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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_					:			Any										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										lowercase_					:			Union[str, Any]										=       None
										if self.use_labels:
																	lowercase_					:			Any										=       ids_tensor([self.batch_size, self.image_size, self.image_size]				,			self.num_labels    )

										lowercase_					:			Any										=       self.get_config()

										return config, pixel_values, labels







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			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=lowercase_				,			initializer_range=self.initializer_range				,			is_hybrid=self.is_hybrid				,			backbone_config=lowercase_				,			backbone_featmap_shape=self.backbone_featmap_shape				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  Any				,			lowercase_				:  Optional[int]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       DPTModel(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			List[Any]										=       model(lowercase_    )
										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)    )







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  Dict				,			lowercase_				:  str				,			lowercase_				:  str    ):
										lowercase_					:			int										=       self.num_labels
										lowercase_					:			str										=       DPTForSemanticSegmentation(lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Dict										=       model(lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(
										    result.logits.shape				,			(self.batch_size, self.num_labels, self.image_size, self.image_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			Union[str, 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     __magic_name__						(    _UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
			UpperCamelCase__				=       (
			    {
			        '''depth-estimation''': DPTForDepthEstimation,
			        '''feature-extraction''': DPTModel,
			        '''image-segmentation''': DPTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase__				=       False
			UpperCamelCase__				=       False
			UpperCamelCase__				=       False







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Tuple										=       DPTModelTester(self    )
										lowercase_					:			Union[str, Any]										=       ConfigTester(self				,			config_class=lowercase_				,			has_text_modality=lowercase_				,			hidden_size=37    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										self.config_tester.run_common_tests()







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_						,						lowercase_					:			List[str]										=       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_					:			Tuple										=       model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(lowercase_				,			nn.Linear    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_						,						lowercase_					:			Tuple										=       self.model_tester.prepare_config_and_inputs_for_common()

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

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			List[str]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			int										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_    )







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

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

																	if model_class in get_values(lowercase_    ):
																								continue

																	lowercase_					:			Tuple										=       model_class(lowercase_    )
																	model.to(lowercase_    )
																	model.train()
																	lowercase_					:			List[str]										=       self._prepare_for_class(lowercase_				,			lowercase_				,			return_labels=lowercase_    )
																	lowercase_					:			str										=       model(**lowercase_    ).loss
																	loss.backward()







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

																	lowercase_						,						lowercase_					:			List[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()
																	lowercase_					:			Dict										=       False
																	lowercase_					:			Optional[int]										=       True

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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_						,						lowercase_					:			Union[str, Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

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







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																	lowercase_					:			str										=       DPTModel.from_pretrained(lowercase_    )
																	self.assertIsNotNone(lowercase_    )







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




def 							lowerCamelCase	(      )			->      List[str]:
							lowercase_					:			int										=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
							return image





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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Tuple										=       DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""    )
										lowercase_					:			Union[str, Any]										=       DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""    ).to(lowercase_    )

										lowercase_					:			Optional[int]										=       prepare_img()
										lowercase_					:			Any										=       image_processor(images=lowercase_				,			return_tensors="""pt"""    ).to(lowercase_    )

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

										# verify the predicted depth
										lowercase_					:			Union[str, Any]										=       torch.Size((1, 384, 384)    )
										self.assertEqual(predicted_depth.shape				,			lowercase_    )

										lowercase_					:			List[str]										=       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'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

							with open(os.path.join(os.path.dirname(UpperCAmelCase__ )      ,      UpperCAmelCase__ ) ) as input_file:
														lowercase_					:			str										=       [
														    [int(UpperCAmelCase__ ) for element in line.split(""",""" )]
														    for line in input_file.readlines()
														]

							lowercase_					:			Optional[Any]										=       len(UpperCAmelCase__ )
							lowercase_					:			Any										=       len(matrix[0] )

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

							for j in range(1      ,      UpperCAmelCase__ ):
														for i in range(UpperCAmelCase__ ):
																					lowercase_					:			Union[str, Any]										=       minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1      ,      UpperCAmelCase__ ):
																					lowercase_					:			Tuple										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i - 1][j] + matrix[i][j] )

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i + 1][j] + matrix[i][j] )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )


if __name__ == "__main__":
						print(f"""{solution() = }""")





