






class 				UpperCAmelCase_  (		_lowercase		):
     pass







class 				UpperCAmelCase_  (		_lowercase		):
     pass







class 				UpperCAmelCase_  :




     def __init__(				self		):
          UpperCAmelCase__      :  List[Any]				      =			[
              [],
              [],
              [],
          ]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):



          try:
               if len(self.queues[priority]		) >= 100:
                    raise OverflowError("""Maximum queue size is 100"""		)
               self.queues[priority].append(__SCREAMING_SNAKE_CASE		)
          except IndexError:
               raise ValueError("""Valid priorities are 0, 1, and 2"""		)




     def   __UpperCAmelCase  (				self		):



          for queue in self.queues:
               if queue:
                    return queue.pop(0		)
          raise UnderFlowError("""All queues are empty"""		)




     def __str__(				self		):
          return "\n".join(f"Priority {i}: {q}" for i, q in enumerate(self.queues		)		)







class 				UpperCAmelCase_  :




     def __init__(				self		):
          UpperCAmelCase__      :  str				      =			[]




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):



          if len(self.queue		) == 100:
               raise OverFlowError("""Maximum queue size is 100"""		)
          self.queue.append(__SCREAMING_SNAKE_CASE		)




     def   __UpperCAmelCase  (				self		):



          if not self.queue:
               raise UnderFlowError("""The queue is empty"""		)
          else:
               UpperCAmelCase__      :  int				      =			min(self.queue		)
               self.queue.remove(__SCREAMING_SNAKE_CASE		)
               return data




     def __str__(				self		):
          return str(self.queue		)





def  _lowerCamelCase						(						) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[int]				      =			FixedPriorityQueue()
     fpq.enqueue(0 ,							10       )
     fpq.enqueue(1 ,							70       )
     fpq.enqueue(0 ,							100       )
     fpq.enqueue(2 ,							1       )
     fpq.enqueue(2 ,							5       )
     fpq.enqueue(1 ,							7       )
     fpq.enqueue(2 ,							4       )
     fpq.enqueue(1 ,							64       )
     fpq.enqueue(0 ,							128       )
     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  _lowerCamelCase						(						) ->			int:

     '''simple docstring'''
     UpperCAmelCase__      :  Union[str, Any]				      =			ElementPriorityQueue()
     epq.enqueue(10       )
     epq.enqueue(70       )
     epq.enqueue(100       )
     epq.enqueue(1       )
     epq.enqueue(5       )
     epq.enqueue(7       )
     epq.enqueue(4       )
     epq.enqueue(64       )
     epq.enqueue(128       )
     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()








"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


