






'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=True    ,		_UpperCamelCase="pt"				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  {"add_prefix_space": True} if isinstance(_UpperCamelCase    ,		_UpperCamelCase				) and not line.startswith(" "				) else {}
   lowercase_						:     Optional[int]							     =  padding_side
   return tokenizer(
       [line]    ,		max_length=_UpperCamelCase    ,		padding="max_length" if pad_to_max_length else None    ,		truncation=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		add_special_tokens=_UpperCamelCase    ,		**_UpperCamelCase    ,		)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None    ,		):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  input_ids.ne(_UpperCamelCase				).any(dim=0				)
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class 							_UpperCAmelCase    (		snake_case      ):




    def __init__(      self	:							Any	,  a	:							Optional[Any]	,  a	:							Any	,  a	:							Optional[int]	,  a	:							Optional[Any]	,  a	:							Dict="train"	,  a	:							List[Any]=None	,  a	:							Tuple=None	,  a	:							Optional[int]=None	,  a	:							Optional[int]=""	,  ):






       '''simple docstring'''






       super().__init__()
       lowercase_						:     str							     =  Path(a      ).joinpath(type_path + ".source"      )
       lowercase_						:     Union[str, Any]							     =  Path(a      ).joinpath(type_path + ".target"      )
       lowercase_						:     Optional[int]							     =  self.get_char_lens(self.src_file      )
       lowercase_						:     Union[str, Any]							     =  max_source_length
       lowercase_						:     List[str]							     =  max_target_length
       assert min(self.src_lens      ) > 0, f"""found empty line in {self.src_file}"""
       lowercase_						:     List[Any]							     =  tokenizer
       lowercase_						:     List[str]							     =  prefix
       if n_obs is not None:
          lowercase_						:     List[str]							     =  self.src_lens[:n_obs]
       lowercase_						:     List[Any]							     =  src_lang
       lowercase_						:     Tuple							     =  tgt_lang




    def __len__(      self	:							Dict      ):






       '''simple docstring'''






       return len(self.src_lens      )




    def __getitem__(      self	:							str	,  a	:							Union[str, Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  index + 1  # linecache starts at 1
       lowercase_						:     Tuple							     =  self.prefix + linecache.getline(str(self.src_file      )	,  a      ).rstrip("\n"      )
       lowercase_						:     Optional[int]							     =  linecache.getline(str(self.tgt_file      )	,  a      ).rstrip("\n"      )
       assert source_line, f"""empty source line for index {index}"""
       assert tgt_line, f"""empty tgt line for index {index}"""

       # Need to add eos token manually for T5
       if isinstance(self.tokenizer	,  a      ):
          source_line += self.tokenizer.eos_token
          tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       lowercase_						:     str							     =  (
           self.tokenizer.question_encoder if isinstance(self.tokenizer	,  a      ) else self.tokenizer
       )
       lowercase_						:     str							     =  self.tokenizer.generator if isinstance(self.tokenizer	,  a      ) else self.tokenizer

       lowercase_						:     Union[str, Any]							     =  encode_line(a	,  a	,  self.max_source_length	,  "right"      )
       lowercase_						:     Tuple							     =  encode_line(a	,  a	,  self.max_target_length	,  "right"      )

       lowercase_						:     int							     =  source_inputs["input_ids"].squeeze()
       lowercase_						:     Optional[int]							     =  target_inputs["input_ids"].squeeze()
       lowercase_						:     Optional[int]							     =  source_inputs["attention_mask"].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }




    @staticmethod
    def 					lowerCAmelCase__					(      a	:							Dict      ):






       '''simple docstring'''






       return [len(a      ) for x in Path(a      ).open().readlines()]





    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Dict							     =  torch.stack([x["input_ids"] for x in batch]      )
       lowercase_						:     Any							     =  torch.stack([x["attention_mask"] for x in batch]      )
       lowercase_						:     Optional[int]							     =  torch.stack([x["decoder_input_ids"] for x in batch]      )
       lowercase_						:     Union[str, Any]							     =  (
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer	,  a      )
           else self.tokenizer.pad_token_id
       )
       lowercase_						:     Any							     =  (
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer	,  a      )
           else self.tokenizer.pad_token_id
       )
       lowercase_						:     Optional[int]							     =  trim_batch(a	,  a      )
       lowercase_     ,		lowercase_						:     Any							     =  trim_batch(a	,  a	,  attention_mask=a      )
       lowercase_						:     str							     =  {
           "input_ids": source_ids,
           "attention_mask": source_mask,
           "decoder_input_ids": y,
       }
       return batch


UpperCamelCase__			=      getLogger(__name__)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   return list(itertools.chain.from_iterable(_UpperCamelCase				)				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     List[str]							     =  get_git_info()
   save_json(_UpperCamelCase    ,		os.path.join(_UpperCamelCase    ,		"git_log.json"				)				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=4    ,		**_UpperCamelCase				):


   """simple docstring"""




   with open(_UpperCamelCase    ,		"w"				) as f:
      json.dump(_UpperCamelCase    ,		_UpperCamelCase    ,		indent=_UpperCamelCase    ,		**_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   with open(_UpperCamelCase				) as f:
      return json.load(_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   lowercase_						:     Tuple							     =  git.Repo(search_parent_directories=_UpperCamelCase				)
   lowercase_						:     Optional[Any]							     =  {
       "repo_id": str(_UpperCamelCase				),
       "repo_sha": str(repo.head.object.hexsha				),
       "repo_branch": str(repo.active_branch				),
       "hostname": str(socket.gethostname()				),
   }
   return repo_infos




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   return list(map(_UpperCamelCase    ,		_UpperCamelCase				)				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   with open(_UpperCamelCase    ,		"wb"				) as f:
      return pickle.dump(_UpperCamelCase    ,		_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   def remove_articles(_UpperCamelCase				):
      return re.sub(R"\b(a|an|the)\b"    ,		" "    ,		_UpperCamelCase				)

   def white_space_fix(_UpperCamelCase				):
      return " ".join(text.split()				)

   def remove_punc(_UpperCamelCase				):
      lowercase_						:     Optional[Any]							     =  set(string.punctuation				)
      return "".join(ch for ch in text if ch not in exclude				)

   def lower(_UpperCamelCase				):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase				)				)				)				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  normalize_answer(_UpperCamelCase				).split()
   lowercase_						:     Tuple							     =  normalize_answer(_UpperCamelCase				).split()
   lowercase_						:     List[str]							     =  Counter(_UpperCamelCase				) & Counter(_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  sum(common.values()				)
   if num_same == 0:
      return 0
   lowercase_						:     int							     =  1.0 * num_same / len(_UpperCamelCase				)
   lowercase_						:     Union[str, Any]							     =  1.0 * num_same / len(_UpperCamelCase				)
   lowercase_						:     Optional[Any]							     =  (2 * precision * recall) / (precision + recall)
   return fa




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   return normalize_answer(_UpperCamelCase				) == normalize_answer(_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   assert len(_UpperCamelCase				) == len(_UpperCamelCase				)
   lowercase_						:     Dict							     =  0
   for hypo, pred in zip(_UpperCamelCase    ,		_UpperCamelCase				):
      em += exact_match_score(_UpperCamelCase    ,		_UpperCamelCase				)
   if len(_UpperCamelCase				) > 0:
      em /= len(_UpperCamelCase				)
   return {"em": em}




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   return model_prefix.startswith("rag"				)





def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     str							     =  {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   lowercase_						:     List[Any]							     =  "dropout_rate"
   for p in extra_params:
      if getattr(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):
         if not hasattr(_UpperCamelCase    ,		_UpperCamelCase				) and not hasattr(_UpperCamelCase    ,		equivalent_param[p]				):
            logger.info("config doesn't have a `{}` attribute".format(_UpperCamelCase				)				)
            delattr(_UpperCamelCase    ,		_UpperCamelCase				)
            continue
         lowercase_						:     Any							     =  p if hasattr(_UpperCamelCase    ,		_UpperCamelCase				) else equivalent_param[p]
         setattr(_UpperCamelCase    ,		_UpperCamelCase    ,		getattr(_UpperCamelCase    ,		_UpperCamelCase				)				)
         delattr(_UpperCamelCase    ,		_UpperCamelCase				)
   return hparams, config












'''simple docstring'''
import argparse

import torch

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


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

   lowercase_						:     Dict							     =  hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"				):
      lowercase_						:     Any							     =  convert_classification(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForAudioFrameClassification"				):
      lowercase_						:     Optional[int]							     =  convert_diarization(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForXVector"				):
      lowercase_						:     List[Any]							     =  convert_xvector(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


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)




