




'''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


a		:	Optional[int]       =      1_0_0_0_0_0_0_0_0_0_0_0
a		:	Optional[Any]       =      1_0_0_0_0

a		:	Union[str, Any]       =      os.path.split(__file__)
a		:	List[Any]       =      os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    _a				:							List[str]							       =      pa.Table.from_pydict({"""col""": [0] * chunk_size}						)
    _a				:							List[str]							       =      pa.concat_tables([table] * (num_examples // chunk_size)						)
    return datasets.Dataset(snake_case_   ,	fingerprint="""table_100B"""						)




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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      np.random.default_rng(self.seed )
      _a				:							str							       =      rng.integers(low=self.low		,     high=self.high		,     size=self.size ).tolist()



  def __iter__(			self ):







      '''simple docstring'''


      return iter(self._sampled_values )



  def __len__(			self ):







      '''simple docstring'''


      return self.size






@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Union[str, Any]							       =      dataset[0]





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      dataset[-1]





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Union[str, Any]							       =      dataset[range(len(snake_case_						) // 2   ,	len(snake_case_						) // 2 + 1_024						)]





@get_duration
def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Tuple							       =      dataset[RandIter(0   ,	len(snake_case_						)   ,	1_024   ,	seed=42						)]





def UpperCAmelCase    (       ):
    _a				:							Dict							       =      {"num examples": SPEED_TEST_N_EXAMPLES}
    _a				:							Union[str, Any]							       =      (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
    print("""generating dataset"""						)
    _a				:							Any							       =      generate_100B_dataset(num_examples=snake_case_   ,	chunk_size=snake_case_						)
    print("""Functions"""						)
    for func in functions:
        print(func.__name__						)
        _a				:							List[str]							       =      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()




import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
