
import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase   						=     logging.get_logger(__name__)


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,  config=__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''projector.weight''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''projector.bias''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.post_net.linear.weight''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.post_net.linear.bias''']
   return model


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,  config=__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.linear.weight''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.linear.bias''']
   return model


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      WavLMForXVector.from_pretrained(__lowerCamelCase,  config=__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''connector.weight''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      SCREAMING_SNAKE_CASE_	=      downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      SCREAMING_SNAKE_CASE_	=      downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   SCREAMING_SNAKE_CASE_	=      downstream_dict['''objective.W''']
   return model


@torch.no_grad()
def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      torch.load(__lowerCamelCase,  map_location='''cpu'''						)

   SCREAMING_SNAKE_CASE_	=      checkpoint['''Downstream''']

   SCREAMING_SNAKE_CASE_	=      WavLMConfig.from_pretrained(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,  return_attention_mask=__lowerCamelCase,  do_normalize=__lowerCamelCase						)

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

   if hf_config.use_weighted_layer_sum:
      SCREAMING_SNAKE_CASE_	=      checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(__lowerCamelCase						)
   hf_model.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
   __UpperCAmelCase   						=     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.")
   __UpperCAmelCase   						=     parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__UpperCAmelCase   						=     datasets.utils.logging.get_logger(__name__)





class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilderConfig	):






       """simple docstring"""


       UpperCAmelCase_				       =None
       UpperCAmelCase_				       =None







class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilder	):






       """simple docstring"""


       UpperCAmelCase_				       =datasets.Image()
       UpperCAmelCase_				       ="image"
       UpperCAmelCase_				       =ImageFolderConfig
       UpperCAmelCase_				       =42  # definition at the bottom of the script
       UpperCAmelCase_				       =ImageClassification(image_column="image"       ,    label_column="label"	)







__UpperCAmelCase   						=     [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
__UpperCAmelCase   						=     IMAGE_EXTENSIONS


