






"""simple docstring"""



import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


_UpperCamelCase		:  Optional[int]						=		logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCamelCase		:  int						=		'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'

@dataclass
class 	a					(       a_     ):
    UpperCAmelCase_			:					Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]


class 	a					(       a_     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      super().__init__()

      self.register_modules(
          prior=_lowerCamelCase							,		text_encoder=_lowerCamelCase							,		tokenizer=_lowerCamelCase							,		scheduler=_lowerCamelCase							,		renderer=_lowerCamelCase							,		)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      if latents is None:
        lowercase							   =	randn_tensor(_lowerCamelCase							,		generator=_lowerCamelCase							,		device=_lowerCamelCase							,		dtype=_lowerCamelCase	)
      else:
        if latents.shape != shape:
          raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'	)
        lowercase							   =	latents.to(_lowerCamelCase	)

      lowercase							   =	latents * scheduler.init_noise_sigma
      return latents







    def       UpperCamelCase_				(      self							,		_lowerCamelCase=0	):
      if is_accelerate_available():
        from accelerate import cpu_offload
      else:
        raise ImportError('Please install accelerate via `pip install accelerate`'	)

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

      lowercase							   =	[self.text_encoder, self.prior]
      for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
          cpu_offload(_lowerCamelCase							,		_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase=0	):
      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.'	)

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

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

      lowercase							   =	None
      for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
        lowercase   ,					lowercase							   =	cpu_offload_with_hook(_lowerCamelCase							,		_lowerCamelCase							,		prev_module_hook=_lowerCamelCase	)

      if self.safety_checker is not None:
        lowercase   ,					lowercase							   =	cpu_offload_with_hook(self.safety_checker							,		_lowerCamelCase							,		prev_module_hook=_lowerCamelCase	)

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







    @property
    def       UpperCamelCase_				(      self	):
      if self.device != torch.device('meta'	) or not hasattr(self.text_encoder							,		'_hf_hook'	):
        return self.device
      for module in self.text_encoder.modules():
        if (
            hasattr(_lowerCamelCase							,		'_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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		):
      len(_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else 1

      # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
      lowercase							   =	0
      # get prompt text embeddings
      lowercase							   =	self.tokenizer(
          _lowerCamelCase							,		padding='max_length'							,		max_length=self.tokenizer.model_max_length							,		truncation=_lowerCamelCase							,		return_tensors='pt'							,		)
      lowercase							   =	text_inputs.input_ids
      lowercase							   =	self.tokenizer(_lowerCamelCase							,		padding='longest'							,		return_tensors='pt'	).input_ids

      if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]	)
        logger.warning(
            'The following part of your input was truncated because CLIP can only handle sequences up to'
            F' {self.tokenizer.model_max_length} tokens: {removed_text}'	)

      lowercase							   =	self.text_encoder(text_input_ids.to(_lowerCamelCase	)	)
      lowercase							   =	text_encoder_output.text_embeds

      lowercase							   =	prompt_embeds.repeat_interleave(_lowerCamelCase							,		dim=0	)
      # in Shap-E it normalize the prompt_embeds and then later rescale it
      lowercase							   =	prompt_embeds / torch.linalg.norm(_lowerCamelCase							,		dim=-1							,		keepdim=_lowerCamelCase	)

      if do_classifier_free_guidance:
        lowercase							   =	torch.zeros_like(_lowerCamelCase	)

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        lowercase							   =	torch.cat([negative_prompt_embeds, prompt_embeds]	)

      # Rescale the features to have unit variance
      lowercase							   =	math.sqrt(prompt_embeds.shape[1]	) * prompt_embeds

      return prompt_embeds







    @torch.no_grad()
    @replace_example_docstring(_lowerCamelCase	)
    def __call__(      self							,		_lowerCamelCase							,		_lowerCamelCase = 1							,		_lowerCamelCase = 2_5							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		_lowerCamelCase = 4.0							,		_lowerCamelCase = 6_4							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = True							,		):

      if isinstance(_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	1
      elif isinstance(_lowerCamelCase							,		_lowerCamelCase	):
        lowercase							   =	len(_lowerCamelCase	)
      else:
        raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_lowerCamelCase	)}'	)

      lowercase							   =	self._execution_device

      lowercase							   =	batch_size * num_images_per_prompt

      lowercase							   =	guidance_scale > 1.0
      lowercase							   =	self._encode_prompt(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	)

      # prior

      self.scheduler.set_timesteps(_lowerCamelCase							,		device=_lowerCamelCase	)
      lowercase							   =	self.scheduler.timesteps

      lowercase							   =	self.prior.config.num_embeddings
      lowercase							   =	self.prior.config.embedding_dim

      lowercase							   =	self.prepare_latents(
          (batch_size, num_embeddings * embedding_dim)							,		prompt_embeds.dtype							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		self.scheduler							,		)

      # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
      lowercase							   =	latents.reshape(latents.shape[0]							,		_lowerCamelCase							,		_lowerCamelCase	)

      for i, t in enumerate(self.progress_bar(_lowerCamelCase	)	):
        # expand the latents if we are doing classifier free guidance
        lowercase							   =	torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
        lowercase							   =	self.scheduler.scale_model_input(_lowerCamelCase							,		_lowerCamelCase	)

        lowercase							   =	self.prior(
            _lowerCamelCase							,		timestep=_lowerCamelCase							,		proj_embedding=_lowerCamelCase							,		).predicted_image_embedding

        # remove the variance
        lowercase   ,					lowercase							   =	noise_pred.split(
            scaled_model_input.shape[2]							,		dim=2	)  # batch_size, num_embeddings, embedding_dim

        if do_classifier_free_guidance is not None:
          lowercase   ,					lowercase							   =	noise_pred.chunk(2	)
          lowercase							   =	noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

        lowercase							   =	self.scheduler.step(
            _lowerCamelCase							,		timestep=_lowerCamelCase							,		sample=_lowerCamelCase							,		).prev_sample

      if output_type == "latent":
        return ShapEPipelineOutput(images=_lowerCamelCase	)

      lowercase							   =	[]
      for i, latent in enumerate(_lowerCamelCase	):
        lowercase							   =	self.renderer.decode(
            latent[None, :]							,		_lowerCamelCase							,		size=_lowerCamelCase							,		ray_batch_size=4_0_9_6							,		n_coarse_samples=6_4							,		n_fine_samples=1_2_8							,		)
        images.append(_lowerCamelCase	)

      lowercase							   =	torch.stack(_lowerCamelCase	)

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

      lowercase							   =	images.cpu().numpy()

      if output_type == "pil":
        lowercase							   =	[self.numpy_to_pil(_lowerCamelCase	) for image in images]

      # Offload last model to CPU
      if hasattr(self							,		'final_offload_hook'	) and self.final_offload_hook is not None:
        self.final_offload_hook.offload()

      if not return_dict:
        return (images,)

      return ShapEPipelineOutput(images=_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}''')


