






"""simple docstring"""



import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_UpperCamelCase						:    int		=						1_0_0_0_0_0_0_0_0_0_0_0
_UpperCamelCase						:    Union[str, Any]		=						1_0_0_0_0

_UpperCamelCase,						_UpperCamelCase						:    Tuple		=						os.path.split(__file__)
_UpperCamelCase						:    List[Any]		=						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    int       ):





  '''simple docstring'''





  lowercase				       =			pa.Table.from_pydict({'col': [0] * chunk_size}       )
  lowercase				       =			pa.concat_tables([table] * (num_examples // chunk_size)       )
  return datasets.Dataset(__snake_case						,   fingerprint='table_100B'       )

@dataclass
class 			a	:
    UpperCAmelCase_					:	int
    UpperCAmelCase_					:	int
    UpperCAmelCase_					:	int
    UpperCAmelCase_					:	int







    def      UpperCamelCase_ (     self			):
      lowercase				       =			np.random.default_rng(self.seed			)
      lowercase				       =			rng.integers(low=self.low				,						high=self.high				,						size=self.size			).tolist()







    def __iter__(     self			):
      return iter(self._sampled_values			)







    def __len__(     self			):
      return self.size


@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[0]
@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[-1]
@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[range(len(__snake_case       ) // 2						,   len(__snake_case       ) // 2 + 10_24       )]
@get_duration
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    datasets.Dataset       ):





  '''simple docstring'''





  lowercase				       =			dataset[RandIter(0						,   len(__snake_case       )						,   10_24						,   seed=42       )]
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  lowercase				       =			{'num examples': SPEED_TEST_N_EXAMPLES}
  lowercase				       =			(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
  print('generating dataset'       )
  lowercase				       =			generate_100B_dataset(num_examples=__snake_case						,   chunk_size=__snake_case       )
  print('Functions'       )
  for func in functions:
    print(func.__name__       )
    lowercase				       =			func(__snake_case       )

  with open(__snake_case						,   'wb'       ) as f:
    f.write(json.dumps(__snake_case       ).encode('utf-8'       )       )


if __name__ == "__main__":  # useful to run the profiler
    benchmark_table_100B()










"""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			):
      lowercase				       =			'<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(' '			)
      lowercase				       =			dict(zip(_lowerCamelCase				,						range(len(_lowerCamelCase			)			)			)			)

      lowercase				       =			{
          'pad_token': '<pad>',
          'unk_token': '<unk>',
          'bos_token': '<s>',
          'eos_token': '</s>',
      }
      lowercase				       =			{
          'feature_size': 1,
          'padding_value': 0.0,
          'sampling_rate': 1_6_0_0_0,
          'return_attention_mask': False,
          'do_normalize': True,
      }

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

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







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      lowercase				       =			self.add_kwargs_tokens_map.copy()
      kwargs.update(_lowerCamelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_feature_extractor()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

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

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

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			WavaVecaProcessor(tokenizer=self.get_tokenizer()				,						feature_extractor=self.get_feature_extractor()			)
      processor.save_pretrained(self.tmpdirname			)

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

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

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

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			floats_list((3, 1_0_0_0)			)

      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(_lowerCamelCase				,						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			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

      lowercase				       =			'This is a test string'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

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

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_feature_extractor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			WavaVecaProcessor(tokenizer=_lowerCamelCase				,						feature_extractor=_lowerCamelCase			)

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


