
"""simple docstring"""


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 					A_			(  unittest.TestCase     ):



							"""simple docstring"""






							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





							@property
							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =1
										lowerCamelCase__			:  Tuple  =3
										lowerCamelCase__			:  List[str]  =(32, 32)

										lowerCamelCase__			:  int  =floats_tensor((batch_size, num_channels) + sizes  ,							rng=random.Random(0 ) ).to(lowercase_ )
										return image





							@property
							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										torch.manual_seed(0 )
										lowerCamelCase__			:  Any  =UNetaDConditionModel(
										    block_out_channels=(32, 64)  ,							layers_per_block=2  ,							sample_size=32  ,							in_channels=4  ,							out_channels=4  ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,							cross_attention_dim=32  ,							)
										return model





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





										"""simple docstring"""

										torch.manual_seed(0 )
										lowerCamelCase__			:  Dict  =AutoencoderKL(
										    block_out_channels=[32, 64]  ,							in_channels=3  ,							out_channels=3  ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,							latent_channels=4  ,							)
										return model





							@property
							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										torch.manual_seed(0 )
										lowerCamelCase__			:  Optional[int]  =CLIPTextConfig(
										    bos_token_id=0  ,							eos_token_id=2  ,							hidden_size=32  ,							intermediate_size=37  ,							layer_norm_eps=1e-05  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1_000  ,							)
										return CLIPTextModel(lowercase_ )





							@property
							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										def extract(*lowerCamelCase_						:int  ,							**lowerCamelCase_						:Any ):

													class 					A_			:



																				"""simple docstring"""






																				def __init__(						self						:str ):





																							"""simple docstring"""

																							lowerCamelCase__			:  Union[str, Any]  =torch.ones([0] )





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





																							"""simple docstring"""

																							self.pixel_values.to(lowercase_ )
																							return self






													return Out()

										return extract





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  ='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__			:  Optional[Any]  =self.dummy_cond_unet
										lowerCamelCase__			:  Union[str, Any]  =DDIMScheduler(
										    beta_start=0.0_00_85  ,							beta_end=0.0_12  ,							beta_schedule='scaled_linear'  ,							clip_sample=lowercase_  ,							set_alpha_to_one=lowercase_  ,							)

										lowerCamelCase__			:  Dict  =self.dummy_vae
										lowerCamelCase__			:  List[str]  =self.dummy_text_encoder
										lowerCamelCase__			:  Union[str, Any]  =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

										# make sure here that pndm scheduler skips prk
										lowerCamelCase__			:  List[Any]  =StableDiffusionPipeline(
										    unet=lowercase_  ,							scheduler=lowercase_  ,							vae=lowercase_  ,							text_encoder=lowercase_  ,							tokenizer=lowercase_  ,							safety_checker=lowercase_  ,							feature_extractor=self.dummy_extractor  ,							)
										lowerCamelCase__			:  List[str]  =sd_pipe.to(lowercase_ )
										sd_pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__			:  Tuple  ='A painting of a squirrel eating a burger'

										lowerCamelCase__			:  Tuple  =torch.Generator(device=lowercase_ ).manual_seed(0 )
										lowerCamelCase__			:  Optional[Any]  =sd_pipe([prompt]  ,							generator=lowercase_  ,							guidance_scale=6.0  ,							num_inference_steps=2  ,							output_type='np' )
										lowerCamelCase__			:  List[Any]  =output.images

										lowerCamelCase__			:  str  =torch.Generator(device=lowercase_ ).manual_seed(0 )
										lowerCamelCase__			:  Any  =sd_pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=6.0  ,							num_inference_steps=2  ,							output_type='np'  ,							return_dict=lowercase_  ,							)[0]

										lowerCamelCase__			:  Union[str, Any]  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  List[Any]  =image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowerCamelCase__			:  List[str]  =np.array([0.57_53, 0.61_14, 0.50_01, 0.50_34, 0.54_70, 0.47_29, 0.49_71, 0.48_67, 0.48_67] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2





							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  ='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__			:  Tuple  =self.dummy_cond_unet
										lowerCamelCase__			:  str  =PNDMScheduler(skip_prk_steps=lowercase_ )
										lowerCamelCase__			:  Optional[int]  =self.dummy_vae
										lowerCamelCase__			:  int  =self.dummy_text_encoder
										lowerCamelCase__			:  int  =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

										# make sure here that pndm scheduler skips prk
										lowerCamelCase__			:  Any  =StableDiffusionPipeline(
										    unet=lowercase_  ,							scheduler=lowercase_  ,							vae=lowercase_  ,							text_encoder=lowercase_  ,							tokenizer=lowercase_  ,							safety_checker=lowercase_  ,							feature_extractor=self.dummy_extractor  ,							)
										lowerCamelCase__			:  List[str]  =sd_pipe.to(lowercase_ )
										sd_pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__			:  Tuple  ='A painting of a squirrel eating a burger'
										lowerCamelCase__			:  Optional[Any]  =torch.Generator(device=lowercase_ ).manual_seed(0 )
										lowerCamelCase__			:  Tuple  =sd_pipe([prompt]  ,							generator=lowercase_  ,							guidance_scale=6.0  ,							num_inference_steps=2  ,							output_type='np' )

										lowerCamelCase__			:  Union[str, Any]  =output.images

										lowerCamelCase__			:  int  =torch.Generator(device=lowercase_ ).manual_seed(0 )
										lowerCamelCase__			:  List[str]  =sd_pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=6.0  ,							num_inference_steps=2  ,							output_type='np'  ,							return_dict=lowercase_  ,							)[0]

										lowerCamelCase__			:  List[str]  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  str  =image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowerCamelCase__			:  List[Any]  =np.array([0.51_22, 0.57_12, 0.48_25, 0.50_53, 0.56_46, 0.47_69, 0.51_79, 0.48_94, 0.49_94] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2





							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =StableDiffusionPipeline.from_pretrained(
										    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'  ,							safety_checker=lowercase_ )
										assert isinstance(lowercase_  ,							lowercase_ )
										assert isinstance(pipe.scheduler  ,							lowercase_ )
										assert pipe.safety_checker is None

										lowerCamelCase__			:  Dict  =pipe('example prompt'  ,							num_inference_steps=2 ).images[0]
										assert image is not None

										# check that there's no error when saving a pipeline with one of the models being None
										with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowercase_ )
													lowerCamelCase__			:  Dict  =StableDiffusionPipeline.from_pretrained(lowercase_ )

										# sanity check that the pipeline still works
										assert pipe.safety_checker is None
										lowerCamelCase__			:  Tuple  =pipe('example prompt'  ,							num_inference_steps=2 ).images[0]
										assert image is not None





							@unittest.skipIf(torch_device != 'cuda'  ,							'This test requires a GPU' )
							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  =self.dummy_cond_unet
										lowerCamelCase__			:  Optional[Any]  =PNDMScheduler(skip_prk_steps=lowercase_ )
										lowerCamelCase__			:  List[str]  =self.dummy_vae
										lowerCamelCase__			:  Any  =self.dummy_text_encoder
										lowerCamelCase__			:  List[str]  =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

										# put models in fp16
										lowerCamelCase__			:  Optional[Any]  =unet.half()
										lowerCamelCase__			:  str  =vae.half()
										lowerCamelCase__			:  int  =bert.half()

										# make sure here that pndm scheduler skips prk
										lowerCamelCase__			:  Any  =StableDiffusionPipeline(
										    unet=lowercase_  ,							scheduler=lowercase_  ,							vae=lowercase_  ,							text_encoder=lowercase_  ,							tokenizer=lowercase_  ,							safety_checker=lowercase_  ,							feature_extractor=self.dummy_extractor  ,							)
										lowerCamelCase__			:  Optional[int]  =sd_pipe.to(lowercase_ )
										sd_pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__			:  int  ='A painting of a squirrel eating a burger'
										lowerCamelCase__			:  Dict  =sd_pipe([prompt]  ,							num_inference_steps=2  ,							output_type='np' ).images

										assert image.shape == (1, 64, 64, 3)






@nightly
@require_torch_gpu
class 					A_			(  unittest.TestCase     ):



							"""simple docstring"""






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





										"""simple docstring"""

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





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





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  ='cuda'
										lowerCamelCase__			:  Optional[int]  =StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' )
										lowerCamelCase__			:  Any  =pipe.to(lowercase_ )
										pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__			:  str  ='a photo of a cat'
										lowerCamelCase__			:  Union[str, Any]  ={
										    'editing_prompt': ['sunglasses'],
										    'reverse_editing_direction': [False],
										    'edit_warmup_steps': 10,
										    'edit_guidance_scale': 6,
										    'edit_threshold': 0.95,
										    'edit_momentum_scale': 0.5,
										    'edit_mom_beta': 0.6,
										}

										lowerCamelCase__			:  Tuple  =3
										lowerCamelCase__			:  Dict  =7

										# no sega enabled
										lowerCamelCase__			:  Any  =torch.Generator(lowercase_ )
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  int  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							)

										lowerCamelCase__			:  int  =output.images
										lowerCamelCase__			:  str  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  List[str]  =[
										    0.34_67_31_13,
										    0.38_49_27_33,
										    0.37_59_73_52,
										    0.34_08_63_35,
										    0.35_65_07_48,
										    0.35_57_92_05,
										    0.3_38_47_63,
										    0.34_34_02_36,
										    0.3_57_32_71,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  Dict  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							**lowercase_  ,							)

										lowerCamelCase__			:  int  =output.images
										lowerCamelCase__			:  Optional[int]  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  Optional[int]  =[
										    0.41_88_78_26,
										    0.37_72_87_66,
										    0.30_13_82_72,
										    0.41_41_63_35,
										    0.41_66_49_85,
										    0.36_28_33_92,
										    0.36_19_12_46,
										    0.43_36_44_65,
										    0.43_00_17_32,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  str  ='cuda'
										lowerCamelCase__			:  List[Any]  =StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' )
										lowerCamelCase__			:  List[str]  =pipe.to(lowercase_ )
										pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__			:  Dict  ='an image of a crowded boulevard, realistic, 4k'
										lowerCamelCase__			:  Optional[Any]  ={
										    'editing_prompt': 'crowd, crowded, people',
										    'reverse_editing_direction': True,
										    'edit_warmup_steps': 10,
										    'edit_guidance_scale': 8.3,
										    'edit_threshold': 0.9,
										    'edit_momentum_scale': 0.5,
										    'edit_mom_beta': 0.6,
										}

										lowerCamelCase__			:  str  =9
										lowerCamelCase__			:  Union[str, Any]  =7

										# no sega enabled
										lowerCamelCase__			:  List[Any]  =torch.Generator(lowercase_ )
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  List[Any]  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							)

										lowerCamelCase__			:  Optional[int]  =output.images
										lowerCamelCase__			:  Optional[Any]  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  str  =[
										    0.43_49_79_98,
										    0.91_81_40_65,
										    0.7_54_07_39,
										    0.55_58_02_05,
										    0.8_46_72_65,
										    0.5_38_96_91,
										    0.62_57_45_06,
										    0.58_89_77_63,
										    0.50_92_67_57,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  Tuple  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							**lowercase_  ,							)

										lowerCamelCase__			:  Any  =output.images
										lowerCamelCase__			:  int  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  Union[str, Any]  =[
										    0.3_08_97_19,
										    0.30_50_01_44,
										    0.29_01_60_42,
										    0.30_63_09_64,
										    0.32_56_87,
										    0.29_41_92_25,
										    0.2_90_80_91,
										    0.28_72_35_98,
										    0.27_69_62_94,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2





							def    UpperCAmelCase__							(						self						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__			:  int  ='cuda'
										lowerCamelCase__			:  str  =StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5' )
										lowerCamelCase__			:  List[str]  =pipe.to(lowercase_ )
										pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__			:  int  ='a castle next to a river'
										lowerCamelCase__			:  List[str]  ={
										    'editing_prompt': ['boat on a river, boat', 'monet, impression, sunrise'],
										    'reverse_editing_direction': False,
										    'edit_warmup_steps': [15, 18],
										    'edit_guidance_scale': 6,
										    'edit_threshold': [0.9, 0.8],
										    'edit_momentum_scale': 0.5,
										    'edit_mom_beta': 0.6,
										}

										lowerCamelCase__			:  Dict  =48
										lowerCamelCase__			:  Any  =7

										# no sega enabled
										lowerCamelCase__			:  Dict  =torch.Generator(lowercase_ )
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  int  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							)

										lowerCamelCase__			:  List[str]  =output.images
										lowerCamelCase__			:  Optional[Any]  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  int  =[
										    0.75_16_35_55,
										    0.76_03_71_45,
										    0.6_17_85,
										    0.9_18_96_73,
										    0.8_62_77_01,
										    0.85_18_96_94,
										    0.8_51_28_13,
										    0.87_01_20_76,
										    0.8_31_28_57,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  Union[str, Any]  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							**lowercase_  ,							)

										lowerCamelCase__			:  List[Any]  =output.images
										lowerCamelCase__			:  int  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  List[str]  =[
										    0.73_55_33_65,
										    0.7_53_72_71,
										    0.74_34_19_05,
										    0.66_48_03_56,
										    0.6_47_29_25,
										    0.63_03_94_16,
										    0.64_81_29_05,
										    0.6_74_97_17,
										    0.6_51_71_02,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  ='cuda'
										lowerCamelCase__			:  Tuple  =StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,							torch_dtype=torch.floataa )
										lowerCamelCase__			:  List[Any]  =pipe.to(lowercase_ )
										pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__			:  Tuple  ='a photo of a cat'
										lowerCamelCase__			:  int  ={
										    'editing_prompt': ['sunglasses'],
										    'reverse_editing_direction': [False],
										    'edit_warmup_steps': 10,
										    'edit_guidance_scale': 6,
										    'edit_threshold': 0.95,
										    'edit_momentum_scale': 0.5,
										    'edit_mom_beta': 0.6,
										}

										lowerCamelCase__			:  Optional[int]  =3
										lowerCamelCase__			:  Union[str, Any]  =7

										# no sega enabled
										lowerCamelCase__			:  Optional[Any]  =torch.Generator(lowercase_ )
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  List[str]  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							)

										lowerCamelCase__			:  Optional[Any]  =output.images
										lowerCamelCase__			:  Union[str, Any]  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  Any  =[
										    0.34_88_76_95,
										    0.3_87_69_53,
										    0.3_75,
										    0.34_42_38_28,
										    0.3_58_15_43,
										    0.35_71_77_73,
										    0.3_38_37_89,
										    0.34_57_03_12,
										    0.35_93_75,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(lowercase_ )
										lowerCamelCase__			:  List[str]  =pipe(
										    [prompt]  ,							generator=lowercase_  ,							guidance_scale=lowercase_  ,							num_inference_steps=50  ,							output_type='np'  ,							width=512  ,							height=512  ,							**lowercase_  ,							)

										lowerCamelCase__			:  str  =output.images
										lowerCamelCase__			:  Optional[int]  =image[0, -3:, -3:, -1]
										lowerCamelCase__			:  int  =[
										    0.42_28_51_56,
										    0.36_91_40_62,
										    0.29_07_71_48,
										    0.42_04_10_16,
										    0.41_91_89_45,
										    0.35_49_80_47,
										    0.3_61_81_64,
										    0.4_42_38_28,
										    0.43_11_52_34,
										]

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2

"""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}""")