





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


A__:  int       					=					1000_0000_0000
A__:  int       					=					1_0000

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



def 							lowerCAmelCase_				(    A_  ,A_):
    UpperCamelCase__:					Any												=      pa.Table.from_pydict({"col": [0] * chunk_size})
    UpperCamelCase__:					Tuple												=      pa.concat_tables([table] * (num_examples // chunk_size))
    return datasets.Dataset(__lowercase  ,fingerprint="table_100B")




@dataclass
class    _a  :

      """simple docstring"""


      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42
      UpperCamelCase__       						=   42






      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					List[Any]												=      np.random.default_rng(self.seed			)
          UpperCamelCase__:					Tuple												=      rng.integers(low=self.low					,  high=self.high					,  size=self.size			).tolist()






      def __iter__(						self:	Optional[Any]			):
          '''simple docstring'''


          return iter(self._sampled_values			)






      def __len__(						self:	Any			):
          '''simple docstring'''


          return self.size



@get_duration
def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					Any												=      dataset[0]



@get_duration
def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					List[str]												=      dataset[-1]



@get_duration
def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					str												=      dataset[range(len(__lowercase) // 2  ,len(__lowercase) // 2 + 10_24)]



@get_duration
def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					List[str]												=      dataset[RandIter(0  ,len(__lowercase)  ,10_24  ,seed=42)]



def 							lowerCAmelCase_				(    ):
    UpperCamelCase__:					str												=      {"num examples": SPEED_TEST_N_EXAMPLES}
    UpperCamelCase__:					Dict												=      (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
    print("generating dataset")
    UpperCamelCase__:					Tuple												=      generate_100B_dataset(num_examples=__lowercase  ,chunk_size=__lowercase)
    print("Functions")
    for func in functions:
        print(func.__name__)
        UpperCamelCase__:					int												=      func(__lowercase)

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


if __name__ == "__main__":  # useful to run the profiler
   benchmark_table_100B()








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

