
"""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    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Optional[int]						      =				dataset.map(**_UpperCamelCase		)
@get_duration
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Optional[int]						      =				dataset.filter(**_UpperCamelCase		)
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						str						      =				{'num examples': SPEED_TEST_N_EXAMPLES}
	with tempfile.TemporaryDirectory() as tmp_dir:
		__lowerCAmelCase      :						Optional[int]						      =				datasets.Features({'text': datasets.Value('string'		), 'numbers': datasets.Value('float32'		)}		)
		__lowerCAmelCase      :						str						      =				generate_example_dataset(
		    os.path.join(_UpperCamelCase	,  'dataset.arrow'		)	,  _UpperCamelCase	,  num_examples=_UpperCamelCase		)

		__lowerCAmelCase      :						int						      =				transformers.AutoTokenizer.from_pretrained('bert-base-cased'	,  use_fast=_UpperCamelCase		)

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

		__lowerCAmelCase      :						List[str]						      =				map(_UpperCamelCase		)

		__lowerCAmelCase      :						Union[str, Any]						      =				map(_UpperCamelCase	,  batched=_UpperCamelCase		)

		__lowerCAmelCase      :						Dict						      =				map(_UpperCamelCase	,  function=lambda _UpperCamelCase		: None	,  batched=_UpperCamelCase		)

		with dataset.formatted_as(type='numpy'		):
			__lowerCAmelCase      :						Union[str, Any]						      =				map(_UpperCamelCase	,  function=lambda _UpperCamelCase		: None	,  batched=_UpperCamelCase		)

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

		with dataset.formatted_as(type='torch'	,  columns='numbers'		):
			__lowerCAmelCase      :						Dict						      =				map(_UpperCamelCase	,  function=lambda _UpperCamelCase		: None	,  batched=_UpperCamelCase		)

		with dataset.formatted_as(type='tensorflow'	,  columns='numbers'		):
			__lowerCAmelCase      :						Optional[int]						      =				map(_UpperCamelCase	,  function=lambda _UpperCamelCase		: None	,  batched=_UpperCamelCase		)

		__lowerCAmelCase      :						List[str]						      =				map(_UpperCamelCase	,  function=_UpperCamelCase	,  batched=_UpperCamelCase		)

		__lowerCAmelCase      :						Optional[int]						      =				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"""







import warnings
from functools import wraps
from typing import Callable
def    __lowerCAmelCase    (_UpperCamelCase		):

	@wraps(_UpperCamelCase		)
	def _inner_fn(*_UpperCamelCase	,  **_UpperCamelCase		):
		warnings.warn(
		    (F"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")	,  _UpperCamelCase	,  )
		return fn(*_UpperCamelCase	,  **_UpperCamelCase		)

	return _inner_fn