



import numpy as np

lowercase_      :				str   					=    [
    ['''a''', '''b''', '''c''', '''d''', '''e'''],
    ['''f''', '''g''', '''h''', '''i''', '''k'''],
    ['''l''', '''m''', '''n''', '''o''', '''p'''],
    ['''q''', '''r''', '''s''', '''t''', '''u'''],
    ['''v''', '''w''', '''x''', '''y''', '''z'''],
]




class      __UpperCamelCase       :







	def __init__(    self						)    ->    None:
							'''simple docstring'''






							lowercase        =							np.array(_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase						)    ->    np.ndarray:
							'''simple docstring'''






							lowercase     ,   lowercase        =							np.where(letter == self.SQUARE						)
							lowercase        =							np.concatenate([indexa + 1, indexa + 1]						)
							return indexes







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							self.SQUARE[indexa - 1, indexa - 1]
							return letter







	def   _a   (    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							message.lower()
							lowercase        =							message.replace(""" """ ,				""""""						)
							lowercase        =							message.replace("""j""" ,				"""i"""						)

							lowercase        =							np.empty((2, len(_lowerCAmelCase						))						)
							for letter_index in range(len(_lowerCAmelCase						)						):
													lowercase        =							self.letter_to_numbers(message[letter_index]						)

													lowercase        =							numbers[0]
													lowercase        =							numbers[1]

							lowercase        =							first_step.reshape(2 * len(_lowerCAmelCase						)						)
							lowercase        =							""""""
							for numbers_index in range(len(_lowerCAmelCase						)						):
													lowercase        =							int(second_step[numbers_index * 2]						)
													lowercase        =							int(second_step[(numbers_index * 2) + 1]						)
													lowercase        =							self.numbers_to_letter(_lowerCAmelCase ,				_lowerCAmelCase						)
													lowercase        =							encoded_message + letter

							return encoded_message







	def   _a   (    self ,				_lowerCAmelCase						)    ->    str:
							'''simple docstring'''






							lowercase        =							message.lower()
							message.replace(""" """ ,				""""""						)
							lowercase        =							np.empty(2 * len(_lowerCAmelCase						)						)
							for letter_index in range(len(_lowerCAmelCase						)						):
													lowercase        =							self.letter_to_numbers(message[letter_index]						)
													lowercase        =							numbers[0]
													lowercase        =							numbers[1]

							lowercase        =							first_step.reshape((2, len(_lowerCAmelCase						))						)
							lowercase        =							""""""
							for numbers_index in range(len(_lowerCAmelCase						)						):
													lowercase        =							int(second_step[0, numbers_index]						)
													lowercase        =							int(second_step[1, numbers_index]						)
													lowercase        =							self.numbers_to_letter(_lowerCAmelCase ,				_lowerCAmelCase						)
													lowercase        =							decoded_message + letter

							return decoded_message







'''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()


