





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_     )   ->						int:
       UpperCamelCase_      				=     copy.deepcopy(UpperCamelCase_     )
       UpperCamelCase_      				=     sorted(UpperCamelCase_     )
       UpperCamelCase_      				=     [bisect.bisect_right(UpperCamelCase_    ,						UpperCamelCase_     ) for y in x]
       return quantized




def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_=0     )   ->						Optional[Any]:
       UpperCamelCase_      				=     np.arange(start=3    ,						stop=UpperCamelCase_    ,						step=4     ).tolist() if k > 0 else [10]
       UpperCamelCase_      				=     _quantize(UpperCamelCase_    ,						UpperCamelCase_     )
       # count number of elements per group
       UpperCamelCase_      				=     np.unique(UpperCamelCase_    ,						return_counts=UpperCamelCase_     )[1]
       UpperCamelCase_      				=     [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    (		lowerCAmelCase_   ):

       def __init__(						self:       Any    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Tuple						)				->       Dict:




              """simple docstring"""


              if not isinstance(__lowercase    ,      __lowercase						):
                     raise ValueError(
                         "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__lowercase						)						)
              UpperCamelCase_      				=     sampler
              UpperCamelCase_      				=     group_ids
              UpperCamelCase_      				=     batch_size

       def __iter__(						self:       Tuple						)				->       int:




              """simple docstring"""


              UpperCamelCase_      				=     defaultdict(__lowercase						)
              UpperCamelCase_      				=     defaultdict(__lowercase						)

              UpperCamelCase_      				=     0
              for idx in self.sampler:
                     UpperCamelCase_      				=     self.group_ids[idx]
                     buffer_per_group[group_id].append(__lowercase						)
                     samples_per_group[group_id].append(__lowercase						)
                     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
              UpperCamelCase_      				=     len(self						)
              UpperCamelCase_      				=     expected_num_batches - num_batches
              if num_remaining > 0:
                     # for the remaining batches, group the batches by similar lengths
                     UpperCamelCase_      				=     []
                     for group_id, idxs in sorted(buffer_per_group.items()    ,      key=lambda _SCREAMING_SNAKE_CASE						: x[0]						):
                            batch_idx.extend(__lowercase						)
                            if len(__lowercase						) >= self.batch_size:
                                   yield batch_idx[: self.batch_size]
                                   UpperCamelCase_      				=     batch_idx[self.batch_size :]
                                   num_remaining -= 1
                     if len(__lowercase						) > 0:
                            yield batch_idx
                            num_remaining -= 1
              assert num_remaining == 0



       def __len__(						self:       Union[str, Any]						)				->       Dict:




              """simple docstring"""


              return (len(self.sampler						) + self.batch_size - 1) // self.batch_size







import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

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






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							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 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

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


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