


from __future__ import annotations

from PIL import Image

# Define glider example
lowerCAmelCase__      		= [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
lowerCAmelCase__      		= [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[list[int]]						):
       _A      :       Tuple				    = []
       for i in range(len(UpperCamelCase__						)						):
              _A      :       Tuple				    = []
              for j in range(len(cells[i]						)						):
                     # Get the number of live neighbours
                     _A      :       Tuple				    = 0
                     if i > 0 and j > 0:
                            neighbour_count += cells[i - 1][j - 1]
                     if i > 0:
                            neighbour_count += cells[i - 1][j]
                     if i > 0 and j < len(cells[i]						) - 1:
                            neighbour_count += cells[i - 1][j + 1]
                     if j > 0:
                            neighbour_count += cells[i][j - 1]
                     if j < len(cells[i]						) - 1:
                            neighbour_count += cells[i][j + 1]
                     if i < len(UpperCamelCase__						) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(UpperCamelCase__						) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(UpperCamelCase__						) - 1 and j < len(cells[i]						) - 1:
                            neighbour_count += cells[i + 1][j + 1]

                     # Rules of the game of life (excerpt from Wikipedia):
                     # 1. Any live cell with two or three live neighbours survives.
                     # 2. Any dead cell with three live neighbours becomes a live cell.
                     # 3. All other live cells die in the next generation.
                     #    Similarly, all other dead cells stay dead.
                     _A      :       List[Any]				    = cells[i][j] == 1
                     if (
                         (alive and 2 <= neighbour_count <= 3)
                         or not alive
                         and neighbour_count == 3
                     ):
                            next_generation_row.append(1						)
                     else:
                            next_generation_row.append(0						)

              next_generation.append(UpperCamelCase__						)
       return next_generation
def 							_UpperCAmelCase				(UpperCamelCase__     :  list[list[int]]				,							UpperCamelCase__     :  int						):
       _A      :       Union[str, Any]				    = []
       for _ in range(UpperCamelCase__						):
              # Create output image
              _A      :       Optional[int]				    = Image.new("RGB"				,							(len(cells[0]						), len(UpperCamelCase__						))						)
              _A      :       Optional[Any]				    = img.load()

              # Save cells to image
              for x in range(len(UpperCamelCase__						)						):
                     for y in range(len(cells[0]						)						):
                            _A      :       Dict				    = 255 - cells[y][x] * 255
                            _A      :       List[str]				    = (colour, colour, colour)

        # Save image
              images.append(UpperCamelCase__						)
              _A      :       List[Any]				    = new_generation(UpperCamelCase__						)
       return images


if __name__ == "__main__":
    lowerCAmelCase__      		= generate_images(GLIDER, 16)
    images[0].save('out.gif', save_all=True, append_images=images[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


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



@require_torch
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						return FSMTTokenizer.from_pretrained(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

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

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

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