





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
snake_case__					:							List[Any]													=     299792458

# Symbols
snake_case__						,    snake_case__						,    snake_case__						,    snake_case__					:							int													=     symbols('ct x y z')



def _a				(	lowerCamelCase:   float )	->						float:







      '''simple docstring'''






      if velocity > c:
            raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
      elif velocity < 1:
            # Usually the speed should be much higher than 1 (c order of magnitude)
            raise ValueError('''Speed must be greater than or equal to 1!''' )

      return velocity / c



def _a				(	lowerCamelCase:   float )	->						float:







      '''simple docstring'''






      return 1 / sqrt(1 - beta(lowerCamelCase ) ** 2 )



def _a				(	lowerCamelCase:   float )	->						np.ndarray:







      '''simple docstring'''






      return np.array(
          [
              [gamma(lowerCamelCase ), -gamma(lowerCamelCase ) * beta(lowerCamelCase ), 0, 0],
              [-gamma(lowerCamelCase ) * beta(lowerCamelCase ), gamma(lowerCamelCase ), 0, 0],
              [0, 0, 1, 0],
              [0, 0, 0, 1],
          ] )



def _a				(	lowerCamelCase:   float					,				lowerCamelCase:   np.ndarray | None = None )	->						np.ndarray:







      '''simple docstring'''






      if event is None:
            __A          =				np.array([ct, x, y, z] )  # Symbolic four vector
      else:
            event[0] *= c  # x0 is ct (speed of light * time)

      return transformation_matrix(lowerCamelCase ) @ event


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # Example of symbolic vector:
       snake_case__					:							Any													=     transform(29979245)
       print('Example of four vector: ')
       print(f'ct\' = {four_vector[0]}')
       print(f'x\' = {four_vector[1]}')
       print(f'y\' = {four_vector[2]}')
       print(f'z\' = {four_vector[3]}')

       # Substitute symbols with numerical values
       snake_case__					:							Optional[int]													=     {ct: c, x: 1, y: 1, z: 1}
       snake_case__					:							Dict													=     [four_vector[i].subs(sub_dict) for i in range(4)]

       print(f'\n{numerical_vector}')












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

snake_case__						,    snake_case__					:							Optional[Any]													=     os.path.split(__file__)
snake_case__					:							List[Any]													=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

            def tokenize(lowerCamelCase:   List[str] ):
                  return tokenizer(examples['''text'''] )

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				filter(lowerCamelCase )

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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





