




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

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


_lowerCamelCase					:     Tuple					  =  logging.get_logger(__name__)

_lowerCamelCase					:     int					  =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase					:     Optional[Any]					  =  {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}
class 				__snake_case     (_a						):



				def __init__(					self					:						Any						,		_UpperCAmelCase					:						str=None						,		_UpperCAmelCase					:						List[Any]=None						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						Optional[Any]=True						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						str="<s>"						,		_UpperCAmelCase					:						Union[str, Any]="</s>"						,		_UpperCAmelCase					:						Optional[Any]="<unk>"						,		_UpperCAmelCase					:						Union[str, Any]="<sep>"						,		_UpperCAmelCase					:						Any="<pad>"						,		_UpperCAmelCase					:						str="<cls>"						,		_UpperCAmelCase					:						Optional[Any]="<mask>"						,		_UpperCAmelCase					:						Optional[int]=["<eop>", "<eod>"]						,		**_UpperCAmelCase					:						Tuple						,		)     ->			int:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					AddedToken(_UpperCAmelCase						,		lstrip=_UpperCAmelCase						,		rstrip=_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else mask_token

								super().__init__(
								    vocab_file=_UpperCAmelCase						,		tokenizer_file=_UpperCAmelCase						,		do_lower_case=_UpperCAmelCase						,		remove_space=_UpperCAmelCase						,		keep_accents=_UpperCAmelCase						,		bos_token=_UpperCAmelCase						,		eos_token=_UpperCAmelCase						,		unk_token=_UpperCAmelCase						,		sep_token=_UpperCAmelCase						,		pad_token=_UpperCAmelCase						,		cls_token=_UpperCAmelCase						,		mask_token=_UpperCAmelCase						,		additional_special_tokens=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

								_lowerCAmelCase			:			List[Any]        =					3
								_lowerCAmelCase			:			Optional[int]        =					do_lower_case
								_lowerCAmelCase			:			List[Any]        =					remove_space
								_lowerCAmelCase			:			Tuple        =					keep_accents
								_lowerCAmelCase			:			int        =					vocab_file
								_lowerCAmelCase			:			List[str]        =					False if not self.vocab_file else True

								try:
												import jieba
								except ModuleNotFoundError as error:
												raise error.__class__(
												    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
												    """See https://pypi.org/project/jieba/ for installation."""					)
								_lowerCAmelCase			:			Union[str, Any]        =					jieba
								_lowerCAmelCase			:			Any        =					str.maketrans(""" \n"""						,		"""\u2582\u2583"""					)



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





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					[self.sep_token_id]
								_lowerCAmelCase			:			List[str]        =					[self.cls_token_id]
								if token_ids_a is None:
												return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						List[int]						,		_UpperCAmelCase					:						Optional[List[int]] = None					)     ->			List[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					[self.sep_token_id]
								_lowerCAmelCase			:			Tuple        =					[2]

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



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						Optional[str] = None					)     ->			Tuple[str]:





								'''simple docstring'''




								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""					)

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

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

								return (out_vocab_file,)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]						,		_UpperCAmelCase					:						List[Any]						,		*_UpperCAmelCase					:						List[str]						,		**_UpperCAmelCase					:						Union[str, Any]					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					[
								    """ """.join([x.translate(self.translator					) for x in self.jieba.cut(_UpperCAmelCase						,		cut_all=_UpperCAmelCase					)]					)
								    for text in batch_text_or_text_pairs
								]
								return super()._batch_encode_plus(_UpperCAmelCase						,		*_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		*_UpperCAmelCase					:						List[Any]						,		**_UpperCAmelCase					:						Optional[int]					)     ->			Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					super()._decode(*_UpperCAmelCase						,		**_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					text.replace(""" """						,		""""""					).replace("""\u2582"""						,		""" """					).replace("""\u2583"""						,		"""\n"""					)
								return text







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

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

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


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

