"""simple docstring"""



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

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

SCREAMING_SNAKE_CASE__:List[Any]		       =       '\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\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], "corgi_3d.gif")\n        ```\n'






@dataclass
class       snake_case__	(    _lowerCamelCase    ):
 _snake_case    : List[Any]				   =		42






class       snake_case__	(    _lowerCamelCase    ):


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

        self.register_modules(
            prior=_UpperCamelCase  ,						image_encoder=_UpperCamelCase  ,						image_processor=_UpperCamelCase  ,						scheduler=_UpperCamelCase  ,						renderer=_UpperCamelCase  ,						)


 def 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        if latents is None:
               __a	      =  randn_tensor(_UpperCamelCase  ,						generator=_UpperCamelCase  ,						device=_UpperCamelCase  ,						dtype=_UpperCamelCase		)
        else:
               if latents.shape != shape:
                      raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
               __a	      =  latents.to(_UpperCamelCase		)

        __a	      =  latents * scheduler.init_noise_sigma
        return latents


 def 		a__					(      self  ,						lowerCamelCase=0		):
        if is_accelerate_available():
               from accelerate import cpu_offload
        else:
               raise ImportError("Please install accelerate via `pip install accelerate`"		)

        __a	      =  torch.device(F"cuda:{gpu_id}"		)

        __a	      =  [self.image_encoder, self.prior]
        for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                      cpu_offload(_UpperCamelCase  ,						_UpperCamelCase		)


 @property
 def 		a__					(      self		):
        if self.device != torch.device("meta"		) or not hasattr(self.image_encoder  ,						"_hf_hook"		):
               return self.device
        for module in self.image_encoder.modules():
               if (
                   hasattr(_UpperCamelCase  ,						"_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 		a__					(      self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						):
        if isinstance(_UpperCamelCase  ,						_UpperCamelCase		) and isinstance(image[0]  ,						torch.Tensor		):
               __a	      =  torch.cat(_UpperCamelCase  ,						axis=0		) if image[0].ndim == 4 else torch.stack(_UpperCamelCase  ,						axis=0		)

        if not isinstance(_UpperCamelCase  ,						torch.Tensor		):
               __a	      =  self.image_processor(_UpperCamelCase  ,						return_tensors="pt"		).pixel_values[0].unsqueeze(0		)

        __a	      =  image.to(dtype=self.image_encoder.dtype  ,						device=_UpperCamelCase		)

        __a	      =  self.image_encoder(_UpperCamelCase		)["last_hidden_state"]
        __a	      =  image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

        __a	      =  image_embeds.repeat_interleave(_UpperCamelCase  ,						dim=0		)

        if do_classifier_free_guidance:
               __a	      =  torch.zeros_like(_UpperCamelCase		)

               # 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
               __a	      =  torch.cat([negative_image_embeds, image_embeds]		)

        return image_embeds






 @torch.no_grad()
 @replace_example_docstring(_UpperCamelCase		)
 def __call__(      self  ,						lowerCamelCase  ,						lowerCamelCase = 1  ,						lowerCamelCase = 25  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = 4.0  ,						lowerCamelCase = 64  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						):

        if isinstance(_UpperCamelCase  ,						PIL.Image.Image		):
               __a	      =  1
        elif isinstance(_UpperCamelCase  ,						torch.Tensor		):
               __a	      =  image.shape[0]
        elif isinstance(_UpperCamelCase  ,						_UpperCamelCase		) and isinstance(image[0]  ,						(torch.Tensor, PIL.Image.Image)		):
               __a	      =  len(_UpperCamelCase		)
        else:
               raise ValueError(
                   F"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_UpperCamelCase		)}"		)

        __a	      =  self._execution_device

        __a	      =  batch_size * num_images_per_prompt

        __a	      =  guidance_scale > 1.0
        __a	      =  self._encode_image(_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase		)

        # prior

        self.scheduler.set_timesteps(_UpperCamelCase  ,						device=_UpperCamelCase		)
        __a	      =  self.scheduler.timesteps

        __a	      =  self.prior.config.num_embeddings
        __a	      =  self.prior.config.embedding_dim

        __a	      =  self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)  ,						image_embeds.dtype  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						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
        __a	      =  latents.reshape(latents.shape[0]  ,						_UpperCamelCase  ,						_UpperCamelCase		)

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

               __a	      =  self.prior(
                   _UpperCamelCase  ,						timestep=_UpperCamelCase  ,						proj_embedding=_UpperCamelCase  ,						).predicted_image_embedding

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

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

               __a	      =  self.scheduler.step(
                   _UpperCamelCase  ,						timestep=_UpperCamelCase  ,						sample=_UpperCamelCase  ,						).prev_sample

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

        __a	      =  []
        for i, latent in enumerate(_UpperCamelCase		):
               print()
               __a	      =  self.renderer.decode(
                   latent[None, :]  ,						_UpperCamelCase  ,						size=_UpperCamelCase  ,						ray_batch_size=4096  ,						n_coarse_samples=64  ,						n_fine_samples=128  ,						)

               images.append(_UpperCamelCase		)

        __a	      =  torch.stack(_UpperCamelCase		)

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

        __a	      =  images.cpu().numpy()

        if output_type == "pil":
               __a	      =  [self.numpy_to_pil(_UpperCamelCase		) 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=_UpperCamelCase		)







import os

snake_case__					:							Any													=     {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value



def _a				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

      __A          =				num // 10_00
      numerals += m_count * "M"
      num %= 10_00

      __A          =				num // 1_00
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 1_00

      __A          =				num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals



def _a				(	lowerCamelCase:   str = "/p089_roman.txt" )	->						int:







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

      for line in lines:
            __A          =				line.strip()
            __A          =				parse_roman_numerals(lowerCamelCase )
            __A          =				generate_roman_numerals(lowerCamelCase )
            savings += len(lowerCamelCase ) - len(lowerCamelCase )

      return savings


if __name__ == "__main__":
       print(f'{solution() = }')





