
"""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						:List[Any]  ,							lowerCamelCase_						:List[Any]  ,							lowerCamelCase_						:str=2  ,							lowerCamelCase_						:Optional[int]=32  ,							lowerCamelCase_						:Any=16  ,							lowerCamelCase_						:Optional[Any]=3  ,							lowerCamelCase_						:int=True  ,							lowerCamelCase_						:int=True  ,							lowerCamelCase_						:str=32  ,							lowerCamelCase_						:str=4  ,							lowerCamelCase_						:List[Any]=[0, 1, 2, 3]  ,							lowerCamelCase_						:Dict=4  ,							lowerCamelCase_						:str=37  ,							lowerCamelCase_						:int="gelu"  ,							lowerCamelCase_						:Union[str, Any]=0.1  ,							lowerCamelCase_						:List[Any]=0.1  ,							lowerCamelCase_						:Any=0.02  ,							lowerCamelCase_						:Optional[Any]=3  ,							lowerCamelCase_						:Tuple=False  ,							lowerCamelCase_						:Dict=None  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  Any  =parent
										lowerCamelCase__			:  Optional[int]  =batch_size
										lowerCamelCase__			:  List[Any]  =image_size
										lowerCamelCase__			:  int  =patch_size
										lowerCamelCase__			:  Dict  =num_channels
										lowerCamelCase__			:  Optional[int]  =is_training
										lowerCamelCase__			:  int  =use_labels
										lowerCamelCase__			:  Optional[Any]  =hidden_size
										lowerCamelCase__			:  Tuple  =num_hidden_layers
										lowerCamelCase__			:  Any  =backbone_out_indices
										lowerCamelCase__			:  Union[str, Any]  =num_attention_heads
										lowerCamelCase__			:  Tuple  =intermediate_size
										lowerCamelCase__			:  Optional[Any]  =hidden_act
										lowerCamelCase__			:  List[Any]  =hidden_dropout_prob
										lowerCamelCase__			:  str  =attention_probs_dropout_prob
										lowerCamelCase__			:  str  =initializer_range
										lowerCamelCase__			:  Optional[int]  =num_labels
										lowerCamelCase__			:  Any  =scope
										lowerCamelCase__			:  List[str]  =is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										lowerCamelCase__			:  int  =(image_size // patch_size) ** 2
										lowerCamelCase__			:  Optional[Any]  =num_patches + 1





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

										lowerCamelCase__			:  Any  =self.get_config()

										return config, pixel_values, labels





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										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=lowerCamelCase_  ,							initializer_range=self.initializer_range  ,							is_hybrid=self.is_hybrid  ,							)





							def    UpperCAmelCase__							(						self						:List[str]  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Optional[int]  ,							lowerCamelCase_						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =DPTModel(config=lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  Tuple  =model(lowerCamelCase_ )
										self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, self.seq_length, self.hidden_size) )





							def    UpperCAmelCase__							(						self						:Union[str, Any]  ,							lowerCamelCase_						:List[Any]  ,							lowerCamelCase_						:List[str]  ,							lowerCamelCase_						:str ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  =self.num_labels
										lowerCamelCase__			:  Dict  =DPTForDepthEstimation(lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  int  =model(lowerCamelCase_ )
										self.parent.assertEqual(result.predicted_depth.shape  ,							(self.batch_size, self.image_size, self.image_size) )





							def    UpperCAmelCase__							(						self						:Dict  ,							lowerCamelCase_						:Optional[Any]  ,							lowerCamelCase_						:Optional[Any]  ,							lowerCamelCase_						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  =self.num_labels
										lowerCamelCase__			:  Optional[int]  =DPTForSemanticSegmentation(lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  Dict  =model(lowerCamelCase_  ,							labels=lowerCamelCase_ )
										self.parent.assertEqual(
										    result.logits.shape  ,							(self.batch_size, self.num_labels, self.image_size, self.image_size) )





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  =self.prepare_config_and_inputs()
										lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__			:  Any  =config_and_inputs
										lowerCamelCase__			:  Tuple  ={'pixel_values': pixel_values}
										return config, inputs_dict






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



							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_			   =     False
							SCREAMING_SNAKE_CASE_			   =     False
							SCREAMING_SNAKE_CASE_			   =     False





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =DPTModelTester(self )
										lowerCamelCase__			:  Optional[int]  =ConfigTester(self  ,							config_class=lowerCamelCase_  ,							has_text_modality=lowerCamelCase_  ,							hidden_size=37 )





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										self.config_tester.run_common_tests()





							@unittest.skip(reason='DPT does not use inputs_embeds' )
							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										pass





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  int  =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase__			:  Optional[Any]  =model_class(lowerCamelCase_ )
													self.assertIsInstance(model.get_input_embeddings()  ,							(nn.Module) )
													lowerCamelCase__			:  Any  =model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCamelCase_  ,							nn.Linear ) )





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

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

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

													lowerCamelCase__			:  str  =['pixel_values']
													self.assertListEqual(arg_names[:1]  ,							lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, Any]  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

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

													lowerCamelCase__ ,       lowerCamelCase__			:  List[str]  =self.model_tester.prepare_config_and_inputs_for_common()
													lowerCamelCase__			:  Dict  =True

													if model_class in get_values(lowerCamelCase_ ):
																continue

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





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

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

													lowerCamelCase__ ,       lowerCamelCase__			:  List[Any]  =self.model_tester.prepare_config_and_inputs_for_common()
													lowerCamelCase__			:  Tuple  =False
													lowerCamelCase__			:  Optional[Any]  =True

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





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  List[str]  =self.model_tester.prepare_config_and_inputs_for_common()

										lowerCamelCase__			:  int  =_config_zero_init(lowerCamelCase_ )
										for model_class in self.all_model_classes:
													lowerCamelCase__			:  int  =model_class(config=lowerCamelCase_ )
													# Skip the check for the backbone
													lowerCamelCase__			:  Any  =[]
													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    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										pass





							@slow
							def    UpperCAmelCase__							(						self						:Union[str, Any] ):





										"""simple docstring"""

										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase__			:  List[str]  =DPTModel.from_pretrained(lowerCamelCase_ )
													self.assertIsNotNone(lowerCamelCase_ )






def      lowerCAmelCase_     (	)     ->Dict:
			lowerCamelCase__			:  Optional[Any]  =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						:str ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =DPTImageProcessor.from_pretrained('Intel/dpt-large' )
										lowerCamelCase__			:  Optional[Any]  =DPTForDepthEstimation.from_pretrained('Intel/dpt-large' ).to(lowerCamelCase_ )

										lowerCamelCase__			:  Optional[Any]  =prepare_img()
										lowerCamelCase__			:  Optional[Any]  =image_processor(images=lowerCamelCase_  ,							return_tensors='pt' ).to(lowerCamelCase_ )

										# forward pass
										with torch.no_grad():
													lowerCamelCase__			:  Tuple  =model(**lowerCamelCase_ )
													lowerCamelCase__			:  Any  =outputs.predicted_depth

										# verify the predicted depth
										lowerCamelCase__			:  List[str]  =torch.Size((1, 384, 384) )
										self.assertEqual(predicted_depth.shape  ,							lowerCamelCase_ )

										lowerCamelCase__			:  Optional[Any]  =torch.tensor(
										    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]] ).to(lowerCamelCase_ )

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





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =DPTImageProcessor.from_pretrained('Intel/dpt-large-ade' )
										lowerCamelCase__			:  Union[str, Any]  =DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade' ).to(lowerCamelCase_ )

										lowerCamelCase__			:  Any  =prepare_img()
										lowerCamelCase__			:  Tuple  =image_processor(images=lowerCamelCase_  ,							return_tensors='pt' ).to(lowerCamelCase_ )

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

										# verify the logits
										lowerCamelCase__			:  Union[str, Any]  =torch.Size((1, 150, 480, 480) )
										self.assertEqual(outputs.logits.shape  ,							lowerCamelCase_ )

										lowerCamelCase__			:  Optional[Any]  =torch.tensor(
										    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]] ).to(lowerCamelCase_ )

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





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  =DPTImageProcessor.from_pretrained('Intel/dpt-large-ade' )
										lowerCamelCase__			:  Optional[int]  =DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade' ).to(lowerCamelCase_ )

										lowerCamelCase__			:  Dict  =prepare_img()
										lowerCamelCase__			:  Union[str, Any]  =image_processor(images=lowerCamelCase_  ,							return_tensors='pt' ).to(lowerCamelCase_ )

										# forward pass
										with torch.no_grad():
													lowerCamelCase__			:  int  =model(**lowerCamelCase_ )

										lowerCamelCase__			:  Optional[int]  =outputs.logits.detach().cpu()

										lowerCamelCase__			:  List[Any]  =image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_  ,							target_sizes=[(500, 300)] )
										lowerCamelCase__			:  Dict  =torch.Size((500, 300) )
										self.assertEqual(segmentation[0].shape  ,							lowerCamelCase_ )

										lowerCamelCase__			:  str  =image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_ )
										lowerCamelCase__			:  str  =torch.Size((480, 480) )
										self.assertEqual(segmentation[0].shape  ,							lowerCamelCase_ )

"""simple docstring"""


def      lowerCAmelCase_     (	snake_case_	:    str    )     ->int:
			lowerCamelCase__			:  Optional[Any]  =len(snake_case_    )
			lowerCamelCase__			:  Any  =[0] * length
			lowerCamelCase__			:  str  =[[False for i in range(snake_case_    )] for j in range(snake_case_    )]
			for i, c in enumerate(snake_case_    ):
						lowerCamelCase__			:  Optional[Any]  =i
						for j in range(i + 1    ):
									if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
												lowerCamelCase__			:  int  =True
												lowerCamelCase__			:  Tuple  =min(snake_case_							,					0 if j == 0 else (cut[j - 1] + 1)    )
						lowerCamelCase__			:  Any  =mincut
			return cut[length - 1]


if __name__ == "__main__":
							lowerCAmelCase			=		input("""Enter the string: """).strip()
							lowerCAmelCase			=		find_minimum_partitions(s)
							print(f"""Minimum number of partitions required for the '{s}' is {ans}""")