



'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				lowercase_    :      list[int] ,  lowercase_    :      list[int]						):

						# Check if the input is valid
						if not len(lowercase_						) == len(lowercase_						) == 3:
												raise ValueError("""Please enter a valid equation."""						)
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("""Both a & b of two equations can't be zero."""						)

						# Extract the coefficients
						lowercase     ,   lowercase     ,   lowercase        =							equationa
						lowercase     ,   lowercase     ,   lowercase        =							equationa

						# Calculate the determinants of the matrices
						lowercase        =							aa * ba - aa * ba
						lowercase        =							ca * ba - ca * ba
						lowercase        =							aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("""Infinite solutions. (Consistent system)"""						)
												else:
																		raise ValueError("""No solution. (Inconsistent system)"""						)
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		lowercase        =							determinant_x / determinant
																		lowercase        =							determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)







'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_      :				int   					=    50_0000

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







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Dict						):
						lowercase        =							dataset.map(**lowercase_						)







@get_duration
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      datasets.Dataset ,  **lowercase_    :      Optional[int]						):
						lowercase        =							dataset.filter(**lowercase_						)







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							{"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase        =							datasets.Features({"""text""": datasets.Value("""string"""						), """numbers""": datasets.Value("""float32"""						)}						)
												lowercase        =							generate_example_dataset(
												    os.path.join(lowercase_ ,  """dataset.arrow"""						) ,  lowercase_ ,  num_examples=lowercase_						)

												lowercase        =							transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" ,  use_fast=lowercase_						)

												def tokenize(lowercase_    :      Dict						):
																		return tokenizer(examples["""text"""]						)

												lowercase        =							map(lowercase_						)

												lowercase        =							map(lowercase_ ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""numpy"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""pandas"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""torch""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												with dataset.formatted_as(type="""tensorflow""" ,  columns="""numbers"""						):
																		lowercase        =							map(lowercase_ ,  function=lambda lowercase_						: None ,  batched=lowercase_						)

												lowercase        =							map(lowercase_ ,  function=lowercase_ ,  batched=lowercase_						)

												lowercase        =							filter(lowercase_						)

												# 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(lowercase_ ,  """wb"""						) as f:
												f.write(json.dumps(lowercase_						).encode("""utf-8"""						)						)


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


