






"""simple docstring"""





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 					a						( unittest.TestCase		):






    def        UpperCamelCase	(	self					:							Optional[Any] )					->		Union[str, Any]:
      lowerCamelCase_   					=						'<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(' ' )
      lowerCamelCase_   					=						dict(zip(__SCREAMING_SNAKE_CASE	,						range(len(__SCREAMING_SNAKE_CASE ) ) ) )

      lowerCamelCase_   					=						{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      lowerCamelCase_   					=						{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 16000,
          'return_attention_mask': False,
          'do_normalize': True,
      }

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

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






    def        UpperCamelCase	(	self					:							List[str]	,						**__SCREAMING_SNAKE_CASE					:							Dict )					->		Any:
      lowerCamelCase_   					=						self.add_kwargs_tokens_map.copy()
      kwargs.update(__SCREAMING_SNAKE_CASE )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Optional[Any]	,						**__SCREAMING_SNAKE_CASE					:							str )					->		Union[str, Any]:
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,						**__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							Tuple )					->		List[str]:
      shutil.rmtree(self.tmpdirname )






    def        UpperCamelCase	(	self					:							Dict )					->		Any:
      lowerCamelCase_   					=						self.get_tokenizer()
      lowerCamelCase_   					=						self.get_feature_extractor()

      lowerCamelCase_   					=						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,						feature_extractor=__SCREAMING_SNAKE_CASE )

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

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

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






    def        UpperCamelCase	(	self					:							Optional[Any] )					->		str:
      lowerCamelCase_   					=						WavaVecaProcessor(tokenizer=self.get_tokenizer()	,						feature_extractor=self.get_feature_extractor() )
      processor.save_pretrained(self.tmpdirname )

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

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

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

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






    def        UpperCamelCase	(	self					:							List[Any] )					->		str:
      lowerCamelCase_   					=						self.get_feature_extractor()
      lowerCamelCase_   					=						self.get_tokenizer()

      lowerCamelCase_   					=						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,						feature_extractor=__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						floats_list((3, 1000) )

      lowerCamelCase_   					=						feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='np' )
      lowerCamelCase_   					=						processor(__SCREAMING_SNAKE_CASE	,						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        UpperCamelCase	(	self					:							str )					->		Optional[int]:
      lowerCamelCase_   					=						self.get_feature_extractor()
      lowerCamelCase_   					=						self.get_tokenizer()

      lowerCamelCase_   					=						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,						feature_extractor=__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						'This is a test string'

      lowerCamelCase_   					=						processor(text=__SCREAMING_SNAKE_CASE )

      lowerCamelCase_   					=						tokenizer(__SCREAMING_SNAKE_CASE )

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






    def        UpperCamelCase	(	self					:							List[Any] )					->		Union[str, Any]:
      lowerCamelCase_   					=						self.get_feature_extractor()
      lowerCamelCase_   					=						self.get_tokenizer()

      lowerCamelCase_   					=						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,						feature_extractor=__SCREAMING_SNAKE_CASE )

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

      lowerCamelCase_   					=						processor.batch_decode(__SCREAMING_SNAKE_CASE )
      lowerCamelCase_   					=						tokenizer.batch_decode(__SCREAMING_SNAKE_CASE )

      self.assertListEqual(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE )






    def        UpperCamelCase	(	self					:							str )					->		int:
      lowerCamelCase_   					=						self.get_feature_extractor()
      lowerCamelCase_   					=						self.get_tokenizer()

      lowerCamelCase_   					=						WavaVecaProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,						feature_extractor=__SCREAMING_SNAKE_CASE )

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












"""simple docstring"""





import numpy as np







def 					lowerCamelCase__	(							_lowerCamelCase	:		Dict		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Tuple			)				->    str:
  lowerCamelCase_   					=						n_draws * class_counts / class_counts.sum()
  # floored means we don't overshoot n_samples, but probably undershoot
  lowerCamelCase_   					=						np.floor(_lowerCamelCase			)
  # we add samples according to how much "left over" probability
  # they had, until we arrive at n_samples
  lowerCamelCase_   					=						int(n_draws - floored.sum()			)
  if need_to_add > 0:
    lowerCamelCase_   					=						continuous - floored
    lowerCamelCase_   					=						np.sort(np.unique(_lowerCamelCase			)			)[::-1]
    # add according to remainder, but break ties
    # randomly to avoid biases
    for value in values:
      ((lowerCamelCase_)					,     )   					=						np.where(remainder == value			)
      # if we need_to_add less than what's in inds
      # we draw randomly from them.
      # if we need to add more, we add them all and
      # go to the next value
      lowerCamelCase_   					=						min(len(_lowerCamelCase			)		,    _lowerCamelCase			)
      lowerCamelCase_   					=						rng.choice(_lowerCamelCase		,    size=_lowerCamelCase		,    replace=_lowerCamelCase			)
      floored[inds] += 1
      need_to_add -= add_now
      if need_to_add == 0:
        break
  return floored.astype(np.intaa			)







def 					lowerCamelCase__	(							_lowerCamelCase	:		Any		,    _lowerCamelCase	:		int		,    _lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		List[str]		,    _lowerCamelCase	:		Optional[int]=10			)				->    Any:
  lowerCamelCase_					,     lowerCamelCase_   					=						np.unique(_lowerCamelCase		,    return_inverse=_lowerCamelCase			)
  lowerCamelCase_   					=						classes.shape[0]
  lowerCamelCase_   					=						np.bincount(_lowerCamelCase			)
  if np.min(_lowerCamelCase			) < 2:
    raise ValueError('Minimum class count error'			)
  if n_train < n_classes:
    raise ValueError(
        'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)			)
  if n_test < n_classes:
    raise ValueError(
        'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)			)
  lowerCamelCase_   					=						np.split(np.argsort(_lowerCamelCase		,    kind='mergesort'			)		,    np.cumsum(_lowerCamelCase			)[:-1]			)
  for _ in range(_lowerCamelCase			):
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)
    lowerCamelCase_   					=						class_counts - n_i
    lowerCamelCase_   					=						approximate_mode(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase			)

    lowerCamelCase_   					=						[]
    lowerCamelCase_   					=						[]

    for i in range(_lowerCamelCase			):
      lowerCamelCase_   					=						rng.permutation(class_counts[i]			)
      lowerCamelCase_   					=						class_indices[i].take(_lowerCamelCase		,    mode='clip'			)
      train.extend(perm_indices_class_i[: n_i[i]]			)
      test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)
    lowerCamelCase_   					=						rng.permutation(_lowerCamelCase			)

    yield train, test




