _lowercase						)



				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		Dict												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*_lowercase						)



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



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Any												=				self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase						)



				def       snake_case    (     self					:	Union[str, Any]						):
						for model_class in self.all_model_classes:
								if model_class.__name__ == "DPTForDepthEstimation":
										continue

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

								if model_class in get_values(_lowercase						):
										continue

								__lowercase				:		Union[str, Any]												=				model_class(_lowercase						)
								model.to(_lowercase						)
								model.train()
								__lowercase				:		Dict												=				self._prepare_for_class(_lowercase    ,   _lowercase    ,   return_labels=_lowercase						)
								__lowercase				:		Union[str, Any]												=				model(**_lowercase						).loss
								loss.backward()



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

								__lowercase						,__lowercase				:		Union[str, Any]												=				self.model_tester.prepare_config_and_inputs_for_common()
								__lowercase				:		Tuple												=				False
								__lowercase				:		Tuple												=				True

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



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

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



				@slow
				def       snake_case    (     self					:	Tuple						):
						for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
								__lowercase				:		Any												=				DPTModel.from_pretrained(_lowercase						)
								self.assertIsNotNone(_lowercase						)



				def       snake_case    (     self					:	str						):
						# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
						__lowercase						,__lowercase				:		Dict												=				self.model_tester.prepare_config_and_inputs_for_common()
						__lowercase				:		Dict												=				"add"
						with self.assertRaises(_lowercase						):
								__lowercase				:		Optional[Any]												=				DPTForDepthEstimation(_lowercase						)





def 						snake_case__       (					)			->List[str]:






		"""simple docstring"""


		__lowercase				:		List[str]												=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
		return image





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


				"""simple docstring"""





				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Optional[Any]												=				DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"						)
						__lowercase				:		Dict												=				DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"						).to(_lowercase						)

						__lowercase				:		Tuple												=				prepare_img()
						__lowercase				:		Tuple												=				image_processor(images=_lowercase    ,   return_tensors="pt"						).to(_lowercase						)

						# forward pass
						with torch.no_grad():
								__lowercase				:		List[str]												=				model(**_lowercase						)
								__lowercase				:		List[Any]												=				outputs.predicted_depth

						# verify the predicted depth
						__lowercase				:		Optional[Any]												=				torch.Size((1, 3_8_4, 3_8_4)						)
						self.assertEqual(predicted_depth.shape    ,   _lowercase						)

						__lowercase				:		Any												=				torch.tensor(
						    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]						).to(_lowercase						)

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






"""simple docstring"""







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )			->list[float]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Dict												=				coefficient_matrix.shape
		__lowercase						,__lowercase				:		Tuple												=				constant_matrix.shape

		if rowsa != colsa:
				__lowercase				:		str												=				F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if colsa != 1:
				__lowercase				:		Dict												=				F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if rowsa != rowsa:
				__lowercase				:		Optional[Any]												=				(
				    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
				    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if len(_lowerCamelCase				) != rowsa:
				__lowercase				:		Dict												=				(
				    "Number of initial values must be equal to number of rows in coefficient "
				    F'matrix but received {len(_lowerCamelCase				)} and {rowsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if iterations <= 0:
				raise ValueError("Iterations must be at least 1"				)

		__lowercase				:		NDArray[floataa]												=				np.concatenate(
		    (coefficient_matrix, constant_matrix),  axis=1				)

		__lowercase						,__lowercase				:		str												=				table.shape

		strictly_diagonally_dominant(_lowerCamelCase				)

		# Iterates the whole matrix for given number of times
		for _ in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				[]
				for row in range(_lowerCamelCase				):
						__lowercase				:		Optional[Any]												=				0
						for col in range(_lowerCamelCase				):
								if col == row:
										__lowercase				:		Tuple												=				table[row][col]
								elif col == cols - 1:
										__lowercase				:		Tuple												=				table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						__lowercase				:		List[Any]												=				(temp + val) / denom
						new_val.append(_lowerCamelCase				)
				__lowercase				:		Dict												=				new_val

		return [float(_lowerCamelCase				) for i in new_val]

def 						snake_case__       (					_lowerCamelCase				)			->bool:






		"""simple docstring"""


		__lowercase						,__lowercase				:		int												=				table.shape

		__lowercase				:		Optional[int]												=				True

		for i in range(0,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				0
				for j in range(0,  cols - 1				):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError("Coefficient matrix is not strictly diagonally dominant"				)

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()
