
"""simple docstring"""


import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SegformerImageProcessor

class 					A_			(  unittest.TestCase     ):



							"""simple docstring"""






							def __init__(						self						:List[Any]  ,							lowerCamelCase_						:List[Any]  ,							lowerCamelCase_						:Union[str, Any]=7  ,							lowerCamelCase_						:Optional[int]=3  ,							lowerCamelCase_						:Union[str, Any]=30  ,							lowerCamelCase_						:str=400  ,							lowerCamelCase_						:int=True  ,							lowerCamelCase_						:Optional[Any]=None  ,							lowerCamelCase_						:Any=True  ,							lowerCamelCase_						:int=[0.5, 0.5, 0.5]  ,							lowerCamelCase_						:Any=[0.5, 0.5, 0.5]  ,							lowerCamelCase_						:Optional[int]=False  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  =size if size is not None else {'height': 30, 'width': 30}
										lowerCamelCase__			:  int  =parent
										lowerCamelCase__			:  List[str]  =batch_size
										lowerCamelCase__			:  str  =num_channels
										lowerCamelCase__			:  Optional[Any]  =min_resolution
										lowerCamelCase__			:  List[str]  =max_resolution
										lowerCamelCase__			:  str  =do_resize
										lowerCamelCase__			:  Optional[Any]  =size
										lowerCamelCase__			:  Dict  =do_normalize
										lowerCamelCase__			:  List[str]  =image_mean
										lowerCamelCase__			:  int  =image_std
										lowerCamelCase__			:  str  =do_reduce_labels





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_reduce_labels": self.do_reduce_labels,
										}






def      lowerCAmelCase_     (	)     ->List[str]:

			'''simple docstring'''





			lowerCamelCase__			:  Union[str, Any]  =load_dataset('hf-internal-testing/fixtures_ade20k'							,					split='test'    )

			lowerCamelCase__			:  Optional[Any]  =Image.open(dataset[0]['file']    )
			lowerCamelCase__			:  int  =Image.open(dataset[1]['file']    )

			return image, map






def      lowerCAmelCase_     (	)     ->int:

			'''simple docstring'''





			lowerCamelCase__			:  Dict  =load_dataset('hf-internal-testing/fixtures_ade20k'							,					split='test'    )

			lowerCamelCase__			:  Union[str, Any]  =Image.open(dataset[0]['file']    )
			lowerCamelCase__			:  Dict  =Image.open(dataset[1]['file']    )
			lowerCamelCase__			:  Dict  =Image.open(dataset[2]['file']    )
			lowerCamelCase__			:  Optional[int]  =Image.open(dataset[3]['file']    )

			return [imagea, imagea], [mapa, mapa]

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



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     SegformerImageProcessor if is_vision_available() else None





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =SegformerImageProcessingTester(self )





							@property
							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(lowerCamelCase_  ,							'do_resize' ) )
										self.assertTrue(hasattr(lowerCamelCase_  ,							'size' ) )
										self.assertTrue(hasattr(lowerCamelCase_  ,							'do_normalize' ) )
										self.assertTrue(hasattr(lowerCamelCase_  ,							'image_mean' ) )
										self.assertTrue(hasattr(lowerCamelCase_  ,							'image_std' ) )
										self.assertTrue(hasattr(lowerCamelCase_  ,							'do_reduce_labels' ) )





							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  =self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size  ,							{'height': 30, 'width': 30} )
										self.assertEqual(image_processor.do_reduce_labels  ,							lowerCamelCase_ )

										lowerCamelCase__			:  Optional[Any]  =self.image_processing_class.from_dict(self.image_processor_dict  ,							size=42  ,							reduce_labels=lowerCamelCase_ )
										self.assertEqual(image_processor.size  ,							{'height': 42, 'width': 42} )
										self.assertEqual(image_processor.do_reduce_labels  ,							lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

										pass





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





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, Any]  =self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										lowerCamelCase__			:  Any  =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=lowerCamelCase_ )
										for image in image_inputs:
													self.assertIsInstance(lowerCamelCase_  ,							Image.Image )

										# Test not batched input
										lowerCamelCase__			:  Tuple  =image_processing(image_inputs[0]  ,							return_tensors='pt' ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,							(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)

										# Test batched
										lowerCamelCase__			:  Optional[Any]  =image_processing(lowerCamelCase_  ,							return_tensors='pt' ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Any  =self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										lowerCamelCase__			:  Tuple  =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=lowerCamelCase_  ,							numpify=lowerCamelCase_ )
										for image in image_inputs:
													self.assertIsInstance(lowerCamelCase_  ,							np.ndarray )

										# Test not batched input
										lowerCamelCase__			:  Optional[int]  =image_processing(image_inputs[0]  ,							return_tensors='pt' ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,							(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)

										# Test batched
										lowerCamelCase__			:  str  =image_processing(lowerCamelCase_  ,							return_tensors='pt' ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										lowerCamelCase__			:  str  =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=lowerCamelCase_  ,							torchify=lowerCamelCase_ )
										for image in image_inputs:
													self.assertIsInstance(lowerCamelCase_  ,							torch.Tensor )

										# Test not batched input
										lowerCamelCase__			:  Union[str, Any]  =image_processing(image_inputs[0]  ,							return_tensors='pt' ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,							(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)

										# Test batched
										lowerCamelCase__			:  List[Any]  =image_processing(lowerCamelCase_  ,							return_tensors='pt' ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										lowerCamelCase__			:  Tuple  =prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=lowerCamelCase_  ,							torchify=lowerCamelCase_ )
										lowerCamelCase__			:  Tuple  =[]
										for image in image_inputs:
													self.assertIsInstance(lowerCamelCase_  ,							torch.Tensor )
													maps.append(torch.zeros(image.shape[-2:] ).long() )

										# Test not batched input
										lowerCamelCase__			:  List[str]  =image_processing(image_inputs[0]  ,							maps[0]  ,							return_tensors='pt' )
										self.assertEqual(
										    encoding['pixel_values'].shape  ,							(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(
										    encoding['labels'].shape  ,							(
										        1,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(encoding['labels'].dtype  ,							torch.long )
										self.assertTrue(encoding['labels'].min().item() >= 0 )
										self.assertTrue(encoding['labels'].max().item() <= 255 )

										# Test batched
										lowerCamelCase__			:  Any  =image_processing(lowerCamelCase_  ,							lowerCamelCase_  ,							return_tensors='pt' )
										self.assertEqual(
										    encoding['pixel_values'].shape  ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(
										    encoding['labels'].shape  ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(encoding['labels'].dtype  ,							torch.long )
										self.assertTrue(encoding['labels'].min().item() >= 0 )
										self.assertTrue(encoding['labels'].max().item() <= 255 )

										# Test not batched input (PIL images)
										lowerCamelCase__			:  List[str]  =prepare_semantic_single_inputs()

										lowerCamelCase__			:  Tuple  =image_processing(lowerCamelCase_  ,							lowerCamelCase_  ,							return_tensors='pt' )
										self.assertEqual(
										    encoding['pixel_values'].shape  ,							(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(
										    encoding['labels'].shape  ,							(
										        1,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(encoding['labels'].dtype  ,							torch.long )
										self.assertTrue(encoding['labels'].min().item() >= 0 )
										self.assertTrue(encoding['labels'].max().item() <= 255 )

										# Test batched input (PIL images)
										lowerCamelCase__			:  str  =prepare_semantic_batch_inputs()

										lowerCamelCase__			:  Tuple  =image_processing(lowerCamelCase_  ,							lowerCamelCase_  ,							return_tensors='pt' )
										self.assertEqual(
										    encoding['pixel_values'].shape  ,							(
										        2,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(
										    encoding['labels'].shape  ,							(
										        2,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )  ,							)
										self.assertEqual(encoding['labels'].dtype  ,							torch.long )
										self.assertTrue(encoding['labels'].min().item() >= 0 )
										self.assertTrue(encoding['labels'].max().item() <= 255 )





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =self.image_processing_class(**self.image_processor_dict )

										# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
										lowerCamelCase__			:  Dict  =prepare_semantic_single_inputs()
										lowerCamelCase__			:  Optional[Any]  =image_processing(lowerCamelCase_  ,							lowerCamelCase_  ,							return_tensors='pt' )
										self.assertTrue(encoding['labels'].min().item() >= 0 )
										self.assertTrue(encoding['labels'].max().item() <= 150 )

										lowerCamelCase__			:  List[Any]  =True
										lowerCamelCase__			:  Dict  =image_processing(lowerCamelCase_  ,							lowerCamelCase_  ,							return_tensors='pt' )
										self.assertTrue(encoding['labels'].min().item() >= 0 )
										self.assertTrue(encoding['labels'].max().item() <= 255 )

"""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}""")