





'''simple docstring'''

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase__						:      str	=    50_00_00

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







@get_duration
def  a__    (    lowercase :					datasets.Dataset, **lowercase :					str				)		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	dataset.map(**lowercase				)







@get_duration
def  a__    (    lowercase :					datasets.Dataset, **lowercase :					List[str]				)		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	dataset.filter(**lowercase				)







def  a__    (    )		->		Union[str, Any]:





     """simple docstring"""

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

          _UpperCamelCase       =	transformers.AutoTokenizer.from_pretrained('''bert-base-cased''', use_fast=lowercase				)

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

          _UpperCamelCase       =	map(lowercase				)

          _UpperCamelCase       =	map(lowercase, batched=lowercase				)

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

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

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

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

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

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

          _UpperCamelCase       =	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()









'''simple docstring'''

from math import isclose, sqrt







def  a__    (    lowercase :					float, lowercase :					float, lowercase :					float				)		->		tuple[float, float, float]:





     """simple docstring"""

     _UpperCamelCase       =	point_y / 4 / point_x
     _UpperCamelCase       =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
     _UpperCamelCase       =	(1 - normal_gradient * normal_gradient) / (
         1 + normal_gradient * normal_gradient
     )
     _UpperCamelCase       =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

     # to find the next point, solve the simultaeneous equations:
     # y^2 + 4x^2 = 100
     # y - b = m * (x - a)
     # ==> A x^2 + B x + C = 0
     _UpperCamelCase       =	outgoing_gradient**2 + 4
     _UpperCamelCase       =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
     _UpperCamelCase       =	(point_y - outgoing_gradient * point_x) ** 2 - 100

     _UpperCamelCase       =	(
         -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)
     _UpperCamelCase       =	(
         -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)

     # two solutions, one of which is our input point
     _UpperCamelCase       =	x_minus if isclose(lowercase, lowercase				) else x_plus
     _UpperCamelCase       =	point_y + outgoing_gradient * (next_x - point_x)

     return next_x, next_y, outgoing_gradient







def  a__    (    lowercase :					float = 1.4, lowercase :					float = -9.6				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	0
     _UpperCamelCase       =	first_x_coord
     _UpperCamelCase       =	first_y_coord
     _UpperCamelCase       =	(1_0.1 - point_y) / (0.0 - point_x)

     while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
          _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	next_point(lowercase, lowercase, lowercase				)
          num_reflections += 1

     return num_reflections


if __name__ == "__main__":
   print(F"""{solution() = }""")



