






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						:			Union[str, Any]     	=					json.load(f)
@require_torch
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     int						, __lowercase   :     List[Any]   )				-> Tuple:
        return FSMTTokenizer.from_pretrained(__lowercase   )
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     Any   )				-> Any:
        __UpperCAmelCase				:      Any	 =    FSMTForConditionalGeneration.from_pretrained(__lowercase   ).to(__lowercase   )
        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       UpperCAmelCase ( self   :     List[str]						, __lowercase   :     List[Any]						, __lowercase   :     Optional[int]   )				-> List[str]:
        # 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
        __UpperCAmelCase				:      Dict	 =    f"""facebook/wmt19-{pair}"""
        __UpperCAmelCase				:      Union[str, Any]	 =    self.get_tokenizer(__lowercase   )
        __UpperCAmelCase				:      Any	 =    self.get_model(__lowercase   )

        __UpperCAmelCase				:      Optional[Any]	 =    bleu_data[pair]["""src"""]
        __UpperCAmelCase				:      Optional[Any]	 =    bleu_data[pair]["""tgt"""]

        __UpperCAmelCase				:      Dict	 =    tokenizer(__lowercase						, return_tensors="""pt"""						, truncation=__lowercase						, padding="""longest"""   ).to(__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    model.generate(
            input_ids=batch.input_ids						, num_beams=8						, )
        __UpperCAmelCase				:      Optional[Any]	 =    tokenizer.batch_decode(
            __lowercase						, skip_special_tokens=__lowercase						, clean_up_tokenization_spaces=__lowercase   )
        __UpperCAmelCase				:      Tuple	 =    calculate_bleu(__lowercase						, __lowercase   )
        print(__lowercase   )
        self.assertGreaterEqual(scores["""bleu"""]						, __lowercase   )




"""simple docstring"""






def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) != len(__lowercase	):
  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.
 UpperCamelCase_     :					int   	=[p / w for p, w in zip(__lowercase						,	__lowercase	)]

 # Creating a copy of the list and sorting profit/weight in ascending order
 UpperCamelCase_     :					Optional[int]   	=sorted(__lowercase	)

 # declaring useful variables
 UpperCamelCase_     :					Optional[int]   	=len(__lowercase	)
 UpperCamelCase_     :					Optional[int]   	=0
 UpperCamelCase_     :					List[Any]   	=0
 UpperCamelCase_     :					Optional[int]   	=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
  UpperCamelCase_     :					List[str]   	=sorted_profit_by_weight[length - i - 1]
  UpperCamelCase_     :					Optional[Any]   	=profit_by_weight.index(__lowercase	)
  UpperCamelCase_     :					str   	=-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.'
 )

 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input profits separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input weights separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)
