
import argparse

import torch

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


logging.set_verbosity_info()
lowercase  :					Tuple													=       logging.get_logger(__name__)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[str]						,  _lowerCamelCase :			str						,  _lowerCamelCase :			Optional[Any])					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					WavaVecaForSequenceClassification.from_pretrained(_lowerCamelCase						,  config=_lowerCamelCase)
							__UpperCamelCase	:   int			   =					downstream_dict["projector.weight"]
							__UpperCamelCase	:   Optional[Any]			   =					downstream_dict["projector.bias"]
							__UpperCamelCase	:   Optional[int]			   =					downstream_dict["model.post_net.linear.weight"]
							__UpperCamelCase	:   Optional[int]			   =					downstream_dict["model.post_net.linear.bias"]
							return model




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Dict						,  _lowerCamelCase :			Optional[Any])					->    Optional[int]:






							'''simple docstring'''





							__UpperCamelCase	:   List[Any]			   =					WavaVecaForAudioFrameClassification.from_pretrained(_lowerCamelCase						,  config=_lowerCamelCase)
							__UpperCamelCase	:   Any			   =					downstream_dict["model.linear.weight"]
							__UpperCamelCase	:   Any			   =					downstream_dict["model.linear.bias"]
							return model




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			List[Any])					->    List[str]:






							'''simple docstring'''





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

							__UpperCamelCase	:   Optional[Any]			   =					downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
							__UpperCamelCase	:   Any			   =					downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
							__UpperCamelCase	:   Optional[int]			   =					downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
							__UpperCamelCase	:   str			   =					downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
							__UpperCamelCase	:   Union[str, Any]			   =					downstream_dict["objective.W"]
							return model




@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[str]						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			Any						,  _lowerCamelCase :			Tuple)					->    List[Any]:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					torch.load(_lowerCamelCase						,  map_location="cpu")

							__UpperCamelCase	:   List[str]			   =					checkpoint["Downstream"]

							__UpperCamelCase	:   Any			   =					WavaVecaConfig.from_pretrained(_lowerCamelCase)
							__UpperCamelCase	:   int			   =					WavaVecaFeatureExtractor.from_pretrained(
							    _lowerCamelCase						,  return_attention_mask=_lowerCamelCase						,  do_normalize=_lowerCamelCase)

							__UpperCamelCase	:   Tuple			   =					hf_config.architectures[0]
							if arch.endswith("ForSequenceClassification"):
														__UpperCamelCase	:   List[str]			   =					convert_classification(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
							elif arch.endswith("ForAudioFrameClassification"):
														__UpperCamelCase	:   List[Any]			   =					convert_diarization(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
							elif arch.endswith("ForXVector"):
														__UpperCamelCase	:   List[Any]			   =					convert_xvector(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
							else:
														raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}')

							if hf_config.use_weighted_layer_sum:
														__UpperCamelCase	:   List[Any]			   =					checkpoint["Featurizer"]["weights"]

							hf_feature_extractor.save_pretrained(_lowerCamelCase)
							hf_model.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
		lowercase  :					int													=       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.')
		lowercase  :					Any													=       parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase  :					int													=       logging.get_logger(__name__)


lowercase  :					Union[str, Any]													=       '▁'

lowercase  :					Tuple													=       {'vocab_file': 'spiece.model'}

lowercase  :					Dict													=       {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

lowercase  :					Any													=       {
    'google/reformer-crime-and-punishment': 524288,
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







	def __init__(					self		:int     ,   a		:List[Any]     ,   a		:Tuple="</s>"     ,   a		:str="<unk>"     ,   a		:Dict=[]     ,   a		:Optional[Dict[str, Any]] = None     ,   **a		:Union[str, Any]     ,   )		-> None:
								__UpperCamelCase	:   str			   =					{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=a     ,   unk_token=a     ,   additional_special_tokens=a     ,   sp_model_kwargs=self.sp_model_kwargs     ,   **a     ,   )

								__UpperCamelCase	:   Optional[Any]			   =					vocab_file
								__UpperCamelCase	:   Dict			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(a						)







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								return self.sp_model.get_piece_size()







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Dict[str, int]:
								__UpperCamelCase	:   str			   =					{self.convert_ids_to_tokens(a						): i for i in range(self.vocab_size						)}
								vocab.update(self.added_tokens_encoder						)
								return vocab







	def __getstate__(					self		:str						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.__dict__.copy()
								__UpperCamelCase	:   Optional[Any]			   =					None
								return state







	def __setstate__(					self		:int     ,   a		:List[str]						)		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					d

								# for backward compatibility
								if not hasattr(self     ,   "sp_model_kwargs"						):
															__UpperCamelCase	:   int			   =					{}

								__UpperCamelCase	:   Any			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(self.vocab_file						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str						)		-> List[str]:
								return self.sp_model.encode(a     ,   out_type=a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[Any]						)		-> str:
								return self.sp_model.piece_to_id(a						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Union[str, Any]						)		-> Optional[int]:
								if index < self.sp_model.get_piece_size():
															__UpperCamelCase	:   Optional[int]			   =					self.sp_model.IdToPiece(a						)
								return token







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					[]
								__UpperCamelCase	:   str			   =					""
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(a						) + token
																						__UpperCamelCase	:   List[Any]			   =					[]
															else:
																						current_sub_tokens.append(a						)
								out_string += self.sp_model.decode(a						)
								return out_string.strip()







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:Optional[str] = None						)		-> Tuple[str]:
								if not os.path.isdir(a						):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
															return
								__UpperCamelCase	:   List[Any]			   =					os.path.join(
								    a     ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

								if os.path.abspath(self.vocab_file						) != os.path.abspath(a						) and os.path.isfile(self.vocab_file						):
															copyfile(self.vocab_file     ,   a						)
								elif not os.path.isfile(self.vocab_file						):
															with open(a     ,   "wb"						) as fi:
																						__UpperCamelCase	:   int			   =					self.sp_model.serialized_model_proto()
																						fi.write(a						)

								return (out_vocab_file,)