





import sys
import turtle


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
 return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , ):
 my_pen.up()
 my_pen.goto(vertexa[0]   , vertexa[1]   )
 my_pen.down()
 my_pen.goto(vertexa[0]   , vertexa[1]   )
 my_pen.goto(vertexa[0]   , vertexa[1]   )
 my_pen.goto(vertexa[0]   , vertexa[1]   )

 if depth == 0:
  return

 triangle(lowerCAmelCase__   , get_mid(lowerCAmelCase__   , lowerCAmelCase__   )   , get_mid(lowerCAmelCase__   , lowerCAmelCase__   )   , depth - 1   )
 triangle(lowerCAmelCase__   , get_mid(lowerCAmelCase__   , lowerCAmelCase__   )   , get_mid(lowerCAmelCase__   , lowerCAmelCase__   )   , depth - 1   )
 triangle(lowerCAmelCase__   , get_mid(lowerCAmelCase__   , lowerCAmelCase__   )   , get_mid(lowerCAmelCase__   , lowerCAmelCase__   )   , depth - 1   )


if __name__ == "__main__":
      if len(sys.argv) != 2:
            raise ValueError(
                """Correct format for using this script: """
                """python fractals.py <int:depth_for_fractal>"""
            )
      _SCREAMING_SNAKE_CASE      	=		turtle.Turtle()
      my_pen.ht()
      my_pen.speed(5)
      my_pen.pencolor("""red""")

      _SCREAMING_SNAKE_CASE      	=		[(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
      triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))








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      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

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

