


import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowerCamelCase						 =	(30, 4_87)
lowerCamelCase						 =	(36, 10_24)
lowerCamelCase						 =	(1_00, 1_00)
lowerCamelCase						 =	1_00

lowerCamelCase						 =	datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

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




@get_duration
def      a_				(   SCREAMING_SNAKE_CASE__ :				Union[str, Any]		,      SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				str					):
       '''simple docstring'''





       with ArrowWriter(features=SCREAMING_SNAKE_CASE__		,      path=os.path.join(SCREAMING_SNAKE_CASE__		,      'beta.arrow'					)					) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase	:		Dict          =my_features.encode_example(SCREAMING_SNAKE_CASE__					)
                     writer.write(SCREAMING_SNAKE_CASE__					)
              _lowerCamelCase     ,					_lowerCamelCase	:		Any          =writer.finalize()




@get_duration
def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =datasets.Dataset.from_file(
           filename=os.path.join(SCREAMING_SNAKE_CASE__		,      'beta.arrow'					)		,      info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__					)					)
       for _ in dataset:
              pass




@get_duration
def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Tuple					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =datasets.Dataset.from_file(
           filename=os.path.join(SCREAMING_SNAKE_CASE__		,      'beta.arrow'					)		,      info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__					)					)
       dataset.set_format('numpy'					)
       for _ in dataset:
              pass




@get_duration
def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				str					):
       '''simple docstring'''





       _lowerCamelCase	:		Dict          =10
       _lowerCamelCase	:		Tuple          =datasets.Dataset.from_file(
           filename=os.path.join(SCREAMING_SNAKE_CASE__		,      'beta.arrow'					)		,      info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__					)					)
       for i in range(0		,      len(SCREAMING_SNAKE_CASE__					)		,      SCREAMING_SNAKE_CASE__					):
              _lowerCamelCase	:		int          =dataset[i : i + batch_size]




@get_duration
def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				Any					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =10
       _lowerCamelCase	:		Dict          =datasets.Dataset.from_file(
           filename=os.path.join(SCREAMING_SNAKE_CASE__		,      'beta.arrow'					)		,      info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__					)					)
       dataset.set_format('numpy'					)
       for i in range(0		,      len(SCREAMING_SNAKE_CASE__					)		,      SCREAMING_SNAKE_CASE__					):
              _lowerCamelCase	:		Tuple          =dataset[i : i + batch_size]




@get_duration
def      a_				(   SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Any          =datasets.Dataset.from_file(
           filename=os.path.join(SCREAMING_SNAKE_CASE__		,      'beta.arrow'					)		,      info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__					)					)
       for col in feats:
              _lowerCamelCase	:		List[str]          =dataset[col]




@get_duration
def      a_				(   SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				List[str]					):
       '''simple docstring'''





       _lowerCamelCase	:		str          =datasets.Dataset.from_file(
           filename=os.path.join(SCREAMING_SNAKE_CASE__		,      'beta.arrow'					)		,      info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__					)					)
       dataset.set_format('numpy'					)
       for col in feats:
              _lowerCamelCase	:		Optional[Any]          =dataset[col]




def      a_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		Tuple          ={}
       _lowerCamelCase	:		Optional[Any]          =(
           read_unformated,
           read_formatted_as_numpy,
           read_batch_unformated,
           read_batch_formatted_as_numpy,
           read_col_unformated,
           read_col_formatted_as_numpy,
       )
       with tempfile.TemporaryDirectory() as tmp_dir:
              _lowerCamelCase	:		Union[str, Any]          =datasets.Features({'image': ArrayaD(SCREAMING_SNAKE_CASE__		,      dtype='float32'					)}					)
              _lowerCamelCase	:		List[Any]          =generate_examples(features=SCREAMING_SNAKE_CASE__		,      num_examples=SCREAMING_SNAKE_CASE__					)
              _lowerCamelCase	:		Optional[Any]          =write(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
              for read_func in read_functions:
                     _lowerCamelCase	:		List[Any]          =read_func(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
              # )
              _lowerCamelCase	:		Dict          =datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'					)					)					)}					)
              _lowerCamelCase	:		Tuple          =generate_examples(
                  features=SCREAMING_SNAKE_CASE__		,      num_examples=SCREAMING_SNAKE_CASE__		,      seq_shapes={'image': SPEED_TEST_SHAPE}					)
              _lowerCamelCase	:		Union[str, Any]          =write(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
              for read_func in read_functions:
                     _lowerCamelCase	:		int          =read_func(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
              # )
              _lowerCamelCase	:		int          =datasets.Features({'image': datasets.Sequence(datasets.Value('float32'					)					)}					)
              _lowerCamelCase	:		Any          =generate_examples(
                  features=SCREAMING_SNAKE_CASE__		,      num_examples=SCREAMING_SNAKE_CASE__		,      seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,      )
              _lowerCamelCase	:		Any          =write(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
              for read_func in read_functions:
                     _lowerCamelCase	:		Tuple          =read_func(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

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


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






import bisect
import copy
from collections import defaultdict

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

from utils import logger




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =copy.deepcopy(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Union[str, Any]          =sorted(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[Any]          =[bisect.bisect_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for y in x]
       return quantized




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =np.arange(start=3		,      stop=SCREAMING_SNAKE_CASE__		,      step=4					).tolist() if k > 0 else [10]
       _lowerCamelCase	:		List[Any]          =_quantize(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       # count number of elements per group
       _lowerCamelCase	:		Union[str, Any]          =np.unique(SCREAMING_SNAKE_CASE__		,      return_counts=SCREAMING_SNAKE_CASE__					)[1]
       _lowerCamelCase	:		int          =[0] + bins + [np.inf]
       logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE__					)					)
       logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE__					)					)
       return groups




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =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
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    for group_id, idxs in sorted(buffer_per_group.items()      ,       key=lambda lowercase_			: x[0]			):
                           batch_idx.extend(lowercase_			)
                           if len(lowercase_			) >= self.batch_size:
                                  yield batch_idx[: self.batch_size]
                                  _lowerCamelCase	:		str          =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						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




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


