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


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

@require_torch
class    snake_case       (   unittest.TestCase    ):




  def 						__lowercase(   self  :						List[str]  ,			a_  :						Any					)->							str:




       """simple docstring"""
       return FSMTTokenizer.from_pretrained(a_					)




  def 						__lowercase(   self  :						int  ,			a_  :						Union[str, Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		FSMTForConditionalGeneration.from_pretrained(a_					).to(a_					)
       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  :						int  ,			a_  :						Optional[int]  ,			a_  :						str					)->							List[str]:




       """simple docstring"""
       # 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__					:					Any      =		F'''facebook/wmt19-{pair}'''
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.get_tokenizer(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.get_model(a_					)

       SCREAMING_SNAKE_CASE__					:					int      =		bleu_data[pair]['src']
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		bleu_data[pair]['tgt']

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











def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     len(lowerCamelCase_							)
				lowercase__  					=     [[False] * (required_sum + 1) for _ in range(arr_len + 1							)]

				# for each arr value, a sum of zero(0) can be formed by not taking any element
				# hence True/1
				for i in range(arr_len + 1							):
								lowercase__  					=     True

				# sum is not zero and set is empty then false
				for i in range(1						,  required_sum + 1							):
								lowercase__  					=     False

				for i in range(1						,  arr_len + 1							):
								for j in range(1						,  required_sum + 1							):
												if arr[i - 1] > j:
																lowercase__  					=     subset[i - 1][j]
												if arr[i - 1] <= j:
																lowercase__  					=     subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

				return subset[arr_len][required_sum]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




