






"""simple docstring"""




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: Optional[int]    =			copy.deepcopy(UpperCamelCase__       )
  _a						: Dict    =			sorted(UpperCamelCase__       )
  _a						: Dict    =			[bisect.bisect_right(UpperCamelCase__  ,     UpperCamelCase__       ) for y in x]
  return quantized

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=0       ):



  '''simple docstring'''
  _a						: Tuple    =			np.arange(start=3  ,     stop=UpperCamelCase__  ,     step=4       ).tolist() if k > 0 else [1_0]
  _a						: List[str]    =			_quantize(UpperCamelCase__  ,     UpperCamelCase__       )
  # count number of elements per group
  _a						: List[Any]    =			np.unique(UpperCamelCase__  ,     return_counts=UpperCamelCase__       )[1]
  _a						: List[Any]    =			[0] + bins + [np.inf]
  logger.info("""Using {} as bins for aspect lengths quantization""".format(UpperCamelCase__       )       )
  logger.info("""Count of instances per bin: {}""".format(UpperCamelCase__       )       )
  return groups






class  UpperCamelCase    (			snake_case_   ):


 def __init__(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		Optional[Any]							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   if not isinstance(UpperCAmelCase__							,		UpperCAmelCase__							):
     raise ValueError(
         """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(UpperCAmelCase__							)							)
   _a						: Dict    =			sampler
   _a						: Optional[Any]    =			group_ids
   _a						: Optional[int]    =			batch_size


 def __iter__(  self	:		Any							)      -> str:
   _a						: str    =			defaultdict(UpperCAmelCase__							)
   _a						: str    =			defaultdict(UpperCAmelCase__							)

   _a						: List[str]    =			0
   for idx in self.sampler:
     _a						: int    =			self.group_ids[idx]
     buffer_per_group[group_id].append(UpperCAmelCase__							)
     samples_per_group[group_id].append(UpperCAmelCase__							)
     if len(buffer_per_group[group_id]							) == self.batch_size:
       yield buffer_per_group[group_id]  # TODO
       num_batches += 1
       del buffer_per_group[group_id]
     assert len(buffer_per_group[group_id]							) < self.batch_size

   # now we have run out of elements that satisfy
   # the group criteria, let's return the remaining
   # elements so that the size of the sampler is
   # deterministic
   _a						: List[str]    =			len(self							)
   _a						: Dict    =			expected_num_batches - num_batches
   if num_remaining > 0:
     # for the remaining batches, group the batches by similar lengths
     _a						: Any    =			[]
     for group_id, idxs in sorted(buffer_per_group.items()							,		key=lambda UpperCAmelCase__							: x[0]							):
       batch_idx.extend(UpperCAmelCase__							)
       if len(UpperCAmelCase__							) >= self.batch_size:
         yield batch_idx[: self.batch_size]
         _a						: Optional[int]    =			batch_idx[self.batch_size :]
         num_remaining -= 1
     if len(UpperCAmelCase__							) > 0:
       yield batch_idx
       num_remaining -= 1
   assert num_remaining == 0







 def __len__(  self	:		str							)      -> int:
   return (len(self.sampler							) + self.batch_size - 1) // self.batch_size









"""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()

