



"""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_     :



						"""simple docstring"""






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






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     str:
												UpperCAmelCase			 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

												UpperCAmelCase			 =					self.get_config()

												return config, pixel_values, labels






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:
												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  ,    )






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:Any   )							->     str:
												UpperCAmelCase			 =					DPTModel(config=__A   )
												model.to(__A   )
												model.eval()
												UpperCAmelCase			 =					model(__A   )
												self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:str  ,    lowercase_					:Optional[Any]  ,    lowercase_					:int   )							->     Optional[int]:
												UpperCAmelCase			 =					self.num_labels
												UpperCAmelCase			 =					DPTForDepthEstimation(__A   )
												model.to(__A   )
												model.eval()
												UpperCAmelCase			 =					model(__A   )
												self.parent.assertEqual(result.predicted_depth.shape  ,    (self.batch_size, self.image_size, self.image_size)   )






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Optional[int]  ,    lowercase_					:str  ,    lowercase_					:int   )							->     List[str]:
												UpperCAmelCase			 =					self.num_labels
												UpperCAmelCase			 =					DPTForSemanticSegmentation(__A   )
												model.to(__A   )
												model.eval()
												UpperCAmelCase			 =					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]   )							->     Tuple:
												UpperCAmelCase			 =					self.prepare_config_and_inputs()
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					config_and_inputs
												UpperCAmelCase			 =					{'pixel_values': pixel_values}
												return config, inputs_dict







@require_torch
class  A_     (	_A      ,  _A      ,  unittest.TestCase							):



						"""simple docstring"""
						__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       UpperCAmelCase__	(   self					:str   )							->     Tuple:
												UpperCAmelCase			 =					DPTModelTester(self   )
												UpperCAmelCase			 =					ConfigTester(self  ,    config_class=__A  ,    has_text_modality=__A  ,    hidden_size=37   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     str:
												self.config_tester.run_common_tests()






						@unittest.skip(reason='DPT does not use inputs_embeds'   )
						def       UpperCAmelCase__	(   self					:Tuple   )							->     List[str]:
												pass






						def       UpperCAmelCase__	(   self					:Any   )							->     List[Any]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

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






						def       UpperCAmelCase__	(   self					:Dict   )							->     int:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCAmelCase			 =					['pixel_values']
																		self.assertListEqual(arg_names[:1]  ,    __A   )






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     List[str]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     str:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_depth_estimation(*__A   )






						def       UpperCAmelCase__	(   self					:int   )							->     Any:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*__A   )






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





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

																		UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()
																		UpperCAmelCase			 =					True

																		if model_class in get_values(__A   ):
																								continue

																		UpperCAmelCase			 =					model_class(__A   )
																		model.to(__A   )
																		model.train()
																		UpperCAmelCase			 =					self._prepare_for_class(__A  ,    __A  ,    return_labels=__A   )
																		UpperCAmelCase			 =					model(**__A   ).loss
																		loss.backward()






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:





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

																		UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()
																		UpperCAmelCase			 =					False
																		UpperCAmelCase			 =					True

																		if model_class in get_values(__A   ) or not model_class.supports_gradient_checkpointing:
																								continue
																		UpperCAmelCase			 =					model_class(__A   )
																		model.to(__A   )
																		model.gradient_checkpointing_enable()
																		model.train()
																		UpperCAmelCase			 =					self._prepare_for_class(__A  ,    __A  ,    return_labels=__A   )
																		UpperCAmelCase			 =					model(**__A   ).loss
																		loss.backward()






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Optional[int]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

												UpperCAmelCase			 =					_config_zero_init(__A   )
												for model_class in self.all_model_classes:
																		UpperCAmelCase			 =					model_class(config=__A   )
																		# Skip the check for the backbone
																		UpperCAmelCase			 =					[]
																		for name, module in model.named_modules():
																								if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																														UpperCAmelCase			 =					[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					:Union[str, Any]   )							->     Optional[Any]:
												pass






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





												for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		UpperCAmelCase			 =					DPTModel.from_pretrained(__A   )
																		self.assertIsNotNone(__A   )


def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
						return image







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



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Any   )							->     List[str]:
												UpperCAmelCase			 =					DPTImageProcessor.from_pretrained('Intel/dpt-large'   )
												UpperCAmelCase			 =					DPTForDepthEstimation.from_pretrained('Intel/dpt-large'   ).to(__A   )

												UpperCAmelCase			 =					prepare_img()
												UpperCAmelCase			 =					image_processor(images=__A  ,    return_tensors='pt'   ).to(__A   )

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase			 =					model(**__A   )
																		UpperCAmelCase			 =					outputs.predicted_depth

												# verify the predicted depth
												UpperCAmelCase			 =					torch.Size((1, 3_84, 3_84)   )
												self.assertEqual(predicted_depth.shape  ,    __A   )

												UpperCAmelCase			 =					torch.tensor(
												    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]   ).to(__A   )

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






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												UpperCAmelCase			 =					DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'   )
												UpperCAmelCase			 =					DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'   ).to(__A   )

												UpperCAmelCase			 =					prepare_img()
												UpperCAmelCase			 =					image_processor(images=__A  ,    return_tensors='pt'   ).to(__A   )

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase			 =					model(**__A   )

												# verify the logits
												UpperCAmelCase			 =					torch.Size((1, 1_50, 4_80, 4_80)   )
												self.assertEqual(outputs.logits.shape  ,    __A   )

												UpperCAmelCase			 =					torch.tensor(
												    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]   ).to(__A   )

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






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     str:
												UpperCAmelCase			 =					DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'   )
												UpperCAmelCase			 =					DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'   ).to(__A   )

												UpperCAmelCase			 =					prepare_img()
												UpperCAmelCase			 =					image_processor(images=__A  ,    return_tensors='pt'   ).to(__A   )

												# forward pass
												with torch.no_grad():
																		UpperCAmelCase			 =					model(**__A   )

												UpperCAmelCase			 =					outputs.logits.detach().cpu()

												UpperCAmelCase			 =					image_processor.post_process_semantic_segmentation(outputs=__A  ,    target_sizes=[(5_00, 3_00)]   )
												UpperCAmelCase			 =					torch.Size((5_00, 3_00)   )
												self.assertEqual(segmentation[0].shape  ,    __A   )

												UpperCAmelCase			 =					image_processor.post_process_semantic_segmentation(outputs=__A   )
												UpperCAmelCase			 =					torch.Size((4_80, 4_80)   )
												self.assertEqual(segmentation[0].shape  ,    __A   )










'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	str					, UpperCAmelCase_      :	str					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int					, ) ->							tuple[list[list[int]], list[list[str]]]:
							lowerCAmelCase__  =   list(UpperCAmelCase_						)
							lowerCAmelCase__  =   list(UpperCAmelCase_						)
							lowerCAmelCase__  =   len(UpperCAmelCase_						)
							lowerCAmelCase__  =   len(UpperCAmelCase_						)

							lowerCAmelCase__  =   [
							    [0 for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
							]
							lowerCAmelCase__  =   [
							    ["""0""" for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
							]

							for i in range(1					, len_source_seq + 1						):
														lowerCAmelCase__  =   i * delete_cost
														lowerCAmelCase__  =   F'''D{source_seq[i - 1]:c}'''

							for i in range(1					, len_destination_seq + 1						):
														lowerCAmelCase__  =   i * insert_cost
														lowerCAmelCase__  =   F'''I{destination_seq[i - 1]:c}'''

							for i in range(1					, len_source_seq + 1						):
														for j in range(1					, len_destination_seq + 1						):
																					if source_seq[i - 1] == destination_seq[j - 1]:
																												lowerCAmelCase__  =   costs[i - 1][j - 1] + copy_cost
																												lowerCAmelCase__  =   F'''C{source_seq[i - 1]:c}'''
																					else:
																												lowerCAmelCase__  =   costs[i - 1][j - 1] + replace_cost
																												lowerCAmelCase__  =   F'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]						)

																					if costs[i - 1][j] + delete_cost < costs[i][j]:
																												lowerCAmelCase__  =   costs[i - 1][j] + delete_cost
																												lowerCAmelCase__  =   F'''D{source_seq[i - 1]:c}'''

																					if costs[i][j - 1] + insert_cost < costs[i][j]:
																												lowerCAmelCase__  =   costs[i][j - 1] + insert_cost
																												lowerCAmelCase__  =   F'''I{destination_seq[j - 1]:c}'''

							return costs, ops
def 			_lowerCAmelCase(      UpperCAmelCase_      :	list[list[str]]					, UpperCAmelCase_      :	int					, UpperCAmelCase_      :	int						) ->							list[str]:
							if i == 0 and j == 0:
														return []
							else:
														if ops[i][j][0] == "C" or ops[i][j][0] == "R":
																					lowerCAmelCase__  =   assemble_transformation(UpperCAmelCase_					, i - 1					, j - 1						)
																					seq.append(ops[i][j]						)
																					return seq
														elif ops[i][j][0] == "D":
																					lowerCAmelCase__  =   assemble_transformation(UpperCAmelCase_					, i - 1					, UpperCAmelCase_						)
																					seq.append(ops[i][j]						)
																					return seq
														else:
																					lowerCAmelCase__  =   assemble_transformation(UpperCAmelCase_					, UpperCAmelCase_					, j - 1						)
																					seq.append(ops[i][j]						)
																					return seq


if __name__ == "__main__":
		_UpperCamelCase						, _UpperCamelCase  						=			compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

		_UpperCamelCase  						=			len(operations)
		_UpperCamelCase  						=			len(operations[0])
		_UpperCamelCase  						=			assemble_transformation(operations, m - 1, n - 1)

		_UpperCamelCase  						=			list("""Python""")
		_UpperCamelCase  						=			0
		_UpperCamelCase  						=			0

		with open("""min_cost.txt""", """w""") as file:
				for op in sequence:
						print("""""".join(string))

						if op[0] == "C":
								file.write("""%-16s""" % """Copy %c""" % op[1])
								file.write("""\t\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost -= 1
						elif op[0] == "R":
								_UpperCamelCase  						=			op[2]

								file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
								file.write("""\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost += 1
						elif op[0] == "D":
								string.pop(i)

								file.write("""%-16s""" % """Delete %c""" % op[1])
								file.write("""\t\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost += 2
						else:
								string.insert(i, op[1])

								file.write("""%-16s""" % """Insert %c""" % op[1])
								file.write("""\t\t\t""" + """""".join(string))
								file.write("""\r\n""")

								cost += 2

						i += 1

				print("""""".join(string))
				print("""Cost: """, cost)

				file.write("""\r\nMinimum cost: """ + str(cost))





