from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_SCREAMING_SNAKE_CASE:    Tuple     						=							'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_=8	):



			"""simple docstring"""





			_snake_case														=   height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			_snake_case														=   width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_=512				,						lowerCamelCase_=512	):



			"""simple docstring"""





			_snake_case														=   pil_image.resize((w, h)				,						resample=Image.BICUBIC				,						reducing_gap=1	)
			_snake_case														=   np.array(pil_image.convert("RGB"	)	)
			_snake_case														=   arr.astype(np.floataa	) / 1_2_7.5 - 1
			_snake_case														=   np.transpose(lowerCamelCase_				,						[2, 0, 1]	)
			_snake_case														=   torch.from_numpy(lowerCamelCase_	).unsqueeze(0	)
			return image







class 						_SCREAMING_SNAKE_CASE				(  _lowercase				):






		"""simple docstring"""







		def __init__(      self     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					UNetaDConditionModel	,       __SCREAMING_SNAKE_CASE     :					DDPMScheduler	,       __SCREAMING_SNAKE_CASE     :					VQModel	,       ):






					"""simple docstring"""
					super().__init__()

					self.register_modules(
					    unet=__SCREAMING_SNAKE_CASE	,       scheduler=__SCREAMING_SNAKE_CASE	,       movq=__SCREAMING_SNAKE_CASE	,       )
					_snake_case														=   2 ** (len(self.movq.config.block_out_channels ) - 1)



		def 					_a					(      self     :					Tuple	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Optional[Any] ):






					"""simple docstring"""
					# get the original timestep using init_timestep
					_snake_case														=   min(int(num_inference_steps * strength )	,       __SCREAMING_SNAKE_CASE )

					_snake_case														=   max(num_inference_steps - init_timestep	,       0 )
					_snake_case														=   self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start



		def 					_a					(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					str	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					Dict	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Optional[Any]=None ):






					"""simple docstring"""
					if not isinstance(__SCREAMING_SNAKE_CASE	,       (torch.Tensor, PIL.Image.Image, list) ):
								raise ValueError(
								    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE )}' )

					_snake_case														=   image.to(device=__SCREAMING_SNAKE_CASE	,       dtype=__SCREAMING_SNAKE_CASE )

					_snake_case														=   batch_size * num_images_per_prompt

					if image.shape[1] == 4:
								_snake_case														=   image

					else:
								if isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) != batch_size:
											raise ValueError(
											    F'You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE )}, but requested an effective batch'
											    F' size of {batch_size}. Make sure the batch size matches the length of the generators.' )

								elif isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ):
											_snake_case														=   [
											    self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__SCREAMING_SNAKE_CASE )
											]
											_snake_case														=   torch.cat(__SCREAMING_SNAKE_CASE	,       dim=0 )
								else:
											_snake_case														=   self.movq.encode(__SCREAMING_SNAKE_CASE ).latent_dist.sample(__SCREAMING_SNAKE_CASE )

								_snake_case														=   self.movq.config.scaling_factor * init_latents

					_snake_case														=   torch.cat([init_latents]	,       dim=0 )

					_snake_case														=   init_latents.shape
					_snake_case														=   randn_tensor(__SCREAMING_SNAKE_CASE	,       generator=__SCREAMING_SNAKE_CASE	,       device=__SCREAMING_SNAKE_CASE	,       dtype=__SCREAMING_SNAKE_CASE )

					# get latents
					_snake_case														=   self.scheduler.add_noise(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )

					_snake_case														=   init_latents

					return latents



		def 					_a					(      self     :					int	,       __SCREAMING_SNAKE_CASE     :					str=0 ):






					"""simple docstring"""
					if is_accelerate_available():
								from accelerate import cpu_offload
					else:
								raise ImportError("Please install accelerate via `pip install accelerate`" )

					_snake_case														=   torch.device(F'cuda:{gpu_id}' )

					_snake_case														=   [
					    self.unet,
					    self.movq,
					]
					for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
											cpu_offload(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )



		def 					_a					(      self     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=0 ):






					"""simple docstring"""
					if is_accelerate_available() and is_accelerate_version(">="	,       "0.17.0.dev0" ):
								from accelerate import cpu_offload_with_hook
					else:
								raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )

					_snake_case														=   torch.device(F'cuda:{gpu_id}' )

					if self.device.type != "cpu":
								self.to("cpu"	,       silence_dtype_warnings=__SCREAMING_SNAKE_CASE )
								torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					_snake_case														=   None
					for cpu_offloaded_model in [self.unet, self.movq]:
								_snake_case							,			_snake_case														=   cpu_offload_with_hook(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       prev_module_hook=__SCREAMING_SNAKE_CASE )

					# We'll offload the last model manually.
					_snake_case														=   hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					if not hasattr(self.unet	,       "_hf_hook" ):
								return self.device
					for module in self.unet.modules():
								if (
								    hasattr(__SCREAMING_SNAKE_CASE	,       "_hf_hook" )
								    and hasattr(module._hf_hook	,       "execution_device" )
								    and module._hf_hook.execution_device is not None
								):
											return torch.device(module._hf_hook.execution_device )
					return self.device







		@torch.no_grad()
		@replace_example_docstring(__SCREAMING_SNAKE_CASE )
		def __call__(      self     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					Union[torch.FloatTensor, List[torch.FloatTensor]]	,       __SCREAMING_SNAKE_CASE     :					Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]	,       __SCREAMING_SNAKE_CASE     :					Union[torch.FloatTensor, List[torch.FloatTensor]]	,       __SCREAMING_SNAKE_CASE     :					torch.FloatTensor	,       __SCREAMING_SNAKE_CASE     :					int = 512	,       __SCREAMING_SNAKE_CASE     :					int = 512	,       __SCREAMING_SNAKE_CASE     :					int = 100	,       __SCREAMING_SNAKE_CASE     :					float = 4.0	,       __SCREAMING_SNAKE_CASE     :					float = 0.3	,       __SCREAMING_SNAKE_CASE     :					int = 1	,       __SCREAMING_SNAKE_CASE     :					Optional[Union[torch.Generator, List[torch.Generator]]] = None	,       __SCREAMING_SNAKE_CASE     :					Optional[str] = "pil"	,       __SCREAMING_SNAKE_CASE     :					bool = True	,       ):






					"""simple docstring"""
					_snake_case														=   self._execution_device

					_snake_case														=   guidance_scale > 1.0

					if isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ):
								_snake_case														=   torch.cat(__SCREAMING_SNAKE_CASE	,       dim=0 )
					if isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ):
								_snake_case														=   torch.cat(__SCREAMING_SNAKE_CASE	,       dim=0 )
					if isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ):
								_snake_case														=   torch.cat(__SCREAMING_SNAKE_CASE	,       dim=0 )

					_snake_case														=   image_embeds.shape[0]

					if do_classifier_free_guidance:
								_snake_case														=   image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE	,       dim=0 )
								_snake_case														=   negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE	,       dim=0 )
								_snake_case														=   hint.repeat_interleave(__SCREAMING_SNAKE_CASE	,       dim=0 )

					_snake_case														=   torch.cat([negative_image_embeds, image_embeds]	,       dim=0 ).to(dtype=self.unet.dtype	,       device=__SCREAMING_SNAKE_CASE )
					_snake_case														=   torch.cat([hint, hint]	,       dim=0 ).to(dtype=self.unet.dtype	,       device=__SCREAMING_SNAKE_CASE )

					if not isinstance(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ):
								_snake_case														=   [image]
					if not all(isinstance(__SCREAMING_SNAKE_CASE	,       (PIL.Image.Image, torch.Tensor) ) for i in image ):
								raise ValueError(
								    F'Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE ) for i in image]}. Currently, we only support  PIL image and pytorch tensor' )

					_snake_case														=   torch.cat([prepare_image(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) for i in image]	,       dim=0 )
					_snake_case														=   image.to(dtype=image_embeds.dtype	,       device=__SCREAMING_SNAKE_CASE )

					_snake_case														=   self.movq.encode(__SCREAMING_SNAKE_CASE )["latents"]
					_snake_case														=   latents.repeat_interleave(__SCREAMING_SNAKE_CASE	,       dim=0 )
					self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	,       device=__SCREAMING_SNAKE_CASE )
					_snake_case							,			_snake_case														=   self.get_timesteps(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					_snake_case														=   timesteps[:1].repeat(batch_size * num_images_per_prompt )
					_snake_case							,			_snake_case														=   downscale_height_and_width(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       self.movq_scale_factor )
					_snake_case														=   self.prepare_latents(
					    __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       image_embeds.dtype	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE ) ):
								# expand the latents if we are doing classifier free guidance
								_snake_case														=   torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

								_snake_case														=   {"image_embeds": image_embeds, "hint": hint}
								_snake_case														=   self.unet(
								    sample=__SCREAMING_SNAKE_CASE	,       timestep=__SCREAMING_SNAKE_CASE	,       encoder_hidden_states=__SCREAMING_SNAKE_CASE	,       added_cond_kwargs=__SCREAMING_SNAKE_CASE	,       return_dict=__SCREAMING_SNAKE_CASE	,       )[0]

								if do_classifier_free_guidance:
											_snake_case							,			_snake_case														=   noise_pred.split(latents.shape[1]	,       dim=1 )
											_snake_case							,			_snake_case														=   noise_pred.chunk(2 )
											_snake_case							,			_snake_case														=   variance_pred.chunk(2 )
											_snake_case														=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
											_snake_case														=   torch.cat([noise_pred, variance_pred_text]	,       dim=1 )

								if not (
								    hasattr(self.scheduler.config	,       "variance_type" )
								    and self.scheduler.config.variance_type in ["learned", "learned_range"]
								):
											_snake_case							,			_snake_case														=   noise_pred.split(latents.shape[1]	,       dim=1 )

								# compute the previous noisy sample x_t -> x_t-1

								_snake_case														=   self.scheduler.step(
								    __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       generator=__SCREAMING_SNAKE_CASE	,       )[0]

					# post-processing
					_snake_case														=   self.movq.decode(__SCREAMING_SNAKE_CASE	,       force_not_quantize=__SCREAMING_SNAKE_CASE )["sample"]

					if output_type not in ["pt", "np", "pil"]:
								raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )

					if output_type in ["np", "pil"]:
								_snake_case														=   image * 0.5 + 0.5
								_snake_case														=   image.clamp(0	,       1 )
								_snake_case														=   image.cpu().permute(0	,       2	,       3	,       1 ).float().numpy()

					if output_type == "pil":
								_snake_case														=   self.numpy_to_pil(__SCREAMING_SNAKE_CASE )

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE )


def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





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


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

