

'''simple docstring'''

import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class 							_snake_case	(    _a		,					unittest.TestCase  ):
			_A    :       List[str]      				=   BertweetTokenizer
			_A    :       Optional[Any]      				=   False
			def       __UpperCamelCase  (	self     : Tuple							):
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					SCREAMING_SNAKE_CASE:List[Any]   =   ["I", "m", "V@@", "R@@", "r", "e@@"]
					SCREAMING_SNAKE_CASE:Optional[Any]   =   dict(zip(SCREAMING_SNAKE_CASE__       ,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
					SCREAMING_SNAKE_CASE:List[str]   =   ["#version: 0.2", "a m</w>"]
					SCREAMING_SNAKE_CASE:Dict   =   {"unk_token": "<unk>"}

					SCREAMING_SNAKE_CASE:str   =   os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES["vocab_file"]							)
					SCREAMING_SNAKE_CASE:Any   =   os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES["merges_file"]							)
					with open(self.vocab_file       ,"w"       ,encoding="utf-8"							) as fp:
							for token in vocab_tokens:
									fp.write(F'''{token} {vocab_tokens[token]}\n'''							)
					with open(self.merges_file       ,"w"       ,encoding="utf-8"							) as fp:
							fp.write("\n".join(SCREAMING_SNAKE_CASE__							)							)
			def       __UpperCamelCase  (	self     : str       ,**SCREAMING_SNAKE_CASE__     : Tuple							):
					kwargs.update(self.special_tokens_map							)
					return BertweetTokenizer.from_pretrained(self.tmpdirname       ,**SCREAMING_SNAKE_CASE__							)
			def       __UpperCamelCase  (	self     : Optional[Any]       ,SCREAMING_SNAKE_CASE__     : Dict							):
					SCREAMING_SNAKE_CASE:Optional[Any]   =   "I am VinAI Research"
					SCREAMING_SNAKE_CASE:Tuple   =   "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
					return input_text, output_text







			def       __UpperCamelCase  (	self     : Tuple							):
					SCREAMING_SNAKE_CASE:List[Any]   =   BertweetTokenizer(self.vocab_file       ,self.merges_file       ,**self.special_tokens_map							)
					SCREAMING_SNAKE_CASE:Dict   =   "I am VinAI Research"
					SCREAMING_SNAKE_CASE:str   =   "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
					SCREAMING_SNAKE_CASE:int   =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
					self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:List[str]   =   tokens + [tokenizer.unk_token]

					SCREAMING_SNAKE_CASE:List[Any]   =   [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)       ,SCREAMING_SNAKE_CASE__							)









'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

A_      =							["UtteranceLevel"]





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(snake_case ,    map_location="cpu"	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

  if hf_congfig.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Featurizer"]["weights"]

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(snake_case	)


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






