



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__A   			=5_0_0_0_0_0

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

@get_duration
def        a						(      _UpperCAmelCase							:					datasets.Dataset ,   **_UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	dataset.map(**_UpperCAmelCase						)

@get_duration
def        a						(      _UpperCAmelCase							:					datasets.Dataset ,   **_UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	dataset.filter(**_UpperCAmelCase						)

def        a						(      ):







		'''simple docstring'''





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

				__UpperCAmelCase    :     Any													=	transformers.AutoTokenizer.from_pretrained('''bert-base-cased''' ,   use_fast=_UpperCAmelCase						)

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

				__UpperCAmelCase    :     List[Any]													=	map(_UpperCAmelCase						)

				__UpperCAmelCase    :     Dict													=	map(_UpperCAmelCase ,   batched=_UpperCAmelCase						)

				__UpperCAmelCase    :     List[Any]													=	map(_UpperCAmelCase ,   function=lambda _UpperCAmelCase						: None ,   batched=_UpperCAmelCase						)

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

				with dataset.formatted_as(type='''pandas'''						):
						__UpperCAmelCase    :     List[Any]													=	map(_UpperCAmelCase ,   function=lambda _UpperCAmelCase						: None ,   batched=_UpperCAmelCase						)

				with dataset.formatted_as(type='''torch''' ,   columns='''numbers'''						):
						__UpperCAmelCase    :     Tuple													=	map(_UpperCAmelCase ,   function=lambda _UpperCAmelCase						: None ,   batched=_UpperCAmelCase						)

				with dataset.formatted_as(type='''tensorflow''' ,   columns='''numbers'''						):
						__UpperCAmelCase    :     List[str]													=	map(_UpperCAmelCase ,   function=lambda _UpperCAmelCase						: None ,   batched=_UpperCAmelCase						)

				__UpperCAmelCase    :     Union[str, Any]													=	map(_UpperCAmelCase ,   function=_UpperCAmelCase ,   batched=_UpperCAmelCase						)

				__UpperCAmelCase    :     Optional[Any]													=	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

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: Optional[Any]       ,       a_							: Optional[int]=None ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	data
					__UpperCAmelCase    :     Any													=	None




			def __repr__(   self							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	[]
					__UpperCAmelCase    :     Any													=	self
					while temp:
							string_rep.append(F'{temp.data}' )
							__UpperCAmelCase    :     Dict													=	temp.next
					return "->".join(a_ )

def        a						(      _UpperCAmelCase							:					list						):







		'''simple docstring'''





		if not elements_list:
				raise Exception('''The Elements List is empty'''						)

		__UpperCAmelCase    :     int													=	Node(elements_list[0]						)
		for i in range(1 ,   len(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     Any													=	Node(elements_list[i]						)
				__UpperCAmelCase    :     Optional[int]													=	current.next
		return head

def        a						(      _UpperCAmelCase							:					Node						):







		'''simple docstring'''





		if head_node is not None and isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
				print_reverse(head_node.next						)
				print(head_node.data						)

def        a						(      ):







		'''simple docstring'''





		from doctest import testmod

		testmod()

		__UpperCAmelCase    :     Tuple													=	make_linked_list([14, 52, 14, 12, 43]						)
		print('''Linked List:'''						)
		print(_UpperCAmelCase						)
		print('''Elements in Reverse:'''						)
		print_reverse(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





