
import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase__	:List[str]										=					50_0000

lowercase__	:Union[str, Any]										=					os.path.split(__file__)
lowercase__	:Dict										=					os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def  UpperCamelCase					(							lowerCAmelCase__				,    **lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		dataset.map(**__UpperCamelCase	)



@get_duration
def  UpperCamelCase					(							lowerCAmelCase__				,    **lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		dataset.filter(**__UpperCamelCase	)



def  UpperCamelCase					(							):
					'''simple docstring'''
					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(__UpperCamelCase				,    '''dataset.arrow'''	)				,    __UpperCamelCase				,    num_examples=__UpperCamelCase	)

										lowercase					=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''				,    use_fast=__UpperCamelCase	)

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

										lowercase					=		map(__UpperCamelCase	)

										lowercase					=		map(__UpperCamelCase				,    batched=__UpperCamelCase	)

										lowercase					=		map(__UpperCamelCase				,    function=lambda lowerCAmelCase__	: None				,    batched=__UpperCamelCase	)

										with dataset.formatted_as(type='''numpy'''	):
															lowercase					=		map(__UpperCamelCase				,    function=lambda lowerCAmelCase__	: None				,    batched=__UpperCamelCase	)

										with dataset.formatted_as(type='''pandas'''	):
															lowercase					=		map(__UpperCamelCase				,    function=lambda lowerCAmelCase__	: None				,    batched=__UpperCamelCase	)

										with dataset.formatted_as(type='''torch'''				,    columns='''numbers'''	):
															lowercase					=		map(__UpperCamelCase				,    function=lambda lowerCAmelCase__	: None				,    batched=__UpperCamelCase	)

										with dataset.formatted_as(type='''tensorflow'''				,    columns='''numbers'''	):
															lowercase					=		map(__UpperCamelCase				,    function=lambda lowerCAmelCase__	: None				,    batched=__UpperCamelCase	)

										lowercase					=		map(__UpperCamelCase				,    function=__UpperCamelCase				,    batched=__UpperCamelCase	)

										lowercase					=		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()






from __future__ import annotations



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < 2:
										raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''	)
					if any(i <= 0 for i in nums	):
										raise ValueError('''All values must be greater than 0'''	)
					lowercase					=		nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




