





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   , __a   ):
 snake_case_		:			Dict											=	cva.getAffineTransform(_A   , _A   )
 return cva.warpAffine(_A   , _A   , (rows, cols)   )


if __name__ == "__main__":
      # read original image
      _SCREAMING_SNAKE_CASE      	=		cva.imread(
          str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
      )
      # turn image in gray scale value
      _SCREAMING_SNAKE_CASE      	=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      _SCREAMING_SNAKE_CASE      	=		gray_img.shape

      # set different points to rotate image
      _SCREAMING_SNAKE_CASE      	=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
      _SCREAMING_SNAKE_CASE      	=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
      _SCREAMING_SNAKE_CASE      	=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
      _SCREAMING_SNAKE_CASE      	=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

      # add all rotated images in a list
      _SCREAMING_SNAKE_CASE      	=		[
          gray_img,
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      ]

      # plot different image rotations
      _SCREAMING_SNAKE_CASE      	=		plt.figure(1)
      _SCREAMING_SNAKE_CASE      	=		['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
      for i, image in enumerate(images):
            plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
            plt.title(titles[i])
            plt.axis("""off""")
            plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
      plt.show()








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	)

