






import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


_snake_case					   =     logging.get_logger(__name__)






class  UpperCamelCase    (			__lowerCamelCase   ):


 def __init__(  self	:		Any							,		*UpperCAmelCase__	:		Optional[Any]							,		**UpperCAmelCase__	:		List[Any]							)      -> Optional[int]:
   warnings.warn(
       """The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use ViltImageProcessor instead."""							,		_lowerCAmelCase							,		)
   super().__init__(*_lowerCAmelCase							,		**_lowerCAmelCase							)









"""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


_snake_case					   =     1000_0000_0000
_snake_case					   =     1_0000

_snake_case   ,				_snake_case					   =     os.path.split(__file__)
_snake_case					   =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Union[str, Any]    =			pa.Table.from_pydict({"""col""": [0] * chunk_size}       )
  _a						: Dict    =			pa.concat_tables([table] * (num_examples // chunk_size)       )
  return datasets.Dataset(UpperCamelCase__  ,     fingerprint="""table_100B"""       )






@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : int
 UpperCamelCase      : int
 UpperCamelCase      : int
 UpperCamelCase      : int


 def _lowercase			(  self	:		Optional[int]							)      -> Dict:
   _a						: Tuple    =			np.random.default_rng(self.seed							)
   _a						: Dict    =			rng.integers(low=self.low							,		high=self.high							,		size=self.size							).tolist()


 def __iter__(  self	:		List[Any]							)      -> Optional[Any]:
   return iter(self._sampled_values							)







 def __len__(  self	:		Dict							)      -> Optional[Any]:
   return self.size







@get_duration
def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Union[str, Any]    =			dataset[0]

@get_duration
def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Optional[Any]    =			dataset[-1]

@get_duration
def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: str    =			dataset[range(len(UpperCamelCase__       ) // 2  ,     len(UpperCamelCase__       ) // 2 + 1_0_2_4       )]

@get_duration
def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Tuple    =			dataset[RandIter(0  ,     len(UpperCamelCase__       )  ,     1_0_2_4  ,     seed=4_2       )]

def     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: str    =			{"""num examples""": SPEED_TEST_N_EXAMPLES}
  _a						: Dict    =			(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
  print("""generating dataset"""       )
  _a						: List[str]    =			generate_100B_dataset(num_examples=UpperCamelCase__  ,     chunk_size=UpperCamelCase__       )
  print("""Functions"""       )
  for func in functions:
    print(func.__name__       )
    _a						: Optional[Any]    =			func(UpperCamelCase__       )

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


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

