


'''simple docstring'''


import argparse

import torch

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


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






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[str]	    =					WavaVecaForSequenceClassification.from_pretrained(__lowerCAmelCase					,       config=__lowerCAmelCase      )
		_UpperCAmelCase						:				Tuple	    =					downstream_dict["projector.weight"]
		_UpperCAmelCase						:				str	    =					downstream_dict["projector.bias"]
		_UpperCAmelCase						:				Optional[int]	    =					downstream_dict["model.post_net.linear.weight"]
		_UpperCAmelCase						:				List[str]	    =					downstream_dict["model.post_net.linear.bias"]
		return model






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					WavaVecaForAudioFrameClassification.from_pretrained(__lowerCAmelCase					,       config=__lowerCAmelCase      )
		_UpperCAmelCase						:				List[str]	    =					downstream_dict["model.linear.weight"]
		_UpperCAmelCase						:				Dict	    =					downstream_dict["model.linear.bias"]
		return model






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				str	    =					WavaVecaForXVector.from_pretrained(__lowerCAmelCase					,       config=__lowerCAmelCase      )
		_UpperCAmelCase						:				int	    =					downstream_dict["connector.weight"]
		_UpperCAmelCase						:				Optional[Any]	    =					downstream_dict["connector.bias"]
		for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
				_UpperCAmelCase						:				Optional[Any]	    =					downstream_dict[
				    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
				]
				_UpperCAmelCase						:				int	    =					downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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






@torch.no_grad()
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Tuple	    =					torch.load(__lowerCAmelCase					,       map_location="cpu"      )

		_UpperCAmelCase						:				Tuple	    =					checkpoint["Downstream"]

		_UpperCAmelCase						:				Optional[int]	    =					WavaVecaConfig.from_pretrained(__lowerCAmelCase      )
		_UpperCAmelCase						:				List[str]	    =					WavaVecaFeatureExtractor.from_pretrained(
		    __lowerCAmelCase					,       return_attention_mask=__lowerCAmelCase					,       do_normalize=__lowerCAmelCase      )

		_UpperCAmelCase						:				Any	    =					hf_config.architectures[0]
		if arch.endswith("ForSequenceClassification"      ):
				_UpperCAmelCase						:				Optional[int]	    =					convert_classification(__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )
		elif arch.endswith("ForAudioFrameClassification"      ):
				_UpperCAmelCase						:				Any	    =					convert_diarization(__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      )
		elif arch.endswith("ForXVector"      ):
				_UpperCAmelCase						:				List[str]	    =					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						:				Optional[Any]	    =					checkpoint["Featurizer"]["weights"]

		hf_feature_extractor.save_pretrained(__lowerCAmelCase      )
		hf_model.save_pretrained(__lowerCAmelCase      )


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







'''simple docstring'''


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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_fnet import FNetTokenizer
else:
						lowerCamelCase__					=   None

lowerCamelCase__					=   logging.get_logger(__name__)
lowerCamelCase__					=   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					=   {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					=   {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Dict				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Optional[Any]				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		List[str]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Optional[int]				      =   ["input_ids", "token_type_ids"]
			lowerCAmelCase       :		Optional[Any]				      =   FNetTokenizer




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   int=None		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   List[str]=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   str="<unk>"		,     lowerCamelCase__     :   List[str]="[SEP]"		,     lowerCamelCase__     :   Union[str, Any]="<pad>"		,     lowerCamelCase__     :   Optional[Any]="[CLS]"		,     lowerCamelCase__     :   Any="[MASK]"		,     **lowerCamelCase__     :   Any		,     )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					(
					    AddedToken(lowerCamelCase__		,     lstrip=lowerCamelCase__		,     rstrip=lowerCamelCase__		,     normalized=lowerCamelCase__       )
					    if isinstance(lowerCamelCase__		,     lowerCamelCase__       )
					    else mask_token
					)

					super().__init__(
					    lowerCamelCase__		,     tokenizer_file=lowerCamelCase__		,     do_lower_case=lowerCamelCase__		,     remove_space=lowerCamelCase__		,     keep_accents=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     **lowerCamelCase__		,     )

					_UpperCAmelCase						:				Optional[Any]	    =					do_lower_case
					_UpperCAmelCase						:				Tuple	    =					remove_space
					_UpperCAmelCase						:				List[Any]	    =					keep_accents
					_UpperCAmelCase						:				Tuple	    =					vocab_file
					_UpperCAmelCase						:				str	    =					False if not self.vocab_file else True




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Tuple	    =					[self.cls_token_id]
					if token_ids_a is None:
							return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Optional[Any]	    =					[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep       ) * [0]
					return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]






			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					if not os.path.isdir(lowerCamelCase__       ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
							return
					_UpperCAmelCase						:				Union[str, Any]	    =					os.path.join(
					    lowerCamelCase__		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

					if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase__       ):
							copyfile(self.vocab_file		,     lowerCamelCase__       )

					return (out_vocab_file,)



