
"""simple docstring"""


import json
import os
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 AlignProcessor, EfficientNetImageProcessor







@require_vision
class  lowerCAmelCase_ (					unittest.TestCase			):



						'''simple docstring'''


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

							A        =      [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							A        =      os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES['vocab_file']					)
							with open(self.vocab_file     ,'w'     ,encoding='utf-8'					) as vocab_writer:
								vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)

							A        =      {
							    'do_resize': True,
							    'size': 20,
							    'do_center_crop': True,
							    'crop_size': 18,
							    'do_normalize': True,
							    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
							    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
							}
							A        =      os.path.join(self.tmpdirname     ,A_					)
							with open(self.image_processor_file     ,'w'     ,encoding='utf-8'					) as fp:
								json.dump(A_     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any     ,**A_   :					Optional[int]					)   ->      Dict:
							return BertTokenizer.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,**A_   :					Any					)   ->      int:
							return BertTokenizerFast.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,**A_   :					Tuple					)   ->      int:
							return EfficientNetImageProcessor.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Optional[Any]:
							shutil.rmtree(self.tmpdirname					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Optional[int]:
							A        =      [np.random.randint(255     ,size=(3, 30, 400)     ,dtype=np.uinta					)]
							A        =      [Image.fromarray(np.moveaxis(A_     ,0     ,-1					)					) for x in image_inputs]
							return image_inputs


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Tuple:
							A        =      self.get_tokenizer()
							A        =      self.get_rust_tokenizer()
							A        =      self.get_image_processor()

							A        =      AlignProcessor(tokenizer=A_     ,image_processor=A_					)
							processor_slow.save_pretrained(self.tmpdirname					)
							A        =      AlignProcessor.from_pretrained(self.tmpdirname     ,use_fast=A_					)

							A        =      AlignProcessor(tokenizer=A_     ,image_processor=A_					)
							processor_fast.save_pretrained(self.tmpdirname					)
							A        =      AlignProcessor.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     ,A_					)
							self.assertIsInstance(processor_fast.tokenizer     ,A_					)

							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     ,A_					)
							self.assertIsInstance(processor_fast.image_processor     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Union[str, Any]:
							A        =      AlignProcessor(tokenizer=self.get_tokenizer()     ,image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

							A        =      self.get_tokenizer(bos_token='(BOS)'     ,eos_token='(EOS)'					)
							A        =      self.get_image_processor(do_normalize=A_     ,padding_value=1.0					)

							A        =      AlignProcessor.from_pretrained(
							    self.tmpdirname     ,bos_token='(BOS)'     ,eos_token='(EOS)'     ,do_normalize=A_     ,padding_value=1.0					)

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

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      str:
							A        =      self.get_image_processor()
							A        =      self.get_tokenizer()

							A        =      AlignProcessor(tokenizer=A_     ,image_processor=A_					)

							A        =      self.prepare_image_inputs()

							A        =      image_processor(A_     ,return_tensors='np'					)
							A        =      processor(images=A_     ,return_tensors='np'					)

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


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

							A        =      AlignProcessor(tokenizer=A_     ,image_processor=A_					)

							A        =      'lower newer'

							A        =      processor(text=A_					)

							A        =      tokenizer(A_     ,padding='max_length'     ,max_length=64					)

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Optional[Any]:
							A        =      self.get_image_processor()
							A        =      self.get_tokenizer()

							A        =      AlignProcessor(tokenizer=A_     ,image_processor=A_					)

							A        =      'lower newer'
							A        =      self.prepare_image_inputs()

							A        =      processor(text=A_     ,images=A_					)

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

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[Any]:
							A        =      self.get_image_processor()
							A        =      self.get_tokenizer()

							A        =      AlignProcessor(tokenizer=A_     ,image_processor=A_					)

							A        =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							A        =      processor.batch_decode(A_					)
							A        =      tokenizer.batch_decode(A_					)

							self.assertListEqual(A_     ,A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      List[str]:
							A        =      self.get_image_processor()
							A        =      self.get_tokenizer()

							A        =      AlignProcessor(tokenizer=A_     ,image_processor=A_					)

							A        =      'lower newer'
							A        =      self.prepare_image_inputs()

							A        =      processor(text=A_     ,images=A_					)

							self.assertListEqual(list(inputs.keys()					)     ,processor.model_input_names					)
from __future__ import annotations

from PIL import Image

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[]
    for i in range(len(SCREAMING_SNAKE_CASE	)	):
        __UpperCamelCase     :Dict			  =		[]
        for j in range(len(cells[i]	)	):
            # Get the number of live neighbours
            __UpperCamelCase     :List[str]			  =		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(SCREAMING_SNAKE_CASE	) - 1 and j > 0:
                neighbour_count += cells[i + 1][j - 1]
            if i < len(SCREAMING_SNAKE_CASE	) - 1:
                neighbour_count += cells[i + 1][j]
            if i < len(SCREAMING_SNAKE_CASE	) - 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.
            __UpperCamelCase     :List[str]			  =		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(SCREAMING_SNAKE_CASE	)
    return next_generation



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		[]
    for _ in range(SCREAMING_SNAKE_CASE	):
        # Create output image
        __UpperCamelCase     :Dict			  =		Image.new('''RGB'''  ,			(len(cells[0]	), len(SCREAMING_SNAKE_CASE	))	)
        __UpperCamelCase     :Any			  =		img.load()

        # Save cells to image
        for x in range(len(SCREAMING_SNAKE_CASE	)	):
            for y in range(len(cells[0]	)	):
                __UpperCamelCase     :Optional[Any]			  =		255 - cells[y][x] * 255
                __UpperCamelCase     :int			  =		(colour, colour, colour)

        # Save image
        images.append(SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :Optional[int]			  =		new_generation(SCREAMING_SNAKE_CASE	)
    return images


if __name__ == "__main__":
    __lowercase							 =					generate_images(GLIDER, 16)
    images[0].save('''out.gif''', save_all=True, append_images=images[1:])





