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







@require_torch
class 							lowerCAmelCase__   ( unittest.TestCase						):

						def 			A_					(      self							,				a					)       ->   List[str]:


								'''simple docstring'''

								return FSMTTokenizer.from_pretrained(lowerCamelCase_					)

						def 			A_					(      self							,				a					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						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 			A_					(      self							,				a							,				a					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						F'facebook/wmt19-{pair}'
								_UpperCamelCase         =						self.get_tokenizer(lowerCamelCase_					)
								_UpperCamelCase         =						self.get_model(lowerCamelCase_					)

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

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






"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


if __name__ == "__main__":
  unittest.main()




