




'''simple docstring'''



from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_							:	Optional[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
UpperCAmelCase_							:	str	     =		[[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def       _UpperCamelCase			(_lowerCamelCase				:  list[list[int]]						)-> list[list[int]]:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  list[list[int]] ,						_lowerCamelCase				:  int						)-> list[Image.Image]:




     '''simple docstring'''





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

          # Save cells to image
          for x in range(len(_lowerCamelCase						)						):
               for y in range(len(cells[0]						)						):
                    __snake_case            =	2_55 - cells[y][x] * 2_55
                    __snake_case            =	(colour, colour, colour)

        # Save image
          images.append(_lowerCamelCase						)
          __snake_case            =	new_generation(_lowerCamelCase						)
     return images


if __name__ == "__main__":
     UpperCAmelCase_							:	Dict	     =		generate_images(GLIDER, 1_6)
     images[0].save('''out.gif''', save_all=True, append_images=images[1:])












'''simple docstring'''



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)





@add_end_docstrings(__lowerCAmelCase)
class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            requires_backends(self     ,	'''vision'''	)
            self.check_model_type(
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
                if self.framework == '''tf'''
                else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING	)


       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            return super().__call__(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	{}
            if "candidate_labels" in kwargs:
                 __snake_case            =	kwargs['''candidate_labels''']
            if "hypothesis_template" in kwargs:
                 __snake_case            =	kwargs['''hypothesis_template''']

            return preprocess_params, {}, {}


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="This is a photo of {}."	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	load_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.image_processor(images=[image]     ,	return_tensors=self.framework	)
            __snake_case            =	candidate_labels
            __snake_case            =	[hypothesis_template.format(__SCREAMING_SNAKE_CASE	) for x in candidate_labels]
            __snake_case            =	self.tokenizer(__SCREAMING_SNAKE_CASE     ,	return_tensors=self.framework     ,	padding=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[text_inputs]
            return inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	model_inputs.pop('''candidate_labels'''	)
            __snake_case            =	model_inputs.pop('''text_inputs'''	)
            if isinstance(text_inputs[0]     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	text_inputs[0]
            else:
                 # Batching case.
                 __snake_case            =	text_inputs[0][0]

            __snake_case            =	self.model(**__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	{
                '''candidate_labels''': candidate_labels,
                '''logits''': outputs.logits_per_image,
            }
            return model_outputs




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	model_outputs.pop('''candidate_labels'''	)
            __snake_case            =	model_outputs['''logits'''][0]
            if self.framework == "pt":
                 __snake_case            =	logits.softmax(dim=-1	).squeeze(-1	)
                 __snake_case            =	probs.tolist()
                 if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case            =	[scores]
            elif self.framework == "tf":
                 __snake_case            =	stable_softmax(__SCREAMING_SNAKE_CASE     ,	axis=-1	)
                 __snake_case            =	probs.numpy().tolist()
            else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''	)

            __snake_case            =	[
                {'''score''': score, '''label''': candidate_label}
                for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)     ,	key=lambda __SCREAMING_SNAKE_CASE	: -x[0]	)
            ]
            return result






