

'''simple docstring'''






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 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     Tuple			):
   return FSMTTokenizer.from_pretrained(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     Optional[int]			):
   __lowercase							  = FSMTForConditionalGeneration.from_pretrained(lowercase__			).to(lowercase__			)
   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 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     str					,lowercase__   :     List[str]			):
   # 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
   __lowercase							  = F"facebook/wmt19-{pair}"
   __lowercase							  = self.get_tokenizer(lowercase__			)
   __lowercase							  = self.get_model(lowercase__			)

   __lowercase							  = bleu_data[pair]['''src''']
   __lowercase							  = bleu_data[pair]['''tgt''']

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




'''simple docstring'''






from __future__ import annotations

from decimal import Decimal

from numpy import array


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = Decimal

 # Check if the provided matrix has 2 rows and 2 columns
 # since this implementation only works for 2x2 matrices
 if len(A__      ) == 2 and len(matrix[0]      ) == 2 and len(matrix[1]      ) == 2:
  # Calculate the determinant of the matrix
  __lowercase							  = float(
      d(matrix[0][0]      ) * d(matrix[1][1]      ) - d(matrix[1][0]      ) * d(matrix[0][1]      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creates a copy of the matrix with swapped positions of the elements
  __lowercase							  = [[0.0, 0.0], [0.0, 0.0]]
  __lowercase  ,	__lowercase							  = matrix[1][1], matrix[0][0]
  __lowercase  ,	__lowercase							  = -matrix[1][0], -matrix[0][1]

  # Calculate the inverse of the matrix
  return [
      [(float(d(A__      )      ) / determinant) or 0.0 for n in row] for row in swapped_matrix
  ]
 elif (
     len(A__      ) == 3
     and len(matrix[0]      ) == 3
     and len(matrix[1]      ) == 3
     and len(matrix[2]      ) == 3
 ):
  # Calculate the determinant of the matrix using Sarrus rule
  __lowercase							  = float(
      (
          (d(matrix[0][0]      ) * d(matrix[1][1]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][2]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][2]      ) * d(matrix[1][0]      ) * d(matrix[2][1]      ))
      )
      - (
          (d(matrix[0][2]      ) * d(matrix[1][1]      ) * d(matrix[2][0]      ))
          + (d(matrix[0][1]      ) * d(matrix[1][0]      ) * d(matrix[2][2]      ))
          + (d(matrix[0][0]      ) * d(matrix[1][2]      ) * d(matrix[2][1]      ))
      )      )
  if determinant == 0:
   raise ValueError('''This matrix has no inverse.'''      )

  # Creating cofactor matrix
  __lowercase							  = [
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
      [d(0.0      ), d(0.0      ), d(0.0      )],
  ]
  __lowercase							  = (d(matrix[1][1]      ) * d(matrix[2][2]      )) - (
      d(matrix[1][2]      ) * d(matrix[2][1]      )
  )
  __lowercase							  = -(
      (d(matrix[1][0]      ) * d(matrix[2][2]      )) - (d(matrix[1][2]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[1][0]      ) * d(matrix[2][1]      )) - (
      d(matrix[1][1]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][1]      ) * d(matrix[2][2]      )) - (d(matrix[0][2]      ) * d(matrix[2][1]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[2][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[2][1]      )) - (d(matrix[0][1]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[0][1]      ) * d(matrix[1][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[1][1]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[1][2]      )) - (d(matrix[0][2]      ) * d(matrix[1][0]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[1][1]      )) - (
      d(matrix[0][1]      ) * d(matrix[1][0]      )
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    __lowercase							  = cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    inverse_matrix[i][j] /= d(A__      )

        # Calculate the inverse of the matrix
  return [[float(d(A__      )      ) or 0.0 for n in row] for row in inverse_matrix]
 raise ValueError('''Please provide a matrix of size 2x2 or 3x3.'''      )

