"""simple docstring"""



from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_UpperCamelCase		:  Tuple						=		logging.get_logger(__name__)  # pylint: disable=invalid-name

class 	a					(       a_     ):
    UpperCAmelCase_			:					List[Any]     					=["safety_checker", "feature_extractor"]







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = True							,		):
      super().__init__()
      if hasattr(scheduler.config							,		'steps_offset'	) and scheduler.config.steps_offset != 1:
        lowercase							   =	(
            F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
            F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
            'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
            ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
            ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
            ' file'
        )
        deprecate('steps_offset!=1'							,		'1.0.0'							,		_lowerCamelCase							,		standard_warn=_lowerCamelCase	)
        lowercase							   =	dict(scheduler.config	)
        lowercase							   =	1
        lowercase							   =	FrozenDict(_lowerCamelCase	)

      self.register_modules(
          vae=_lowerCamelCase							,		text_encoder=_lowerCamelCase							,		tokenizer=_lowerCamelCase							,		unet=_lowerCamelCase							,		scheduler=_lowerCamelCase							,		safety_checker=_lowerCamelCase							,		feature_extractor=_lowerCamelCase							,		)
      self.register_to_config(requires_safety_checker=_lowerCamelCase	)







    @property
    def       UpperCamelCase_				(      self	):
      return {k: getattr(self							,		_lowerCamelCase	) for k in self.config.keys() if not k.startswith('_'	)}







    def       UpperCamelCase_				(      self							,		_lowerCamelCase = "auto"	):
      if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        lowercase							   =	self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      self.enable_attention_slicing(_lowerCamelCase	)







    @torch.no_grad()
    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 0.8							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = 7.5							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		_lowerCamelCase = 0.0							,		_lowerCamelCase = None							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		):
      # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
      return StableDiffusionInpaintPipelineLegacy(**self.components	)(
          prompt=_lowerCamelCase							,		image=_lowerCamelCase							,		mask_image=_lowerCamelCase							,		strength=_lowerCamelCase							,		num_inference_steps=_lowerCamelCase							,		guidance_scale=_lowerCamelCase							,		negative_prompt=_lowerCamelCase							,		num_images_per_prompt=_lowerCamelCase							,		eta=_lowerCamelCase							,		generator=_lowerCamelCase							,		output_type=_lowerCamelCase							,		return_dict=_lowerCamelCase							,		callback=_lowerCamelCase							,		)







    @torch.no_grad()
    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase = 0.8							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = 7.5							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		_lowerCamelCase = 0.0							,		_lowerCamelCase = None							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		**_lowerCamelCase							,		):
      # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
      return StableDiffusionImgaImgPipeline(**self.components	)(
          prompt=_lowerCamelCase							,		image=_lowerCamelCase							,		strength=_lowerCamelCase							,		num_inference_steps=_lowerCamelCase							,		guidance_scale=_lowerCamelCase							,		negative_prompt=_lowerCamelCase							,		num_images_per_prompt=_lowerCamelCase							,		eta=_lowerCamelCase							,		generator=_lowerCamelCase							,		output_type=_lowerCamelCase							,		return_dict=_lowerCamelCase							,		callback=_lowerCamelCase							,		callback_steps=_lowerCamelCase							,		)







    @torch.no_grad()
    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = 5_1_2							,		_lowerCamelCase = 5_1_2							,		_lowerCamelCase = 5_0							,		_lowerCamelCase = 7.5							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		_lowerCamelCase = 0.0							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		_lowerCamelCase = None							,		_lowerCamelCase = 1							,		):
      # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
      return StableDiffusionPipeline(**self.components	)(
          prompt=_lowerCamelCase							,		height=_lowerCamelCase							,		width=_lowerCamelCase							,		num_inference_steps=_lowerCamelCase							,		guidance_scale=_lowerCamelCase							,		negative_prompt=_lowerCamelCase							,		num_images_per_prompt=_lowerCamelCase							,		eta=_lowerCamelCase							,		generator=_lowerCamelCase							,		latents=_lowerCamelCase							,		output_type=_lowerCamelCase							,		return_dict=_lowerCamelCase							,		callback=_lowerCamelCase							,		callback_steps=_lowerCamelCase							,		)










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  str   ):





  '''simple docstring'''





  lowercase							   =	len(__snake_case   )
  lowercase							   =	[0] * length
  lowercase							   =	[[False for i in range(__snake_case   )] for j in range(__snake_case   )]
  for i, c in enumerate(__snake_case   ):
    lowercase							   =	i
    for j in range(i + 1   ):
      if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
        lowercase							   =	True
        lowercase							   =	min(__snake_case		, 0 if j == 0 else (cut[j - 1] + 1)   )
    lowercase							   =	mincut
  return cut[length - 1]


if __name__ == "__main__":
    _UpperCamelCase		:  List[Any]						=		input('Enter the string: ').strip()
    _UpperCamelCase		:  List[Any]						=		find_minimum_partitions(s)
    print(F'''Minimum number of partitions required for the \'{s}\' is {ans}''')


