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


A   :     List[Any]       =				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
      A   :     str       =				json.load(f)


@require_torch
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Any     ,   __magic_name__       :				Dict      )     ->		int:
        return FSMTTokenizer.from_pretrained(__magic_name__      )







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				Optional[Any]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     FSMTForConditionalGeneration.from_pretrained(__magic_name__      ).to(__magic_name__      )
        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       :				Any     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				int      )     ->		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
        SCREAMING_SNAKE_CASE_      =     F'''facebook/wmt19-{pair}'''
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.get_model(__magic_name__      )

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

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



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


