






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


__A          = (30, 487)
__A          = (36, 1024)
__A          = (100, 100)
__A          = 100

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

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




@get_duration
def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







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




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for _ in dataset:
										pass




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					dataset.set_format('''numpy'''       )
					for _ in dataset:
										pass




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







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




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







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




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       datasets.Dataset.from_file(
					    filename=os.path.join(_lowercase    ,	'''beta.arrow'''       )    ,	info=datasets.DatasetInfo(features=_lowercase       )       )
					for col in feats:
										_A									=       dataset[col]




@get_duration
def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







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




def    __A    (   ):

					'''simple docstring'''







					_A									=       {}
					_A									=       (
					    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:
										_A									=       datasets.Features({'''image''': ArrayaD(_lowercase    ,	dtype='''float32'''       )}       )
										_A									=       generate_examples(features=_lowercase    ,	num_examples=_lowercase       )
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

					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])}
										# )
										_A									=       datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''       )       )       )}       )
										_A									=       generate_examples(
										    features=_lowercase    ,	num_examples=_lowercase    ,	seq_shapes={'''image''': SPEED_TEST_SHAPE}       )
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

					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])}
										# )
										_A									=       datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''       )       )}       )
										_A									=       generate_examples(
										    features=_lowercase    ,	num_examples=_lowercase    ,	seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}    ,	)
										_A									=       write(_lowercase    ,	_lowercase    ,	_lowercase       )
										for read_func in read_functions:
															_A									=       read_func(_lowercase    ,	_lowercase       )

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


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











class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def __init__(     self:      Any      )			-> None:
												# numbermap keys are the number and its values are lists of indexes sorted
												# in ascending order
												_A									=       {}
												# indexmap keys are an index and it's values are the number at that index
												_A									=       {}

							def 					__A       (     self:      Any						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_delete() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == item:
																						array.pop(__A      )
																						return array
																	elif array[mid] < item:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1
												raise ValueError(
												    '''Either the item is not in the array or the array was unsorted'''      )

							def 					__A       (     self:      int						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_insert() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == index:
																						# If the item already exists in the array,
																						# insert it after the existing item
																						array.insert(mid + 1						,   __A      )
																						return array
																	elif array[mid] < index:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
												array.insert(__A						,   __A      )
												return array

							def 					__A       (     self:      Union[str, Any]						,   __A:      int						,   __A:      int      )			-> None:
												# Remove previous index
												if index in self.indexmap:
																	_A									=       self.indexmap[index]
																	if len(self.numbermap[n]      ) == 1:
																						del self.numbermap[n]
																	else:
																						_A									=       self.binary_search_delete(self.numbermap[n]						,   __A      )

        # Set new index
												_A									=       number

												# Number not seen before or empty so insert number value
												if number not in self.numbermap:
																	_A									=       [index]

												# Here we need to perform a binary search insertion in order to insert
												# The item in the correct place
												else:
																	_A									=       self.binary_search_insert(
																	    self.numbermap[number]						,   __A      )



							def 					__A       (     self:      List[Any]						,   __A:      int      )			-> int:
												return self.numbermap.get(__A						,   [-1]      )[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



