



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

from PIL import Image

# Define glider example
snake_case_					:      str    			=    [
    [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
snake_case_					:      List[str]    			=    [[0, 1, 0], [0, 1, 0], [0, 1, 0]]






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						List[str]												=  []
						for i in range(len(UpperCAmelCase_				)				):
												_UpperCamelCase   :						List[Any]												=  []
												for j in range(len(cells[i]				)				):
																		# Get the number of live neighbours
																		_UpperCamelCase   :						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(UpperCAmelCase_				) - 1 and j > 0:
																								neighbour_count += cells[i + 1][j - 1]
																		if i < len(UpperCAmelCase_				) - 1:
																								neighbour_count += cells[i + 1][j]
																		if i < len(UpperCAmelCase_				) - 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   :						int												=  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(UpperCAmelCase_				)
						return next_generation






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  []
						for _ in range(UpperCAmelCase_				):
												# Create output image
												_UpperCamelCase   :						str												=  Image.new('RGB'    ,    (len(cells[0]				), len(UpperCAmelCase_				))				)
												_UpperCamelCase   :						Any												=  img.load()

												# Save cells to image
												for x in range(len(UpperCAmelCase_				)				):
																		for y in range(len(cells[0]				)				):
																								_UpperCamelCase   :						List[Any]												=  2_5_5 - cells[y][x] * 2_5_5
																								_UpperCamelCase   :						Any												=  (colour, colour, colour)

        # Save image
												images.append(UpperCAmelCase_				)
												_UpperCamelCase   :						Optional[Any]												=  new_generation(UpperCAmelCase_				)
						return images


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







'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor






class 				lowercase__      (				unittest.TestCase  ):





	def __init__(					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[Any]=13					,lowerCamelCase__   :							List[Any]=3					,lowerCamelCase__   :							Optional[Any]=224					,lowerCamelCase__   :							int=30					,lowerCamelCase__   :							str=400					,lowerCamelCase__   :							str=True					,lowerCamelCase__   :							Any=None					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							Tuple=[0.5, 0.5, 0.5]					,lowerCamelCase__   :							Optional[int]=[0.5, 0.5, 0.5]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  size if size is not None else {'height': 18, 'width': 18}
							_UpperCamelCase   :						List[Any]												=  parent
							_UpperCamelCase   :						Union[str, Any]												=  batch_size
							_UpperCamelCase   :						Dict												=  num_channels
							_UpperCamelCase   :						List[str]												=  image_size
							_UpperCamelCase   :						Optional[Any]												=  min_resolution
							_UpperCamelCase   :						Dict												=  max_resolution
							_UpperCamelCase   :						Tuple												=  do_resize
							_UpperCamelCase   :						Optional[int]												=  size
							_UpperCamelCase   :						Tuple												=  do_normalize
							_UpperCamelCase   :						str												=  image_mean
							_UpperCamelCase   :						int												=  image_std





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}






@require_torch
@require_vision
class 				lowercase__      (				lowercase		,   unittest.TestCase  ):
	lowercase__    							=     ViTImageProcessor if is_vision_available() else None





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  EfficientFormerImageProcessorTester(self					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self.image_proc_tester.prepare_image_processor_dict()





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_mean'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'image_std'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_normalize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'do_resize'					)					)
							self.assertTrue(hasattr(lowerCamelCase__					,'size'					)					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						str												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							_UpperCamelCase   :						Optional[Any]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,Image.Image					)

							# Test not batched input
							_UpperCamelCase   :						int												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						int												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							_UpperCamelCase   :						Optional[int]												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,numpify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,np.ndarray					)

							# Test not batched input
							_UpperCamelCase   :						Union[str, Any]												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Optional[Any]												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							# Initialize image_processor
							_UpperCamelCase   :						Any												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							_UpperCamelCase   :						Any												=  prepare_image_inputs(self.image_proc_tester					,equal_resolution=lowerCamelCase__					,torchify=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,torch.Tensor					)

							# Test not batched input
							_UpperCamelCase   :						Dict												=  image_processor(image_inputs[0]					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)

							# Test batched
							_UpperCamelCase   :						Any												=  image_processor(lowerCamelCase__					,return_tensors='pt'					).pixel_values
							self.assertEqual(
							    encoded_images.shape					,(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['height'],
							        self.image_proc_tester.size['width'],
							    )					,)


