




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


UpperCAmelCase_		:					List[str]          =  500000

UpperCAmelCase_    ,  UpperCAmelCase_		:					List[Any]          =  os.path.split(__file__)
UpperCAmelCase_		:					Any          =  os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




@get_duration
def 		lowerCAmelCase_ (						lowerCamelCase						,  **lowerCamelCase ):
				__magic_name__						:						Tuple										=dataset.map(**lowerCamelCase )




@get_duration
def 		lowerCAmelCase_ (						lowerCamelCase						,  **lowerCamelCase ):
				__magic_name__						:						List[str]										=dataset.filter(**lowerCamelCase )




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

								__magic_name__						:						Dict										=transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,  use_fast=lowerCamelCase )

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

								__magic_name__						:						Union[str, Any]										=map(lowerCamelCase )

								__magic_name__						:						List[Any]										=map(lowerCamelCase						,  batched=lowerCamelCase )

								__magic_name__						:						Optional[int]										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

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

								with dataset.formatted_as(type="""pandas""" ):
												__magic_name__						:						int										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								with dataset.formatted_as(type="""torch"""						,  columns="""numbers""" ):
												__magic_name__						:						List[Any]										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								with dataset.formatted_as(type="""tensorflow"""						,  columns="""numbers""" ):
												__magic_name__						:						Optional[Any]										=map(lowerCamelCase						,  function=lambda lowerCamelCase : None						,  batched=lowerCamelCase )

								__magic_name__						:						Optional[Any]										=map(lowerCamelCase						,  function=lowerCamelCase						,  batched=lowerCamelCase )

								__magic_name__						:						Optional[int]										=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"""





from typing import List

from .keymap import KEYMAP, get_character



def   a    (     __UpperCAmelCase      :    str  )    ->							Optional[int]:

						def decorator(__UpperCAmelCase      :    List[str]  ):
												__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   """handle_key"""      ,   []  )
												handle += [key]
												setattr(__UpperCAmelCase      ,   """handle_key"""      ,   __UpperCAmelCase  )
												return func

						return decorator



def   a    (     *__UpperCAmelCase      :    List[str]  )    ->							Union[str, Any]:

						def decorator(__UpperCAmelCase      :    Tuple  ):
												__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   """handle_key"""      ,   []  )
												handle += keys
												setattr(__UpperCAmelCase      ,   """handle_key"""      ,   __UpperCAmelCase  )
												return func

						return decorator



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __new__(			cls  :      Optional[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any					)			->							List[Any]:
											__magic_name__:	str							=       super().__new__(cls     ,		__snake_case     ,		__snake_case     ,		__snake_case					)
											if not hasattr(__snake_case     ,		"""key_handler"""					):
																	setattr(__snake_case     ,		"""key_handler"""     ,		{}					)
											setattr(__snake_case     ,		"""handle_input"""     ,		KeyHandler.handle_input					)

											for value in attrs.values():
																	__magic_name__:	List[str]							=       getattr(__snake_case     ,		"""handle_key"""     ,		[]					)
																	for key in handled_keys:
																							__magic_name__:	Any							=       value
											return new_cls






					@staticmethod
					def   lowerCamelCase__   (			cls  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	List[str]							=       get_character()
											if char != KEYMAP["undefined"]:
																	__magic_name__:	Union[str, Any]							=       ord(__snake_case					)
											__magic_name__:	Dict							=       cls.key_handler.get(__snake_case					)
											if handler:
																	__magic_name__:	List[str]							=       char
																	return handler(cls					)
											else:
																	return None



def   a    (     cls      :    List[Any]  )    ->							int:
						return KeyHandler(cls.__name__      ,   cls.__bases__      ,   cls.__dict__.copy()  )



