





"""simple docstring"""



import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     None
 _lowerCamelCase        =     False
 _lowerCamelCase        =     False
 _lowerCamelCase        =     False
 _lowerCamelCase        =     None
 _lowerCamelCase        =     None
 _lowerCamelCase        =     False
 _lowerCamelCase        =     False
 _lowerCamelCase        =     False
 _lowerCamelCase        =     True
 _lowerCamelCase        =     None
 _lowerCamelCase        =     1
 _lowerCamelCase        =     None
 _lowerCamelCase        =     False
 _lowerCamelCase        =     None
 _lowerCamelCase        =     None

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.__class__(**{k: copy.deepcopy(__lowerCamelCase  ) for k, v in self.__dict__.items()}  )











"""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_  :  Tuple						)   ->Tuple:



    '''simple docstring'''







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







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



    '''simple docstring'''







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







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



    '''simple docstring'''







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







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



    '''simple docstring'''







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







def 		__lowercase     (   )   ->Optional[int]:



    '''simple docstring'''







    __A    :						int						 =     {'''num examples''': SPEED_TEST_N_EXAMPLES}
    __A    :						Optional[int]						 =     [
        (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    :						int						 =     [
        (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    :						Any						 =     datasets.Features(
            {'''list''': datasets.Sequence(datasets.Value('''float32'''						)						), '''numbers''': datasets.Value('''float32'''						)}						)
        __A    :						List[Any]						 =     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    :						Dict						 =     func(snake_case_				,**snake_case_						)

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




