




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer




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



				@slow
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[Any]:
							a       =     XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"				)
							model.to(snake_case_				)

							# encoder-decoder outputs
							a       =     torch.tensor([[17, 9_62_08, 10_34_71, 2]]				).to(snake_case_				)
							a       =     torch.tensor(
							    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]				).to(snake_case_				)
							a       =     model(
							    input_ids=snake_case_   , attention_mask=snake_case_   , encoder_outputs=snake_case_   , decoder_input_ids=snake_case_				)
							a       =     output[0]
							a       =     torch.Size((1, 14, 25_00_12)				)
							self.assertEqual(output_predited_logis.shape   , snake_case_				)
							a       =     torch.tensor(
							    [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]				).to(snake_case_				)
							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   , snake_case_   , atol=1e-4				)				)

							# encoder outputs
							a       =     model.prophetnet.encoder(snake_case_				)[0]
							a       =     torch.tensor(
							    [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]				).to(snake_case_				)
							a       =     torch.Size((1, 4, 10_24)				)
							self.assertEqual(encoder_outputs.shape   , snake_case_				)
							self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]   , snake_case_   , atol=1e-4				)				)

							# decoder outputs
							a       =     model.prophetnet.decoder(
							    snake_case_   , encoder_hidden_states=snake_case_   , )
							a       =     decoder_outputs[1].view(1   , model.config.ngram   , 14   , -1				)
							a       =     model.lm_head(snake_case_				)
							a       =     predicting_streams_logits[:, 0]
							self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]   , snake_case_   , atol=1e-4				)				)





				@slow
				def 				__UpperCAmelCase				( self							:	int				)   ->	List[Any]:
							a       =     XLMProphetNetForConditionalGeneration.from_pretrained(
							    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"				)
							model.to(snake_case_				)

							a       =     torch.tensor([[17, 9_62_08, 10_34_71, 2]]				).to(snake_case_				)
							a       =     torch.tensor(
							    [[2, 2_50, 99_53, 34, 6_94_89, 16_20, 32, 11_84_24, 6_24, 2_10, 1_05, 29_13, 10_32, 3_51]]				).to(snake_case_				)
							a       =     model(
							    input_ids=snake_case_   , attention_mask=snake_case_   , encoder_outputs=snake_case_   , decoder_input_ids=snake_case_				)
							a       =     output[0]
							a       =     torch.Size((1, 14, 25_00_12)				)
							self.assertEqual(output_predited_logis.shape   , snake_case_				)
							# compare the actual values for a slice.
							a       =     torch.tensor(
							    [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]				).to(snake_case_				)

							self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]   , snake_case_   , atol=1e-4				)				)





				@slow
				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	List[str]:
							a       =     XLMProphetNetForConditionalGeneration.from_pretrained(
							    "microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"				)
							model.to(snake_case_				)
							a       =     5_12

							a       =     XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"				)

							a       =     (
							    "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
							    " January 14, 2020, according to the official portal of the organization. From that day, users of this"
							    " system will not be able to receive security updates, which could make their computers vulnerable to"
							    " cyber attacks."
							)
							a       =     (
							    "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
							    " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
							    " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
							    " к кибератакам."
							)
							a       =     (
							    "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
							)

							a       =     tokenizer(
							    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]   , padding=snake_case_   , max_length=2_55   , return_tensors="pt"				).input_ids
							a       =     input_ids.to(snake_case_				)

							a       =     model.generate(
							    snake_case_   , num_beams=10   , length_penalty=1.0   , no_repeat_ngram_size=3   , early_stopping=snake_case_				)
							a       =     [tokenizer.decode(snake_case_   , skip_special_tokens=snake_case_				) for g in summary_ids]
							a       =     "Microsoft to end Windows 7 free support after January 14, 2020"
							a       =     "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
							a       =     "微软打算终止对Windows 7操作系统的免费支持"
							self.assertListEqual(
							    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]   , snake_case_   , )

							a       =     model.generate(
							    snake_case_   , num_beams=1   , length_penalty=1.0   , no_repeat_ngram_size=3   , early_stopping=snake_case_				)
							a       =     [
							    tokenizer.convert_ids_to_tokens(snake_case_   , skip_special_tokens=snake_case_				) for g in summary_ids_beama
							]
							a       =     "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" "				)
							a       =     "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
							    " "				)
							a       =     "微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(" "				)
							self.assertListEqual(
							    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]   , snake_case_   , )














"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    UpperCamelCase__								=     len(cells[0]       )  # 31
    UpperCamelCase__								=     []
    for i in range(SCREAMING_SNAKE_CASE       ):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        UpperCamelCase__								=     0 if i == 0 else cells[time][i - 1]
        UpperCamelCase__								=     0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        UpperCamelCase__								=     7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,						2       )
        next_generation.append(rule[situation]       )
    return next_generation







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


if __name__ == "__main__":
  A__       :			Optional[int]=						bin(int(input("""Rule:\n""").strip()))[2:]
  A__       :			str=						format_ruleset(int(rule_num))
  for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
  A__       :			Tuple=						generate_image(CELLS)
  # Uncomment to save the image
  # img.save(f"rule_{rule_num}.png")
  img.show()




