



"""simple docstring"""
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__SCREAMING_SNAKE_CASE  :						List[str]    							=      5_0_0_0_0_0

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



@get_duration
def        lowerCAmelCase_(   lowercase_     :							datasets.Dataset						,   **lowercase_     :							Union[str, Any]							)	->       List[str]:
				_lowerCamelCase        =				dataset.map(**lowercase_							)



@get_duration
def        lowerCAmelCase_(   lowercase_     :							datasets.Dataset						,   **lowercase_     :							Optional[int]							)	->       Any:
				_lowerCamelCase        =				dataset.filter(**lowercase_							)



def        lowerCAmelCase_(   )	->       List[Any]:
				_lowerCamelCase        =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								_lowerCamelCase        =				datasets.Features({'''text''': datasets.Value('''string'''							), '''numbers''': datasets.Value('''float32'''							)}							)
								_lowerCamelCase        =				generate_example_dataset(
								    os.path.join(lowercase_						,   '''dataset.arrow'''							)						,   lowercase_						,   num_examples=lowercase_							)

								_lowerCamelCase        =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''						,   use_fast=lowercase_							)

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

								_lowerCamelCase        =				map(lowercase_							)

								_lowerCamelCase        =				map(lowercase_						,   batched=lowercase_							)

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

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

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

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

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

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

								_lowerCamelCase        =				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 __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE  :						List[str]    							=      numpy.array([0, 0])
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      numpy.array([0.5, 0.866_0254])
__SCREAMING_SNAKE_CASE  :						Tuple    							=      numpy.array([1, 0])
__SCREAMING_SNAKE_CASE  :						List[Any]    							=      [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]						,   lowercase_     :							int							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				initial_vectors
				for _ in range(lowercase_							):
								_lowerCamelCase        =				iteration_step(lowercase_							)
				return vectors



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       list[numpy.ndarray]:
				_lowerCamelCase        =				[]
				for i, start_vector in enumerate(vectors[:-1]							):
								_lowerCamelCase        =				vectors[i + 1]
								new_vectors.append(lowercase_							)
								_lowerCamelCase        =				end_vector - start_vector
								new_vectors.append(start_vector + difference_vector / 3							)
								new_vectors.append(
								    start_vector + difference_vector / 3 + rotate(difference_vector / 3						,   60							)							)
								new_vectors.append(start_vector + difference_vector * 2 / 3							)
				new_vectors.append(vectors[-1]							)
				return new_vectors



def        lowerCAmelCase_(   lowercase_     :							numpy.ndarray						,   lowercase_     :							float							)	->       numpy.ndarray:
				_lowerCamelCase        =				numpy.radians(lowercase_							)
				_lowerCamelCase  ,     _lowerCamelCase        =				numpy.cos(lowercase_							), numpy.sin(lowercase_							)
				_lowerCamelCase        =				numpy.array(((c, -s), (s, c))							)
				return numpy.dot(lowercase_						,   lowercase_							)



def        lowerCAmelCase_(   lowercase_     :							list[numpy.ndarray]							)	->       None:
				_lowerCamelCase        =				plt.gca()
				axes.set_aspect('''equal'''							)

				# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
				# y-coordinates as inputs, which are constructed from the vector-list using
				# zip()
				_lowerCamelCase  ,     _lowerCamelCase        =				zip(*lowercase_							)
				plt.plot(lowercase_						,   lowercase_							)
				plt.show()


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			__SCREAMING_SNAKE_CASE  :						str    							=      iterate(INITIAL_VECTORS, 5)
			plot(processed_vectors)
