





'''simple docstring'''




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a__					:			Union[str, Any]          =     50_0000

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




@get_duration
def 						__lowerCamelCase			(	UpperCAmelCase_					,				**UpperCAmelCase_							)   ->int:
	snake_case__	       =   dataset.map(**UpperCAmelCase_							)




@get_duration
def 						__lowerCamelCase			(	UpperCAmelCase_					,				**UpperCAmelCase_							)   ->str:
	snake_case__	       =   dataset.filter(**UpperCAmelCase_							)




def 						__lowerCamelCase			(	)   ->int:
	snake_case__	       =   {'num examples': SPEED_TEST_N_EXAMPLES}
	with tempfile.TemporaryDirectory() as tmp_dir:
		snake_case__	       =   datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
		snake_case__	       =   generate_example_dataset(
		    os.path.join(UpperCAmelCase_					,				'dataset.arrow'							)					,				UpperCAmelCase_					,				num_examples=UpperCAmelCase_							)

		snake_case__	       =   transformers.AutoTokenizer.from_pretrained('bert-base-cased'					,				use_fast=UpperCAmelCase_							)

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

		snake_case__	       =   map(UpperCAmelCase_							)

		snake_case__	       =   map(UpperCAmelCase_					,				batched=UpperCAmelCase_							)

		snake_case__	       =   map(UpperCAmelCase_					,				function=lambda UpperCAmelCase_							: None					,				batched=UpperCAmelCase_							)

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

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

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

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

		snake_case__	       =   map(UpperCAmelCase_					,				function=UpperCAmelCase_					,				batched=UpperCAmelCase_							)

		snake_case__	       =   filter(UpperCAmelCase_							)

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


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










'''simple docstring'''




from abc import ABC, abstractmethod
from argparse import ArgumentParser






class 				__snake_case     (					__magic_name__							):





	@staticmethod
	@abstractmethod
	def   _snake_case   (		UpperCamelCase_		)      ->		Dict:
		raise NotImplementedError()





	@abstractmethod
	def   _snake_case   (		self		)      ->		List[Any]:
		raise NotImplementedError()



