


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


snake_case				:    str       				=     get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
     snake_case				:    Union[str, Any]       				=     json.load(f)







@require_torch
class 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return FSMTTokenizer.from_pretrained(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Tuple 							=       FSMTForConditionalGeneration.from_pretrained(_lowerCamelCase    ).to(_lowerCamelCase    )
       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       # note: this test is not testing the best performance since it only evals a small batch
       # but it should be enough to detect a regression in the output quality
       a						:Tuple 							=       F'''facebook/wmt19-{pair}'''
       a						:Optional[Any] 							=       self.get_tokenizer(_lowerCamelCase    )
       a						:str 							=       self.get_model(_lowerCamelCase    )

       a						:Optional[int] 							=       bleu_data[pair]['''src''']
       a						:Optional[Any] 							=       bleu_data[pair]['''tgt''']

       a						:str 							=       tokenizer(_lowerCamelCase					,  return_tensors='''pt'''					,  truncation=_lowerCamelCase					,  padding='''longest'''    ).to(_lowerCamelCase    )
       a						:Optional[Any] 							=       model.generate(
           input_ids=batch.input_ids					,  num_beams=8					,  )
       a						:str 							=       tokenizer.batch_decode(
           _lowerCamelCase					,  skip_special_tokens=_lowerCamelCase					,  clean_up_tokenization_spaces=_lowerCamelCase    )
       a						:int 							=       calculate_bleu(_lowerCamelCase					,  _lowerCamelCase    )
       print(_lowerCamelCase    )
       self.assertGreaterEqual(scores['''bleu''']					,  _lowerCamelCase    )









def 				__lowerCamelCase						( UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		list ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      if len(UpperCAmelCase_						) != len(UpperCAmelCase_						):
            raise ValueError('''The length of profit and weight must be same.'''						)
      if max_weight <= 0:
            raise ValueError('''max_weight must greater than zero.'''						)
      if any(p < 0 for p in profit						):
            raise ValueError('''Profit can not be negative.'''						)
      if any(w < 0 for w in weight						):
            raise ValueError('''Weight can not be negative.'''						)

      # List created to store profit gained for the 1kg in case of each weight
      # respectively.  Calculate and append profit/weight for each element.
      a						:Optional[int] 							=       [p / w for p, w in zip(UpperCAmelCase_ ,  UpperCAmelCase_						)]

      # Creating a copy of the list and sorting profit/weight in ascending order
      a						:List[Any] 							=       sorted(UpperCAmelCase_						)

      # declaring useful variables
      a						:Dict 							=       len(UpperCAmelCase_						)
      a						:Tuple 							=       0
      a						:List[Any] 							=       0
      a						:str 							=       0

      # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
      while limit <= max_weight and i < length:
            # flag value for encountered greatest element in sorted_profit_by_weight
            a						:List[Any] 							=       sorted_profit_by_weight[length - i - 1]
            a						:Optional[Any] 							=       profit_by_weight.index(UpperCAmelCase_						)
            a						:Optional[int] 							=       -1

            # check if the weight encountered is less than the total weight
            # encountered before.
            if max_weight - limit >= weight[index]:
                  limit += weight[index]
                  # Adding profit gained for the given weight 1 ===
                  # weight[index]/weight[index]
                  gain += 1 * profit[index]
            else:
                  # Since the weight encountered is greater than limit, therefore take the
                  # required number of remaining kgs and calculate profit for it.
                  # weight remaining / weight[index]
                  gain += (max_weight - limit) / weight[index] * profit[index]
                  break
            i += 1
      return gain


if __name__ == "__main__":
     print(
         '''Input profits, weights, and then max_weight (all positive ints) separated by '''
         '''spaces.'''
     )

     snake_case				:    Union[str, Any]       				=     [int(x) for x in input('''Input profits separated by spaces: ''').split()]
     snake_case				:    Tuple       				=     [int(x) for x in input('''Input weights separated by spaces: ''').split()]
     snake_case				:    str       				=     int(input('''Max weight allowed: '''))

     # Function Call
     calc_profit(profit, weight, max_weight)





