



from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


lowercase_						=       logging.get_logger(__name__)  # pylint: disable=invalid-name
class 			_snake_case     (   lowercase__):




				def __init__(  self   :  Any,   __lowercase   :  CLIPSegForImageSegmentation,   __lowercase   :  CLIPSegProcessor,   __lowercase   :  AutoencoderKL,   __lowercase   :  CLIPTextModel,   __lowercase   :  CLIPTokenizer,   __lowercase   :  UNetaDConditionModel,   __lowercase   :  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],   __lowercase   :  StableDiffusionSafetyChecker,   __lowercase   :  CLIPImageProcessor,   ):
							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										    F'''The configuration file of this scheduler: {scheduler} is outdated.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",   __lowercase,   standard_warn=__lowercase  )
										lowercase__   					=			dict(scheduler.config  )
										lowercase__   					=			1
										lowercase__   					=			FrozenDict(__lowercase  )

							if hasattr(scheduler.config,   "skip_prk_steps"  ) and scheduler.config.skip_prk_steps is False:
										lowercase__   					=			(
										    F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
										    " `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
										    " sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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("skip_prk_steps not set",   "1.0.0",   __lowercase,   standard_warn=__lowercase  )
										lowercase__   					=			dict(scheduler.config  )
										lowercase__   					=			True
										lowercase__   					=			FrozenDict(__lowercase  )

							if safety_checker is None:
										logger.warning(
										    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
										    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
										    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
										    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
										    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
										    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."  )

							self.register_modules(
							    segmentation_model=__lowercase,   segmentation_processor=__lowercase,   vae=__lowercase,   text_encoder=__lowercase,   tokenizer=__lowercase,   unet=__lowercase,   scheduler=__lowercase,   safety_checker=__lowercase,   feature_extractor=__lowercase,   )




				def 							A__ (  self   :  Dict,   __lowercase   :  Optional[Union[str, int]] = "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(__lowercase  )




				def 							A__ (  self   :  Any  ):
							self.enable_attention_slicing(__lowercase  )




				def 							A__ (  self   :  Optional[Any]  ):
							if is_accelerate_available():
										from accelerate import cpu_offload
							else:
										raise ImportError("Please install accelerate via `pip install accelerate`"  )

							lowercase__   					=			torch.device("cuda"  )

							for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
										if cpu_offloaded_model is not None:
													cpu_offload(__lowercase,   __lowercase  )




				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 							A__ (  self   :  Union[str, Any]  ):
							if self.device != torch.device("meta"  ) or not hasattr(self.unet,   "_hf_hook"  ):
										return self.device
							for module in self.unet.modules():
										if (
										    hasattr(__lowercase,   "_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()
				def __call__(  self   :  Any,   __lowercase   :  Union[str, List[str]],   __lowercase   :  Union[torch.FloatTensor, PIL.Image.Image],   __lowercase   :  str,   __lowercase   :  int = 512,   __lowercase   :  int = 512,   __lowercase   :  int = 50,   __lowercase   :  float = 7.5,   __lowercase   :  Optional[Union[str, List[str]]] = None,   __lowercase   :  Optional[int] = 1,   __lowercase   :  float = 0.0,   __lowercase   :  Optional[torch.Generator] = None,   __lowercase   :  Optional[torch.FloatTensor] = None,   __lowercase   :  Optional[str] = "pil",   __lowercase   :  bool = True,   __lowercase   :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None,   __lowercase   :  int = 1,   **__lowercase   :  Optional[int],   ):
							lowercase__   					=			self.segmentation_processor(
							    text=[text],   images=[image],   padding="max_length",   return_tensors="pt"  ).to(self.device  )
							lowercase__   					=			self.segmentation_model(**__lowercase  )
							lowercase__   					=			torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
							lowercase__   					=			self.numpy_to_pil(__lowercase  )[0].resize(image.size  )

							# Run inpainting pipeline with the generated mask
							lowercase__   					=			StableDiffusionInpaintPipeline(
							    vae=self.vae,   text_encoder=self.text_encoder,   tokenizer=self.tokenizer,   unet=self.unet,   scheduler=self.scheduler,   safety_checker=self.safety_checker,   feature_extractor=self.feature_extractor,   )
							return inpainting_pipeline(
							    prompt=__lowercase,   image=__lowercase,   mask_image=__lowercase,   height=__lowercase,   width=__lowercase,   num_inference_steps=__lowercase,   guidance_scale=__lowercase,   negative_prompt=__lowercase,   num_images_per_prompt=__lowercase,   eta=__lowercase,   generator=__lowercase,   latents=__lowercase,   output_type=__lowercase,   return_dict=__lowercase,   callback=__lowercase,   callback_steps=__lowercase,   )









import fire

from utils import calculate_rouge, save_json



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					**SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()]
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()][: len(SCREAMING_SNAKE_CASE_					)]
			lowercase__   					=			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)




