

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_									=   5_0_0_0_0_0

lowercase_									=   os.path.split(__file__)
lowercase_									=   os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def      a      (			A__	:   datasets.Dataset      ,   **A__	:   Optional[Any]     )    ->      Tuple:





      """simple docstring"""



      _lowercase	       =dataset.map(**_lowerCamelCase     )
@get_duration
def      a      (			A__	:   datasets.Dataset      ,   **A__	:   Tuple     )    ->      Union[str, Any]:





      """simple docstring"""



      _lowercase	       =dataset.filter(**_lowerCamelCase     )
def      a      (			)    ->      str:





      """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(_lowerCamelCase      ,   'dataset.arrow'     )      ,   _lowerCamelCase      ,   num_examples=_lowerCamelCase     )

            _lowercase	       =transformers.AutoTokenizer.from_pretrained('bert-base-cased'      ,   use_fast=_lowerCamelCase     )

            def tokenize(A__	:   List[str]     ):
                  return tokenizer(examples['text']     )

            _lowercase	       =map(_lowerCamelCase     )

            _lowercase	       =map(_lowerCamelCase      ,   batched=_lowerCamelCase     )

            _lowercase	       =map(_lowerCamelCase      ,   function=lambda A__     : None      ,   batched=_lowerCamelCase     )

            with dataset.formatted_as(type='numpy'     ):
                  _lowercase	       =map(_lowerCamelCase      ,   function=lambda A__     : None      ,   batched=_lowerCamelCase     )

            with dataset.formatted_as(type='pandas'     ):
                  _lowercase	       =map(_lowerCamelCase      ,   function=lambda A__     : None      ,   batched=_lowerCamelCase     )

            with dataset.formatted_as(type='torch'      ,   columns='numbers'     ):
                  _lowercase	       =map(_lowerCamelCase      ,   function=lambda A__     : None      ,   batched=_lowerCamelCase     )

            with dataset.formatted_as(type='tensorflow'      ,   columns='numbers'     ):
                  _lowercase	       =map(_lowerCamelCase      ,   function=lambda A__     : None      ,   batched=_lowerCamelCase     )

            _lowercase	       =map(_lowerCamelCase      ,   function=_lowerCamelCase      ,   batched=_lowerCamelCase     )

            _lowercase	       =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 Any






class      a						:






    def __init__(       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		List[Any]:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						None






class      a						:






    def __init__(       self     :							Union[str, Any]					)     ->		List[Any]:
      lowerCamelCase_         =						None






    def    UpperCamelCase       (       self     :							Dict					)     ->		Optional[int]:
      lowerCamelCase_         =						self.head
      while temp is not None:
        print(temp.data       ,						end=' '					)
        lowerCamelCase_         =						temp.next
      print()






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Union[str, Any]:
      lowerCamelCase_         =						Node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.head
      lowerCamelCase_         =						new_node






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		Union[str, Any]:
      if node_data_a == node_data_a:
        return
      else:
        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        lowerCamelCase_         =						self.head
        while node_a is not None and node_a.data != node_data_a:
          lowerCamelCase_         =						node_a.next

        if node_a is None or node_a is None:
          return

        lowerCamelCase_     ,	lowerCamelCase_         =						node_a.data, node_a.data


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						LinkedList()
  for i in range(5, 0, -1):
    ll.push(i)

  ll.print_list()

  ll.swap_nodes(1, 4)
  print('''After swapping''')
  ll.print_list()




