




"""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 lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=2    , lowercase=32    , lowercase=16    , lowercase=3    , lowercase=True    , lowercase=True    , lowercase=32    , lowercase=4    , lowercase=[0, 1, 2, 3]    , lowercase=4    , lowercase=37    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=0.02    , lowercase=3    , lowercase=[1, 384, 24, 24]    , lowercase=True    , lowercase=None    , ):
							_lowerCamelCase	:     List[str]       =       parent
							_lowerCamelCase	:     Optional[int]       =       batch_size
							_lowerCamelCase	:     List[Any]       =       image_size
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Tuple       =       num_channels
							_lowerCamelCase	:     Any       =       is_training
							_lowerCamelCase	:     List[str]       =       use_labels
							_lowerCamelCase	:     Optional[Any]       =       hidden_size
							_lowerCamelCase	:     Tuple       =       num_hidden_layers
							_lowerCamelCase	:     Tuple       =       backbone_out_indices
							_lowerCamelCase	:     Optional[Any]       =       num_attention_heads
							_lowerCamelCase	:     List[Any]       =       intermediate_size
							_lowerCamelCase	:     str       =       hidden_act
							_lowerCamelCase	:     int       =       hidden_dropout_prob
							_lowerCamelCase	:     Dict       =       attention_probs_dropout_prob
							_lowerCamelCase	:     Optional[Any]       =       initializer_range
							_lowerCamelCase	:     Dict       =       num_labels
							_lowerCamelCase	:     Any       =       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	:     str       =       (image_size // patch_size) ** 2
							_lowerCamelCase	:     Optional[Any]       =       num_patches + 1
			def    A_       (    self    ):
							_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	:     Dict       =       ids_tensor([self.batch_size, self.image_size, self.image_size]    , self.num_labels    )

							_lowerCamelCase	:     Tuple       =       self.get_config()

							return config, pixel_values, labels
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       {
							    '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=lowercase    , initializer_range=self.initializer_range    , is_hybrid=self.is_hybrid    , backbone_config=lowercase    , backbone_featmap_shape=self.backbone_featmap_shape    , )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Any       =       DPTModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     str       =       model(lowercase    )
							self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Optional[Any]       =       self.num_labels
							_lowerCamelCase	:     Tuple       =       DPTForDepthEstimation(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Any       =       model(lowercase    )
							self.parent.assertEqual(result.predicted_depth.shape    , (self.batch_size, self.image_size, self.image_size)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Tuple       =       self.num_labels
							_lowerCamelCase	:     Union[str, Any]       =       DPTForSemanticSegmentation(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     List[Any]       =       model(lowercase    , labels=lowercase    )
							self.parent.assertEqual(
							    result.logits.shape    , (self.batch_size, self.num_labels, self.image_size, self.image_size)    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       self.prepare_config_and_inputs()
							_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       config_and_inputs
							_lowerCamelCase	:     Any       =       {'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      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    A_       (    self    ):
							_lowerCamelCase	:     str       =       DPTModelTester(self    )
							_lowerCamelCase	:     Optional[int]       =       ConfigTester(self    , config_class=lowercase    , has_text_modality=lowercase    , hidden_size=37    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			@unittest.skip(reason='DPT does not use inputs_embeds'    )
			def    A_       (    self    ):
							pass
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     str       =       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase	:     str       =       model_class(lowercase    )
											self.assertIsInstance(model.get_input_embeddings()    , (nn.Module)    )
											_lowerCamelCase	:     Tuple       =       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowercase    , nn.Linear    )    )
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Dict       =       self.model_tester.prepare_config_and_inputs_for_common()

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

											_lowerCamelCase	:     List[str]       =       ['pixel_values']
											self.assertListEqual(arg_names[:1]    , lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase    )
			def    A_       (    self    ):
							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

											_lowerCamelCase,					_lowerCamelCase	:     str       =       self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCamelCase	:     Dict       =       True

											if model_class in get_values(lowercase    ):
															continue

											_lowerCamelCase	:     int       =       model_class(lowercase    )
											model.to(lowercase    )
											model.train()
											_lowerCamelCase	:     int       =       self._prepare_for_class(lowercase    , lowercase    , return_labels=lowercase    )
											_lowerCamelCase	:     Optional[Any]       =       model(**lowercase    ).loss
											loss.backward()
			def    A_       (    self    ):
							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

											_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       self.model_tester.prepare_config_and_inputs_for_common()
											_lowerCamelCase	:     Dict       =       False
											_lowerCamelCase	:     Optional[int]       =       True

											if model_class in get_values(lowercase    ) or not model_class.supports_gradient_checkpointing:
															continue
											_lowerCamelCase	:     str       =       model_class(lowercase    )
											model.to(lowercase    )
											model.gradient_checkpointing_enable()
											model.train()
											_lowerCamelCase	:     Tuple       =       self._prepare_for_class(lowercase    , lowercase    , return_labels=lowercase    )
											_lowerCamelCase	:     str       =       model(**lowercase    ).loss
											loss.backward()
			def    A_       (    self    ):
							_lowerCamelCase,					_lowerCamelCase	:     Optional[Any]       =       self.model_tester.prepare_config_and_inputs_for_common()

							_lowerCamelCase	:     str       =       _config_zero_init(lowercase    )
							for model_class in self.all_model_classes:
											_lowerCamelCase	:     Dict       =       model_class(config=lowercase    )
											# Skip the check for the backbone
											_lowerCamelCase	:     str       =       []
											for name, module in model.named_modules():
															if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																			_lowerCamelCase	:     str       =       [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    ):
							pass
			@slow
			def    A_       (    self    ):
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_lowerCamelCase	:     List[str]       =       DPTModel.from_pretrained(lowercase    )
											self.assertIsNotNone(lowercase    )






			def    A_       (    self    ):
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							_lowerCamelCase,					_lowerCamelCase	:     List[str]       =       self.model_tester.prepare_config_and_inputs_for_common()
							_lowerCamelCase	:     Dict       =       'add'
							with self.assertRaises(lowercase    ):
											_lowerCamelCase	:     str       =       DPTForDepthEstimation(lowercase    )







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






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






			'''simple docstring'''



			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'    )
							_lowerCamelCase	:     Optional[int]       =       DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'    ).to(lowercase    )

							_lowerCamelCase	:     List[Any]       =       prepare_img()
							_lowerCamelCase	:     Union[str, Any]       =       image_processor(images=lowercase    , return_tensors='pt'    ).to(lowercase    )

							# forward pass
							with torch.no_grad():
											_lowerCamelCase	:     str       =       model(**lowercase    )
											_lowerCamelCase	:     int       =       outputs.predicted_depth

							# verify the predicted depth
							_lowerCamelCase	:     Any       =       torch.Size((1, 384, 384)    )
							self.assertEqual(predicted_depth.shape    , lowercase    )

							_lowerCamelCase	:     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(lowercase    )

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





"""simple docstring"""






from __future__ import annotations







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     int       =       len(lowercase__						)
				# We need to create solution object to save path.
				_lowerCamelCase	:     Tuple       =       [[0 for _ in range(lowercase__						)] for _ in range(lowercase__						)]
				_lowerCamelCase	:     Optional[Any]       =       run_maze(lowercase__				,       0				,       0				,       lowercase__						)
				if solved:
								print('\n'.join(str(lowercase__						) for row in solutions						)						)
				else:
								print('No solution exists!'						)
				return solved







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Optional[int]       =       len(lowercase__						)
				# Final check point.
				if i == j == (size - 1):
								_lowerCamelCase	:     Optional[Any]       =       1
								return True

				_lowerCamelCase	:     List[str]       =       (not i < 0) and (not j < 0)  # Check lower bounds
				_lowerCamelCase	:     List[str]       =       (i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								_lowerCamelCase	:     List[Any]       =       (not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												_lowerCamelCase	:     Union[str, Any]       =       1

												# check for directions
												if (
												    run_maze(lowercase__				,       i + 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j + 1				,       lowercase__						)
												    or run_maze(lowercase__				,       i - 1				,       lowercase__				,       lowercase__						)
												    or run_maze(lowercase__				,       lowercase__				,       j - 1				,       lowercase__						)
												):
																return True

												_lowerCamelCase	:     Dict       =       0
												return False
				return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()