





'''simple docstring'''
from __future__ import annotations

from PIL import Image

# Define glider example
a__							:							int =[
    [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
a__							:							Any =[[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def 				lowercase__   (   __lowercase						:  list[list[int]]     ) ->      list[list[int]]:



       """simple docstring"""






       __UpperCamelCase       =  []
       for i in range(len(__lowercase     )     ):
              __UpperCamelCase       =  []
              for j in range(len(cells[i]     )     ):
                     # Get the number of live neighbours
                     __UpperCamelCase       =  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(__lowercase     ) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(__lowercase     ) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(__lowercase     ) - 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       =  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(__lowercase     )
       return next_generation
def 				lowercase__   (   __lowercase						:  list[list[int]]				,  __lowercase						:  int     ) ->      list[Image.Image]:



       """simple docstring"""






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

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

        # Save image
              images.append(__lowercase     )
              __UpperCamelCase       =  new_generation(__lowercase     )
       return images


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







'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


     def __call__(						self						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 0						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          if images is None and text is None:
               raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

          if text_encoding is not None:
               encoding_image_processor.update(_UpperCAmelCase)

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))