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


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






@require_torch
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Union[str, Any]:
     return FSMTTokenizer.from_pretrained(lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Any=      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 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					int:
     # 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
     SCREAMING_SNAKE_CASE__:   List[Any]=      f'facebook/wmt19-{pair}'
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.get_tokenizer(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.get_model(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      bleu_data[pair]['''src''']
     SCREAMING_SNAKE_CASE__:   str=      bleu_data[pair]['''tgt''']

     SCREAMING_SNAKE_CASE__:   Tuple=      tokenizer(lowerCAmelCase , return_tensors='''pt''' , truncation=lowerCAmelCase , padding='''longest'''   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      model.generate(
         input_ids=batch.input_ids , num_beams=8 , )
     SCREAMING_SNAKE_CASE__:   Tuple=      tokenizer.batch_decode(
         lowerCAmelCase , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      calculate_bleu(lowerCAmelCase , lowerCAmelCase   )
     print(lowerCAmelCase   )
     self.assertGreaterEqual(scores['''bleu'''] , lowerCAmelCase   )


import os







def 			A__      (	snake_case_ :		str = "matrix.txt"      ):
 with open(os.path.join(os.path.dirname(snake_case_      )       ,				snake_case_      )      ) as in_file:
  SCREAMING_SNAKE_CASE__:   Dict=      in_file.read()

 SCREAMING_SNAKE_CASE__:   Optional[int]=      [[int(snake_case_      ) for cell in row.split(''','''      )] for row in data.strip().splitlines()]
 SCREAMING_SNAKE_CASE__:   int=      [[0 for cell in row] for row in grid]
 SCREAMING_SNAKE_CASE__:   Dict=      len(grid[0]      )

 SCREAMING_SNAKE_CASE__:   Dict=      [[0 for i in range(snake_case_      )] for j in range(snake_case_      )]
 SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][0]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][i] + dp[0][i - 1]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[Any]=      grid[i][0] + dp[i - 1][0]

 for i in range(1       ,				snake_case_      ):
  for j in range(1       ,				snake_case_      ):
   SCREAMING_SNAKE_CASE__:   Dict=      grid[i][j] + min(dp[i - 1][j]       ,				dp[i][j - 1]      )

 return dp[-1][-1]


if __name__ == "__main__":
      print(f'''{solution() = }''')

