





class A_					(			_lowerCamelCase   ):
   pass





class A_					(			_lowerCamelCase   ):
   pass





class A_					:




   def __init__(self	:Optional[int] )->					Union[str, Any]:
         __A          =				[
             [],
             [],
             [],
         ]




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:int )->					None:
         try:
               if len(self.queues[priority] ) >= 100:
                     raise OverflowError('''Maximum queue size is 100''' )
               self.queues[priority].append(_UpperCamelCase )
         except IndexError:
               raise ValueError('''Valid priorities are 0, 1, and 2''' )




   def _lowerCAmelCase      (self	:Dict )->					int:
         for queue in self.queues:
               if queue:
                     return queue.pop(0 )
         raise UnderFlowError('''All queues are empty''' )






   def __str__(self	:Optional[Any] )->					str:
         return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )





class A_					:




   def __init__(self	:Union[str, Any] )->					str:
         __A          =				[]




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:int )->					None:
         if len(self.queue ) == 100:
               raise OverFlowError('''Maximum queue size is 100''' )
         self.queue.append(_UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[Any] )->					int:
         if not self.queue:
               raise UnderFlowError('''The queue is empty''' )
         else:
               __A          =				min(self.queue )
               self.queue.remove(_UpperCamelCase )
               return data






   def __str__(self	:int )->					str:
         return str(self.queue )





def _a				(	)	->						Dict:







      '''simple docstring'''






      __A          =				FixedPriorityQueue()
      fpq.enqueue(0					,				10 )
      fpq.enqueue(1					,				70 )
      fpq.enqueue(0					,				1_00 )
      fpq.enqueue(2					,				1 )
      fpq.enqueue(2					,				5 )
      fpq.enqueue(1					,				7 )
      fpq.enqueue(2					,				4 )
      fpq.enqueue(1					,				64 )
      fpq.enqueue(0					,				1_28 )
      print(lowerCamelCase )
      print(fpq.dequeue() )
      print(fpq.dequeue() )
      print(fpq.dequeue() )
      print(fpq.dequeue() )
      print(fpq.dequeue() )
      print(lowerCamelCase )
      print(fpq.dequeue() )
      print(fpq.dequeue() )
      print(fpq.dequeue() )
      print(fpq.dequeue() )
      print(fpq.dequeue() )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				ElementPriorityQueue()
      epq.enqueue(10 )
      epq.enqueue(70 )
      epq.enqueue(1_00 )
      epq.enqueue(1 )
      epq.enqueue(5 )
      epq.enqueue(7 )
      epq.enqueue(4 )
      epq.enqueue(64 )
      epq.enqueue(1_28 )
      print(lowerCamelCase )
      print(epq.dequeue() )
      print(epq.dequeue() )
      print(epq.dequeue() )
      print(epq.dequeue() )
      print(epq.dequeue() )
      print(lowerCamelCase )
      print(epq.dequeue() )
      print(epq.dequeue() )
      print(epq.dequeue() )
      print(epq.dequeue() )
      print(epq.dequeue() )


if __name__ == "__main__":
       fixed_priority_queue()
       element_priority_queue()












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


snake_case__					:							Union[str, Any]													=     500000

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



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.map(**lowerCamelCase )



@get_duration
def _a				(	lowerCamelCase:   datasets.Dataset					,				**lowerCamelCase:   Optional[int] )	->						str:







      '''simple docstring'''






      __A          =				dataset.filter(**lowerCamelCase )



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				{'''num examples''': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            __A          =				datasets.Features({'''text''': datasets.Value('''string''' ), '''numbers''': datasets.Value('''float32''' )} )
            __A          =				generate_example_dataset(
                os.path.join(lowerCamelCase					,				'''dataset.arrow''' )					,				lowerCamelCase					,				num_examples=lowerCamelCase )

            __A          =				transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''					,				use_fast=lowerCamelCase )

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

            __A          =				map(lowerCamelCase )

            __A          =				map(lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''numpy''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''pandas''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''torch'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            with dataset.formatted_as(type='''tensorflow'''					,				columns='''numbers''' ):
                  __A          =				map(lowerCamelCase					,				function=lambda lowerCamelCase : None					,				batched=lowerCamelCase )

            __A          =				map(lowerCamelCase					,				function=lowerCamelCase					,				batched=lowerCamelCase )

            __A          =				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()





