

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase			     =       5_0_0_0_0_0

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



@get_duration
def    _lowercase			(	a__						:			int							,  **a__						:			Tuple					)							->					Any:





	"""simple docstring"""


	_UpperCamelCase			=   dataset.map(**_UpperCamelCase					)



@get_duration
def    _lowercase			(	a__						:			List[Any]							,  **a__						:			Dict					)							->					int:





	"""simple docstring"""


	_UpperCamelCase			=   dataset.filter(**_UpperCamelCase					)



def    _lowercase			(	)							->					Tuple:





	"""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(_UpperCamelCase							,  "dataset.arrow"					)							,  _UpperCamelCase							,  num_examples=_UpperCamelCase					)

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

		def tokenize(a__						:			List[str]					):
			return tokenizer(examples["text"]					)

		_UpperCamelCase			=   map(_UpperCamelCase					)

		_UpperCamelCase			=   map(_UpperCamelCase							,  batched=_UpperCamelCase					)

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

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

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

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

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

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

		_UpperCamelCase			=   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()








import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class 						lowerCamelCase_							:



					'''simple docstring'''







					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					True
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					1
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					False
					__UpperCAmelCase							  =					None
					__UpperCAmelCase							  =					None






					def A   (					self    ) ->      "DownloadConfig":







								'''simple docstring'''

								return self.__class__(**{k: copy.deepcopy(snake_case_    ) for k, v in self.__dict__.items()}    )


