



"""simple docstring"""






from typing import List, Optional, Union

import torch

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,
)


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

snake_case   					=				"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=8	)						->				Any:
  _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







class     UpperCAmelCase    (	_a  ):



   def __init__(       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       ):



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

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



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     if latents is None:
       _snake_case   	=      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}"""				)
       _snake_case   	=      latents.to(lowerCamelCase__				)

     _snake_case   	=      latents * scheduler.init_noise_sigma
     return latents



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	Union[str, Any]=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(lowerCamelCase__							,       lowerCamelCase__				)



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	List[Any]=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=lowerCamelCase__				)
       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   	=      cpu_offload_with_hook(lowerCamelCase__							,       lowerCamelCase__							,       prev_module_hook=lowerCamelCase__				)

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



   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     if not hasattr(self.unet							,       '''_hf_hook'''				):
       return self.device
     for module in self.unet.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





   @torch.no_grad()
   @replace_example_docstring(lowerCamelCase__				)
   def __call__(       self    :	Any							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	int = 5_1_2							,       __lowerCamelCase    :	Any = 5_1_2							,       __lowerCamelCase    :	Tuple = 1_0_0							,       __lowerCamelCase    :	Any = 4.0							,       __lowerCamelCase    :	List[str] = 1							,       __lowerCamelCase    :	List[Any] = None							,       __lowerCamelCase    :	str = None							,       __lowerCamelCase    :	str = "pil"							,       __lowerCamelCase    :	Tuple = True							,       ):



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

     _snake_case   	=      guidance_scale > 1.0

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

     _snake_case   	=      image_embeds.shape[0] * num_images_per_prompt

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

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

     _snake_case   	=      self.movq.config.latent_channels

     _snake_case   	=      downscale_height_and_width(lowerCamelCase__							,       lowerCamelCase__							,       self.movq_scale_factor				)

     # create initial latent
     _snake_case   	=      self.prepare_latents(
         (batch_size, num_channels_latents, height, width)							,       image_embeds.dtype							,       lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       self.scheduler							,       )

     for i, t in enumerate(self.progress_bar(lowerCamelCase__				)				):
       # 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=lowerCamelCase__							,       timestep=lowerCamelCase__							,       encoder_hidden_states=lowerCamelCase__							,       added_cond_kwargs=lowerCamelCase__							,       return_dict=lowerCamelCase__							,       )[0]

       if do_classifier_free_guidance:
         _snake_case   	=      noise_pred.split(latents.shape[1]							,       dim=1				)
         _snake_case   	=      noise_pred.chunk(2				)
         _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   	=      noise_pred.split(latents.shape[1]							,       dim=1				)

       # compute the previous noisy sample x_t -> x_t-1
       _snake_case   	=      self.scheduler.step(
           lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       generator=lowerCamelCase__							,       )[0]
     # post-processing
     _snake_case   	=      self.movq.decode(lowerCamelCase__							,       force_not_quantize=lowerCamelCase__				)["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(lowerCamelCase__				)

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=lowerCamelCase__				)








import fire

from utils import calculate_rouge, save_json



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()]
	UpperCAmelCase__:	Dict									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
	UpperCAmelCase__:	Any									= calculate_rouge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      )
	if save_path is not None:
		save_json(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,indent=SCREAMING_SNAKE_CASE      )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)