"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						lowerCamelCase=""  ,						lowerCamelCase="train"		):
        assert os.path.isdir(lowerCamelCase		)

        __a     				=  []
        __a     				=  os.listdir(lowerCamelCase		)
        for story_filename in story_filenames_list:
               if "summary" in story_filename:
                      continue
               __a     				=  os.path.join(lowerCamelCase  ,						lowerCamelCase		)
               if not os.path.isfile(lowerCamelCase		):
                      continue
               self.documents.append(lowerCamelCase		)


 def __len__(				self		):
        return len(self.documents		)






 def __getitem__(				self  ,						lowerCamelCase		):
        __a     				=  self.documents[idx]
        __a     				=  document_path.split("/"		)[-1]
        with open(lowerCamelCase  ,						encoding="utf-8"		) as source:
               __a     				=  source.read()
               __a				, __a     				=  process_story(lowerCamelCase		)
        return document_name, story_lines, summary_lines



def 					_lowerCamelCase(					a							):
       __a     				=  list(filter(lambda a							: len(a							) != 0 ,  [line.strip() for line in raw_story.split("\n"							)]							)							)

       # for some unknown reason some lines miss a period, add it
       __a     				=  [_add_missing_period(a							) for line in nonempty_lines]

       # gather article lines
       __a     				=  []
       __a     				=  deque(a							)
       while True:
              try:
                     __a     				=  lines.popleft()
                     if element.startswith("@highlight"							):
                            break
                     story_lines.append(a							)
              except IndexError:
                     # if "@highlight" is absent from the file we pop
                     # all elements until there is None, raising an exception.
                     return story_lines, []

    # gather summary lines
       __a     				=  list(filter(lambda a							: not t.startswith("@highlight"							) ,  a							)							)

       return story_lines, summary_lines



def 					_lowerCamelCase(					a							):
       __a     				=  [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
       if line.startswith("@highlight"							):
              return line
       if line[-1] in END_TOKENS:
              return line
       return line + "."



def 					_lowerCamelCase(					a ,  a ,  a							):
       if len(a							) > block_size:
              return sequence[:block_size]
       else:
              sequence.extend([pad_token_id] * (block_size - len(a							))							)
              return sequence



def 					_lowerCamelCase(					a ,  a							):
       __a     				=  torch.ones_like(a							)
       __a     				=  sequence == pad_token_id
       __a     				=  0
       return mask



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  [tokenizer.encode(a							) for line in story_lines]
       __a     				=  [token for sentence in story_lines_token_ids for token in sentence]
       __a     				=  [tokenizer.encode(a							) for line in summary_lines]
       __a     				=  [token for sentence in summary_lines_token_ids for token in sentence]

       return story_token_ids, summary_token_ids




def 					_lowerCamelCase(					a ,  a							):
       __a     				=  []
       for sequence in batch:
              __a     				=  -1
              __a     				=  []
              for s in sequence:
                     if s == separator_token_id:
                            sentence_num += 1
                     embeddings.append(sentence_num % 2							)
              batch_embeddings.append(a							)
       return torch.tensor(a							)

"""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)
