from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__SCREAMING_SNAKE_CASE						:    Optional[int]     = [[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 					snake_case						(__lowercase						)       ->       list[int]:

						'''simple docstring'''



						return [int(__lowercase						) for c in F"""{ruleset:08}"""[:8]]
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       list[int]:

						'''simple docstring'''



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

						'''simple docstring'''



						_snake_case						:     str				=    Image.new("RGB"					,			(len(cells[0]						), len(__lowercase						))						)
						_snake_case						:     int				=    img.load()
						# Generates image
						for w in range(img.width						):
												for h in range(img.height						):
																		_snake_case						:     List[Any]				=    255 - int(255 * cells[h][w]						)
																		_snake_case						:     Union[str, Any]				=    (color, color, color)
						return img


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Any     = bin(int(input('Rule:\n').strip()))[2:]
				__SCREAMING_SNAKE_CASE						:    Tuple     = format_ruleset(int(rule_num))
				for time in range(1_6):
								CELLS.append(new_generation(CELLS, rule, time))
				__SCREAMING_SNAKE_CASE						:    List[Any]     = generate_image(CELLS)
				# Uncomment to save the image
				# img.save(f"rule_{rule_num}.png")
				img.show()






import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import FlavaImageProcessor, FlavaProcessor
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )





@require_vision
class 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		str  			=							tempfile.mkdtemp()

            # fmt: off
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
            # fmt: on
            _SCREAMING_SNAKE_CASE				:		int  			=							os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]							)

            with open(self.vocab_file			,  "w"			,  encoding="utf-8"							) as fp:
                 fp.write("".join([x + "\n" for x in vocab_tokens]							)							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							{
                "image_mean": FLAVA_IMAGE_MEAN,
                "image_std": FLAVA_IMAGE_STD,
                "do_normalize": True,
                "do_resize": True,
                "size": 2_2_4,
                "do_center_crop": True,
                "crop_size": 2_2_4,
                "input_size_patches": 1_4,
                "total_mask_patches": 7_5,
                "mask_group_max_patches": None,
                "mask_group_min_patches": 1_6,
                "mask_group_min_aspect_ratio": 0.3,
                "mask_group_max_aspect_ratio": None,
                "codebook_do_resize": True,
                "codebook_size": 1_1_2,
                "codebook_do_center_crop": True,
                "codebook_crop_size": 1_1_2,
                "codebook_do_map_pixels": True,
                "codebook_do_normalize": True,
                "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
                "codebook_image_std": FLAVA_CODEBOOK_STD,
            }

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							os.path.join(self.tmpdirname			,  __lowerCamelCase							)
            with open(self.image_processor_file			,  "w"			,  encoding="utf-8"							) as fp:
                 json.dump(__lowerCamelCase			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   List[str]:
            return BertTokenizer.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Optional[Any]:
            return BertTokenizerFast.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Tuple:
            return FlavaImageProcessor.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   int:
            shutil.rmtree(self.tmpdirname							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[np.random.randint(2_5_5			,  size=(3, 3_0, 4_0_0)			,  dtype=np.uinta							)]

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for x in image_inputs]

            return image_inputs




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_rust_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_image_processor()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_slow.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							FlavaProcessor.from_pretrained(self.tmpdirname			,  use_fast=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_fast.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor.from_pretrained(self.tmpdirname							)

            self.assertEqual(processor_slow.tokenizer.get_vocab()			,  tokenizer_slow.get_vocab()							)
            self.assertEqual(processor_fast.tokenizer.get_vocab()			,  tokenizer_fast.get_vocab()							)
            self.assertEqual(tokenizer_slow.get_vocab()			,  tokenizer_fast.get_vocab()							)
            self.assertIsInstance(processor_slow.tokenizer			,  __lowerCamelCase							)
            self.assertIsInstance(processor_fast.tokenizer			,  __lowerCamelCase							)

            self.assertEqual(processor_slow.image_processor.to_json_string()			,  image_processor.to_json_string()							)
            self.assertEqual(processor_fast.image_processor.to_json_string()			,  image_processor.to_json_string()							)
            self.assertIsInstance(processor_slow.image_processor			,  __lowerCamelCase							)
            self.assertIsInstance(processor_fast.image_processor			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()							)
            processor.save_pretrained(self.tmpdirname							)

            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_tokenizer(bos_token="(BOS)"			,  eos_token="(EOS)"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_image_processor(do_normalize=__lowerCamelCase			,  padding_value=1.0							)

            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor.from_pretrained(
                self.tmpdirname			,  bos_token="(BOS)"			,  eos_token="(EOS)"			,  do_normalize=__lowerCamelCase			,  padding_value=1.0							)

            self.assertEqual(processor.tokenizer.get_vocab()			,  tokenizer_add_kwargs.get_vocab()							)
            self.assertIsInstance(processor.tokenizer			,  __lowerCamelCase							)

            self.assertEqual(processor.image_processor.to_json_string()			,  image_processor_add_kwargs.to_json_string()							)
            self.assertIsInstance(processor.image_processor			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Any  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		int  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processor(__lowerCamelCase			,  return_tensors="np"							)
            _SCREAMING_SNAKE_CASE				:		str  			=							processor(images=__lowerCamelCase			,  return_tensors="np"							)

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2							)

            # With rest of the args
            random.seed(1_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_processor(
                __lowerCamelCase			,  return_image_mask=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_tensors="np"							)
            random.seed(1_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							processor(
                images=__lowerCamelCase			,  return_image_mask=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_tensors="np"							)

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()			,  input_processor[key].sum()			,  delta=1E-2							)




       def 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"lower newer"

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							processor(text=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							tokenizer(__lowerCamelCase							)

            for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]			,  encoded_processor[key]							)




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							"lower newer"
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

            self.assertListEqual(list(inputs.keys()							)			,  ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]							)

            # add extra args
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_image_mask=__lowerCamelCase							)

            self.assertListEqual(
                list(inputs.keys()							)			,  [
                    "input_ids",
                    "token_type_ids",
                    "attention_mask",
                    "pixel_values",
                    "codebook_pixel_values",
                    "bool_masked_pos",
                ]			,  )

            # test if it raises when no input is passed
            with pytest.raises(__lowerCamelCase							):
                 processor()




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _SCREAMING_SNAKE_CASE				:		Dict  			=							processor.batch_decode(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							tokenizer.batch_decode(__lowerCamelCase							)

            self.assertListEqual(__lowerCamelCase			,  __lowerCamelCase							)







       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		int  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"lower newer"
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

            self.assertListEqual(list(inputs.keys()							)			,  processor.model_input_names							)