

'''simple docstring'''

import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							_snake_case	(    _a		,					unittest.TestCase  ):
			_A    :       Dict      				=   HerbertTokenizer
			_A    :       Dict      				=   HerbertTokenizerFast
			_A    :       Optional[int]      				=   True
			def       __UpperCamelCase  (	self     : Tuple							):
					super().setUp()

					# Use a simpler test file without japanese/chinese characters
					with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''       ,encoding="utf-8"							) as f_data:
							SCREAMING_SNAKE_CASE:List[str]   =   f_data.read().replace("\n\n"       ,"\n"							).strip()

					SCREAMING_SNAKE_CASE:Any   =   [
					    "<s>",
					    "</s>",
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "w</w>",
					    "r</w>",
					    "t</w>",
					    "lo",
					    "low",
					    "er</w>",
					    "low</w>",
					    "lowest</w>",
					    "newer</w>",
					    "wider</w>",
					    ",</w>",
					    "<unk>",
					]
					SCREAMING_SNAKE_CASE:List[str]   =   dict(zip(SCREAMING_SNAKE_CASE__       ,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
					SCREAMING_SNAKE_CASE:Any   =   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

					SCREAMING_SNAKE_CASE:List[str]   =   os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES["vocab_file"]							)
					SCREAMING_SNAKE_CASE:Dict   =   os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES["merges_file"]							)
					with open(self.vocab_file       ,"w"							) as fp:
							fp.write(json.dumps(SCREAMING_SNAKE_CASE__							)							)
					with open(self.merges_file       ,"w"							) as fp:
							fp.write("\n".join(SCREAMING_SNAKE_CASE__							)							)
			def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : Optional[Any]							):
					SCREAMING_SNAKE_CASE:Optional[Any]   =   "lower newer"
					SCREAMING_SNAKE_CASE:Tuple   =   "lower newer"
					return input_text, output_text
			def       __UpperCamelCase  (	self     : Optional[int]							):
					SCREAMING_SNAKE_CASE:List[str]   =   self.tokenizer_class(vocab_file=self.vocab_file       ,merges_file=self.merges_file							)

					SCREAMING_SNAKE_CASE:Tuple   =   "lower"
					SCREAMING_SNAKE_CASE:Tuple   =   ["low", "er</w>"]
					SCREAMING_SNAKE_CASE:str   =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
					self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:Dict   =   tokens + ["<unk>"]
					SCREAMING_SNAKE_CASE:Tuple   =   [16, 17, 23]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__							)       ,SCREAMING_SNAKE_CASE__							)
			def       __UpperCamelCase  (	self     : Dict							):



					if not self.test_rust_tokenizer:
							return

					SCREAMING_SNAKE_CASE:Optional[int]   =   self.get_tokenizer()
					SCREAMING_SNAKE_CASE:int   =   self.get_rust_tokenizer()

					SCREAMING_SNAKE_CASE:int   =   "lower,newer"

					SCREAMING_SNAKE_CASE:Optional[Any]   =   tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Dict   =   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
					self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:List[Any]   =   tokenizer.encode(SCREAMING_SNAKE_CASE__       ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:int   =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE__       ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
					self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:Dict   =   self.get_rust_tokenizer()
					SCREAMING_SNAKE_CASE:int   =   tokenizer.encode(SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Tuple   =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE__							)
					self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)







			@slow
			def       __UpperCamelCase  (	self     : List[str]							):
					SCREAMING_SNAKE_CASE:Optional[Any]   =   self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"							)

					SCREAMING_SNAKE_CASE:Any   =   tokenizer.encode("konstruowanie sekwencji"       ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:Any   =   tokenizer.encode("konstruowanie wielu sekwencji"       ,add_special_tokens=SCREAMING_SNAKE_CASE__							)

					SCREAMING_SNAKE_CASE:Dict   =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__							)
					SCREAMING_SNAKE_CASE:List[Any]   =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)

					assert encoded_sentence == [0] + text + [2]
					assert encoded_pair == [0] + text + [2] + text_a + [2]









'''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)






