





import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       __magic_name__				=       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''						)
       __magic_name__				=       dict(zip(A	,  range(len(A						)						)						)						)

       __magic_name__				=       {
           '''pad_token''': '''<pad>''',
           '''unk_token''': '''<unk>''',
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
       }
       __magic_name__				=       {
           '''feature_size''': 1,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_60_00,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }

       __magic_name__				=       tempfile.mkdtemp()
       __magic_name__				=       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file''']						)
       __magic_name__				=       os.path.join(self.tmpdirname	,  A						)
       with open(self.vocab_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)

       with open(self.feature_extraction_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)





  def 			__A      (  self	,  **A						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.add_kwargs_tokens_map.copy()
       kwargs.update(A						)
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self	,  **A						)		->   List[Any]:





       '''simple docstring'''







       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       shutil.rmtree(self.tmpdirname						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_tokenizer()
       __magic_name__				=       self.get_feature_extractor()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       processor.save_pretrained(self.tmpdirname						)
       __magic_name__				=       WavaVecaProcessor.from_pretrained(self.tmpdirname						)

       self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer.get_vocab()						)
       self.assertIsInstance(processor.tokenizer	,  A						)

       self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor.to_json_string()						)
       self.assertIsInstance(processor.feature_extractor	,  A						)





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       WavaVecaProcessor(tokenizer=self.get_tokenizer()	,  feature_extractor=self.get_feature_extractor()						)
       processor.save_pretrained(self.tmpdirname						)

       __magic_name__				=       self.get_tokenizer(bos_token='''(BOS)'''	,  eos_token='''(EOS)'''						)
       __magic_name__				=       self.get_feature_extractor(do_normalize=A	,  padding_value=1.0						)

       __magic_name__				=       WavaVecaProcessor.from_pretrained(
           self.tmpdirname	,  bos_token='''(BOS)'''	,  eos_token='''(EOS)'''	,  do_normalize=A	,  padding_value=1.0						)

       self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()						)
       self.assertIsInstance(processor.tokenizer	,  A						)

       self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor_add_kwargs.to_json_string()						)
       self.assertIsInstance(processor.feature_extractor	,  A						)





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       floats_list((3, 10_00)						)

       __magic_name__				=       feature_extractor(A	,  return_tensors='''np'''						)
       __magic_name__				=       processor(A	,  return_tensors='''np'''						)

       for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1E-2						)





  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       '''This is a test string'''

       __magic_name__				=       processor(text=A						)

       __magic_name__				=       tokenizer(A						)

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]						)





  def 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       __magic_name__				=       processor.batch_decode(A						)
       __magic_name__				=       tokenizer.batch_decode(A						)

       self.assertListEqual(A	,  A						)







  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.get_feature_extractor()
       __magic_name__				=       self.get_tokenizer()

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       self.assertListEqual(
           processor.model_input_names	,  feature_extractor.model_input_names	,  msg='''`processor` and `feature_extractor` model input names do not match'''	,  )






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     __magic_name__				=       args.serialization_dir
     __magic_name__				=       args.pruning_method
     __magic_name__				=       args.threshold

     __magic_name__				=       torch.load(os.path.join(snake_case_ ,     '''pytorch_model.bin'''						) ,     map_location='''cpu'''						)

     __magic_name__				=       0  # Number of remaining (not pruned) params in the encoder
     __magic_name__				=       0  # Number of params in the encoder

     print('''name'''.ljust(60 ,     ''' '''						) ,     '''Remaining Weights %''' ,     '''Remaining Weight'''						)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    __magic_name__				=       TopKBinarizer.apply(snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __magic_name__				=       ThresholdBinarizer.apply(snake_case_ ,     snake_case_ ,     snake_case_						).sum().item()
               elif pruning_method == "l0":
                    __magic_name__		,  __magic_name__				=       -0.1, 1.1
                    __magic_name__				=       torch.sigmoid(snake_case_						)
                    __magic_name__				=       s * (r - l) + l
                    __magic_name__				=       s_bar.clamp(min=0.0 ,     max=1.0						)
                    __magic_name__				=       (mask > 0.0).sum().item()
               else:
                    raise ValueError('''Unknown pruning method'''						)
               remaining_count += mask_ones
               print(name.ljust(60 ,     ''' '''						) ,     str(round(100 * mask_ones / param.numel() ,     3						)						).ljust(20 ,     ''' '''						) ,     str(snake_case_						)						)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(''''''						)
     print('''Remaining Weights (global) %: ''' ,     100 * remaining_count / encoder_count						)


if __name__ == "__main__":
    a_    :				Optional[Any]					=			argparse.ArgumentParser()

    parser.add_argument(
        '--pruning_method',
        choices=['l0', 'topK', 'sigmoied_threshold'],
        type=str,
        required=True,
        help=(
            'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
            ' pruning)'
        ),
    )
    parser.add_argument(
        '--threshold',
        type=float,
        required=False,
        help=(
            'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
            'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
            'Not needed for `l0`'
        ),
    )
    parser.add_argument(
        '--serialization_dir',
        type=str,
        required=True,
        help='Folder containing the model that was previously fine-pruned',
    )

    a_    :				List[Any]					=			parser.parse_args()

    main(args)