

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 							A_			(		snake_case__							,     unittest.TestCase ):
     _lowercase      :					Any       =     HerbertTokenizer
     _lowercase      :					List[str]       =     HerbertTokenizerFast
     _lowercase      :					List[str]       =     True


     def   UpperCAmelCase  (							self      :		Tuple )							->     List[Any]:
      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:
       __lowerCAmelCase:       str            =				f_data.read().replace('\n\n'      ,  '\n' ).strip()

      __lowerCAmelCase:       Optional[int]            =				[
          '<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>',
      ]
      __lowerCAmelCase:       Optional[Any]            =				dict(zip(UpperCAmelCase      ,  range(len(UpperCAmelCase ) ) ) )
      __lowerCAmelCase:       Union[str, Any]            =				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

      __lowerCAmelCase:       Optional[Any]            =				os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file'] )
      __lowerCAmelCase:       List[Any]            =				os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['merges_file'] )
      with open(self.vocab_file      ,  'w' ) as fp:
       fp.write(json.dumps(UpperCAmelCase ) )
      with open(self.merges_file      ,  'w' ) as fp:
       fp.write('\n'.join(UpperCAmelCase ) )


     def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		Dict )							->     int:
      __lowerCAmelCase:       List[str]            =				'lower newer'
      __lowerCAmelCase:       int            =				'lower newer'
      return input_text, output_text


     def   UpperCAmelCase  (							self      :		Tuple )							->     List[str]:
      __lowerCAmelCase:       Dict            =				self.tokenizer_class(vocab_file=self.vocab_file      ,  merges_file=self.merges_file )

      __lowerCAmelCase:       Optional[int]            =				'lower'
      __lowerCAmelCase:       Any            =				['low', 'er</w>']
      __lowerCAmelCase:       Any            =				tokenizer.tokenize(UpperCAmelCase )
      self.assertListEqual(UpperCAmelCase      ,  UpperCAmelCase )

      __lowerCAmelCase:       List[str]            =				tokens + ['<unk>']
      __lowerCAmelCase:       int            =				[1_6, 1_7, 2_3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase )      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		int )							->     Tuple:
      if not self.test_rust_tokenizer:
       return

      __lowerCAmelCase:       Tuple            =				self.get_tokenizer()
      __lowerCAmelCase:       Tuple            =				self.get_rust_tokenizer()

      __lowerCAmelCase:       Dict            =				'lower,newer'

      __lowerCAmelCase:       Tuple            =				tokenizer.tokenize(UpperCAmelCase )
      __lowerCAmelCase:       Optional[Any]            =				rust_tokenizer.tokenize(UpperCAmelCase )
      self.assertListEqual(UpperCAmelCase      ,  UpperCAmelCase )

      __lowerCAmelCase:       str            =				tokenizer.encode(UpperCAmelCase      ,  add_special_tokens=UpperCAmelCase )
      __lowerCAmelCase:       Any            =				rust_tokenizer.encode(UpperCAmelCase      ,  add_special_tokens=UpperCAmelCase )
      self.assertListEqual(UpperCAmelCase      ,  UpperCAmelCase )

      __lowerCAmelCase:       str            =				self.get_rust_tokenizer()
      __lowerCAmelCase:       Tuple            =				tokenizer.encode(UpperCAmelCase )
      __lowerCAmelCase:       Dict            =				rust_tokenizer.encode(UpperCAmelCase )
      self.assertListEqual(UpperCAmelCase      ,  UpperCAmelCase )


     @slow
     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     Union[str, Any]:
      __lowerCAmelCase:       Any            =				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

      __lowerCAmelCase:       Any            =				tokenizer.encode('konstruowanie sekwencji'      ,  add_special_tokens=UpperCAmelCase )
      __lowerCAmelCase:       Any            =				tokenizer.encode('konstruowanie wielu sekwencji'      ,  add_special_tokens=UpperCAmelCase )

      __lowerCAmelCase:       Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase )
      __lowerCAmelCase:       Union[str, Any]            =				tokenizer.build_inputs_with_special_tokens(UpperCAmelCase      ,  UpperCAmelCase )

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








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			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				torch.load(SCREAMING_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}'''		)

 __lowerCAmelCase:       str            =				checkpoint['Downstream']

 __lowerCAmelCase:       List[str]            =				HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Any            =				HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   , return_attention_mask=SCREAMING_SNAKE_CASE   , do_normalize=SCREAMING_SNAKE_CASE		)

 if hf_congfig.use_weighted_layer_sum:
  __lowerCAmelCase:       Dict            =				checkpoint['Featurizer']['weights']

 __lowerCAmelCase:       str            =				downstream_dict['projector.weight']
 __lowerCAmelCase:       Any            =				downstream_dict['projector.bias']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.weight']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE		)
 hf_model.save_pretrained(SCREAMING_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)





