





"""simple docstring"""



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


a_			=      50000
a_			=      5000

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







@get_duration
def 		__lowercase					(       snake_case_  :  datasets.Dataset    ,snake_case_  :  Dict						)       ->List[Any]:



    '''simple docstring'''







    for i in range(snake_case_						):
        __A   :						Tuple									=					dataset[i]







@get_duration
def 		__lowercase					(       snake_case_  :  datasets.Dataset    ,snake_case_  :  Optional[Any]    ,snake_case_  :  List[str]						)       ->Union[str, Any]:



    '''simple docstring'''







    for i in range(0    ,len(snake_case_						)    ,snake_case_						):
        __A   :						Dict									=					dataset[i : i + batch_size]







@get_duration
def 		__lowercase					(       snake_case_  :  datasets.Dataset    ,snake_case_  :  List[Any]    ,snake_case_  :  str						)       ->List[str]:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(snake_case_						):
            __A   :						Any									=					dataset[i]







@get_duration
def 		__lowercase					(       snake_case_  :  datasets.Dataset    ,snake_case_  :  str    ,snake_case_  :  Optional[int]    ,snake_case_  :  List[str]						)       ->List[str]:



    '''simple docstring'''







    with dataset.formatted_as(type=snake_case_						):
        for i in range(0    ,snake_case_    ,snake_case_						):
            __A   :						Union[str, Any]									=					dataset[i : i + batch_size]







def 		__lowercase					(       )       ->Tuple:



    '''simple docstring'''







    __A   :						List[Any]									=					{'''num examples''': SPEED_TEST_N_EXAMPLES}
    __A   :						List[Any]									=					[
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
        (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
    ]

    __A   :						Optional[Any]									=					[
        (read, {'''length''': SMALL_TEST}),
        (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
        (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
        (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
        (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print('''generating dataset'''						)
        __A   :						Tuple									=					datasets.Features(
            {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
        __A   :						Tuple									=					generate_example_dataset(
            os.path.join(snake_case_    ,'''dataset.arrow'''						)    ,snake_case_    ,num_examples=snake_case_    ,seq_shapes={'''list''': (100,)}    ,)
        print('''first set of iterations'''						)
        for func, kwargs in functions:
            print(func.__name__    ,str(snake_case_						)						)
            __A   :						Optional[Any]									=					func(snake_case_    ,**snake_case_						)

        print('''shuffling dataset'''						)
        __A   :						Optional[int]									=					dataset.shuffle()
        print('''Second set of iterations (after shuffling'''						)
        for func, kwargs in functions_shuffled:
            print('''shuffled '''    ,func.__name__    ,str(snake_case_						)						)
            __A   :						List[Any]									=					func(
                snake_case_    ,**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_iterating()











"""simple docstring"""



from typing import TYPE_CHECKING

from ....utils import _LazyModule


a_			=      {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
       from .tokenization_tapex import TapexTokenizer


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure)




