

import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							CLIPConfig

    lowerCamelCase			=							['CLIPEncoderLayer']


    def __init__(      self :       int,lowercase_ :       CLIPConfig		)->    Union[str, Any]:





        '''simple docstring'''







        super().__init__(lowercase_		)

        A__          =   CLIPVisionModelWithProjection(config.vision_config		)

        A__          =   nn.Linear(config.vision_config.projection_dim,1		)
        A__          =   nn.Linear(config.vision_config.projection_dim,1		)


    @torch.no_grad()
    def 						snake_case__     (      self :       Optional[int],lowercase_ :       List[Any],lowercase_ :       Dict,lowercase_ :       str=0.5,lowercase_ :       List[Any]=0.5		)->    List[str]:





        '''simple docstring'''







        A__          =   self.vision_model(lowercase_		)[0]

        A__          =   self.p_head(lowercase_		)
        A__          =   nsfw_detected.flatten()
        A__          =   nsfw_detected > p_threshold
        A__          =   nsfw_detected.tolist()

        if any(lowercase_		):
            logger.warning(
                'Potential NSFW content was detected in one or more images. A black image will be returned instead.'
                ' Try again with a different prompt and/or seed.'		)

        for idx, nsfw_detected_ in enumerate(lowercase_		):
            if nsfw_detected_:
                A__          =   np.zeros(images[idx].shape		)

        A__          =   self.w_head(lowercase_		)
        A__          =   watermark_detected.flatten()
        A__          =   watermark_detected > w_threshold
        A__          =   watermark_detected.tolist()

        if any(lowercase_		):
            logger.warning(
                'Potential watermarked content was detected in one or more images. A black image will be returned instead.'
                ' Try again with a different prompt and/or seed.'		)

        for idx, watermark_detected_ in enumerate(lowercase_		):
            if watermark_detected_:
                A__          =   np.zeros(images[idx].shape		)

        return images, nsfw_detected, watermark_detected




from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


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

