
'''simple docstring'''

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__snake_case:	Union[str, Any]										=      50_00_00

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






@get_duration
def 						_snake_case    (   A_							:     Tuple  ,		**A_							:     List[str] ):
  """simple docstring"""







  a_   :						int   =				dataset.map(**A_ )






@get_duration
def 						_snake_case    (   A_							:     int  ,		**A_							:     List[Any] ):
  """simple docstring"""







  a_   :						str   =				dataset.filter(**A_ )






def 						_snake_case    (   ):
  """simple docstring"""







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

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

    def tokenize(A_							:     Optional[int] ):
      return tokenizer(examples["""text"""] )

    a_   :						List[str]   =				map(A_ )

    a_   :						Tuple   =				map(A_  ,		batched=A_ )

    a_   :						Optional[Any]   =				map(A_  ,		function=lambda A_ : None  ,		batched=A_ )

    with dataset.formatted_as(type="""numpy""" ):
      a_   :						List[Any]   =				map(A_  ,		function=lambda A_ : None  ,		batched=A_ )

    with dataset.formatted_as(type="""pandas""" ):
      a_   :						Optional[int]   =				map(A_  ,		function=lambda A_ : None  ,		batched=A_ )

    with dataset.formatted_as(type="""torch"""  ,		columns="""numbers""" ):
      a_   :						Dict   =				map(A_  ,		function=lambda A_ : None  ,		batched=A_ )

    with dataset.formatted_as(type="""tensorflow"""  ,		columns="""numbers""" ):
      a_   :						Union[str, Any]   =				map(A_  ,		function=lambda A_ : None  ,		batched=A_ )

    a_   :						List[Any]   =				map(A_  ,		function=A_  ,		batched=A_ )

    a_   :						Dict   =				filter(A_ )

    # 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(A_  ,		"""wb""" ) as f:
    f.write(json.dumps(A_ ).encode("""utf-8""" ) )


if __name__ == "__main__":  # useful to run the profiler
    benchmark_map_filter()








def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if index == r:
						for j in range(SCREAMING_SNAKE_CASE ):
									print(data[j]       ,    end=' ' )
						print(' ' )
						return
			#  When no more elements are there to put in data[]
			if i >= n:
						return
			# current is included, put next at next location
			SCREAMING_SNAKE_CASE_								=						arr[i]
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    0       ,    SCREAMING_SNAKE_CASE       ,    0 )


if __name__ == "__main__":
				# Driver code to check the function above
				SCREAMING_SNAKE_CASE__     :     Dict		    =       [10, 20, 30, 40, 50]
				print_combination(arr, len(arr), 3)
				# This code is contributed by Ambuj sahu
