




'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


A					       =logging.get_logger(__name__)

A					       ={
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}


class      _a    (      __a					):
       __a							:	Optional[int]  							=   """regnet"""
       __a							:	Optional[Any]  							=   ["""x""", """y"""]
       def __init__(      self		:      int  ,							lowercase		:      List[Any]=3  ,							lowercase		:      Union[str, Any]=32  ,							lowercase		:      Tuple=[128, 192, 512, 1_088]  ,							lowercase		:      List[Any]=[2, 6, 12, 2]  ,							lowercase		:      Optional[Any]=64  ,							lowercase		:      Optional[int]="y"  ,							lowercase		:      int="relu"  ,							**lowercase		:      int  ,							):



           '''simple docstring'''

           super().__init__(**lowercase							)
           if layer_type not in self.layer_types:
               raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types							)}"							)
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						embedding_size
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						groups_width
           UpperCAmelCase     			=						layer_type
           UpperCAmelCase     			=						hidden_act
           # always downsample in the first stage
           UpperCAmelCase     			=						True










'''simple docstring'''







import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


A					       =10_00_00_00_00_00
A					       =1_00_00

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





def 		snake_case_				(_a      :				int       ,  _a      :				int	):
    UpperCAmelCase     			=						pa.Table.from_pydict({'''col''': [0] * chunk_size}	)
    UpperCAmelCase     			=						pa.concat_tables([table] * (num_examples // chunk_size)	)
    return datasets.Dataset(_a       ,  fingerprint='''table_100B'''	)


@dataclass
class      _a    :
       __a							:	int
       __a							:	int
       __a							:	int
       __a							:	int
       def        A (      self		:      List[str]							):



           '''simple docstring'''

           UpperCAmelCase     			=						np.random.default_rng(self.seed							)
           UpperCAmelCase     			=						rng.integers(low=self.low  ,							high=self.high  ,							size=self.size							).tolist()
       def __iter__(      self		:      Tuple							):



           '''simple docstring'''

           return iter(self._sampled_values							)







       def __len__(      self		:      Dict							):



           '''simple docstring'''

           return self.size






@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[0]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[-1]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[range(len(_a	) // 2       ,  len(_a	) // 2 + 1_0_2_4	)]





@get_duration
def 		snake_case_				(_a      :				datasets.Dataset	):
    UpperCAmelCase     			=						dataset[RandIter(0       ,  len(_a	)       ,  1_0_2_4       ,  seed=4_2	)]





def 		snake_case_				():
    UpperCAmelCase     			=						{'''num examples''': SPEED_TEST_N_EXAMPLES}
    UpperCAmelCase     			=						(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
    print('''generating dataset'''	)
    UpperCAmelCase     			=						generate_100B_dataset(num_examples=_a       ,  chunk_size=_a	)
    print('''Functions'''	)
    for func in functions:
        print(func.__name__	)
        UpperCAmelCase     			=						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()




