'''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


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





@require_torch
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	int)      ->					int:
         return FSMTTokenizer.from_pretrained(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	int)      ->					Any:
         lowercase_     =      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				:	Optional[Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Dict)      ->					List[Any]:
         # 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
         lowercase_     =      F'facebook/wmt19-{pair}'
         lowercase_     =      self.get_tokenizer(__lowerCAmelCase)
         lowercase_     =      self.get_model(__lowerCAmelCase)

         lowercase_     =      bleu_data[pair]["src"]
         lowercase_     =      bleu_data[pair]["tgt"]

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







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # We need to create solution object to save path.
      lowercase_     =      [[0 for _ in range(__lowerCamelCase				)] for _ in range(__lowerCamelCase				)]
      lowercase_     =      run_maze(__lowerCamelCase ,		0 ,		0 ,		__lowerCamelCase				)
      if solved:
            print("\n".join(str(__lowerCamelCase				) for row in solutions				)				)
      else:
            print("No solution exists!"				)
      return solved


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						list[list[int]]				)	->							bool:
      '''simple docstring'''


      lowercase_     =      len(__lowerCamelCase				)
      # Final check point.
      if i == j == (size - 1):
            lowercase_     =      1
            return True

      lowercase_     =      (not i < 0) and (not j < 0)  # Check lower bounds
      lowercase_     =      (i < size) and (j < size)  # Check upper bounds

      if lower_flag and upper_flag:
            # check for already visited and block points.
            lowercase_     =      (not solutions[i][j]) and (not maze[i][j])
            if block_flag:
                  # check visited
                  lowercase_     =      1

                  # check for directions
                  if (
                      run_maze(__lowerCamelCase ,		i + 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j + 1 ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		i - 1 ,		__lowerCamelCase ,		__lowerCamelCase				)
                      or run_maze(__lowerCamelCase ,		__lowerCamelCase ,		j - 1 ,		__lowerCamelCase				)
                  ):
                        return True

                  lowercase_     =      0
                  return False
      return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()






