



'''simple docstring'''


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__    	=     500_000

lowerCamelCase__	,lowerCamelCase__    	=     os.path.split(__file__)
lowerCamelCase__    	=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def  __lowerCAmelCase (__lowerCAmelCase      , **__lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[Any]								=      dataset.map(**__lowerCAmelCase   )






@get_duration
def  __lowerCAmelCase (__lowerCAmelCase      , **__lowerCAmelCase   ):
		_UpperCAmelCase		:       Dict								=      dataset.filter(**__lowerCAmelCase   )






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       str								=      {"num examples": SPEED_TEST_N_EXAMPLES}
		with tempfile.TemporaryDirectory() as tmp_dir:
				_UpperCAmelCase		:       Tuple								=      datasets.Features({"text": datasets.Value("string"   ), "numbers": datasets.Value("float32"   )}   )
				_UpperCAmelCase		:       Optional[Any]								=      generate_example_dataset(
				    os.path.join(__lowerCAmelCase      , "dataset.arrow"   )      , __lowerCAmelCase      , num_examples=__lowerCAmelCase   )

				_UpperCAmelCase		:       Tuple								=      transformers.AutoTokenizer.from_pretrained("bert-base-cased"      , use_fast=__lowerCAmelCase   )

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

				_UpperCAmelCase		:       List[Any]								=      map(__lowerCAmelCase   )

				_UpperCAmelCase		:       List[Any]								=      map(__lowerCAmelCase      , batched=__lowerCAmelCase   )

				_UpperCAmelCase		:       str								=      map(__lowerCAmelCase      , function=lambda __lowerCAmelCase   : None      , batched=__lowerCAmelCase   )

				with dataset.formatted_as(type="numpy"   ):
						_UpperCAmelCase		:       Optional[Any]								=      map(__lowerCAmelCase      , function=lambda __lowerCAmelCase   : None      , batched=__lowerCAmelCase   )

				with dataset.formatted_as(type="pandas"   ):
						_UpperCAmelCase		:       Dict								=      map(__lowerCAmelCase      , function=lambda __lowerCAmelCase   : None      , batched=__lowerCAmelCase   )

				with dataset.formatted_as(type="torch"      , columns="numbers"   ):
						_UpperCAmelCase		:       Optional[Any]								=      map(__lowerCAmelCase      , function=lambda __lowerCAmelCase   : None      , batched=__lowerCAmelCase   )

				with dataset.formatted_as(type="tensorflow"      , columns="numbers"   ):
						_UpperCAmelCase		:       Tuple								=      map(__lowerCAmelCase      , function=lambda __lowerCAmelCase   : None      , batched=__lowerCAmelCase   )

				_UpperCAmelCase		:       Tuple								=      map(__lowerCAmelCase      , function=__lowerCAmelCase      , batched=__lowerCAmelCase   )

				_UpperCAmelCase		:       Dict								=      filter(__lowerCAmelCase   )

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


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







'''simple docstring'''


import os






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       List[Any]								=      os.path.join(os.path.dirname(__lowerCAmelCase   )      , "num.txt"   )
		with open(__lowerCAmelCase   ) as file_hand:
				return str(sum(int(__lowerCAmelCase   ) for line in file_hand   )   )[:10]


if __name__ == "__main__":
						print(solution())



