

"""simple docstring"""



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

from ...utils import logging


SCREAMING_SNAKE_CASE__:List[str]			=	logging.get_logger(__name__)






class 				snake_case__     (   __UpperCamelCase   ):
 _snake_case   :			Dict           =		CLIPConfig

 _snake_case   :			Any           =		["CLIPEncoderLayer"]


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

        __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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase=0.5  ,						lowerCamelCase=0.5		):
        __a     				=  self.vision_model(_lowerCAmelCase		)[0]

        __a     				=  self.p_head(_lowerCAmelCase		)
        __a     				=  nsfw_detected.flatten()
        __a     				=  nsfw_detected > p_threshold
        __a     				=  nsfw_detected.tolist()

        if any(_lowerCAmelCase		):
               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(_lowerCAmelCase		):
               if nsfw_detected_:
                      __a     				=  np.zeros(images[idx].shape		)

        __a     				=  self.w_head(_lowerCAmelCase		)
        __a     				=  watermark_detected.flatten()
        __a     				=  watermark_detected > w_threshold
        __a     				=  watermark_detected.tolist()

        if any(_lowerCAmelCase		):
               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(_lowerCAmelCase		):
               if watermark_detected_:
                      __a     				=  np.zeros(images[idx].shape		)

        return images, nsfw_detected, watermark_detected

"""simple docstring"""



import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Dict			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["projector.weight"]
       __a     				=  downstream_dict["projector.bias"]
       __a     				=  downstream_dict["model.post_net.linear.weight"]
       __a     				=  downstream_dict["model.post_net.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["connector.weight"]
       __a     				=  downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
              __a     				=  downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              __a     				=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       __a     				=  downstream_dict["objective.W"]
       return model



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

       __a     				=  WavaVecaConfig.from_pretrained(a							)
       __a     				=  WavaVecaFeatureExtractor.from_pretrained(
           a ,  return_attention_mask=a ,  do_normalize=a							)

       __a     				=  hf_config.architectures[0]
       if arch.endswith("ForSequenceClassification"							):
              __a     				=  convert_classification(a ,  a ,  a							)
       elif arch.endswith("ForAudioFrameClassification"							):
              __a     				=  convert_diarization(a ,  a ,  a							)
       elif arch.endswith("ForXVector"							):
              __a     				=  convert_xvector(a ,  a ,  a							)
       else:
              raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"							)

       if hf_config.use_weighted_layer_sum:
              __a     				=  checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(a							)
       hf_model.save_pretrained(a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, Any]			=	argparse.ArgumentParser()
   parser.add_argument(
       """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
   )
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
   parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
   parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
   SCREAMING_SNAKE_CASE__:Any			=	parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
