


'''simple docstring'''


from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_						:  Dict				  =						[
    [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_						:  List[Any]				  =						[[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= []
				for i in range(len(__a	)	):
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= []
								for j in range(len(cells[i]	)	):
												# Get the number of live neighbours
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= 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(__a	) - 1 and j > 0:
																neighbour_count += cells[i + 1][j - 1]
												if i < len(__a	) - 1:
																neighbour_count += cells[i + 1][j]
												if i < len(__a	) - 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.
												_SCREAMING_SNAKE_CASE					:			Dict							= 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(__a	)
				return next_generation







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= []
				for _ in range(__a	):
								# Create output image
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= Image.new("""RGB"""				, (len(cells[0]	), len(__a	))	)
								_SCREAMING_SNAKE_CASE					:			Tuple							= img.load()

								# Save cells to image
								for x in range(len(__a	)	):
												for y in range(len(cells[0]	)	):
																_SCREAMING_SNAKE_CASE					:			Dict							= 255 - cells[y][x] * 255
																_SCREAMING_SNAKE_CASE					:			Any							= (colour, colour, colour)

        # Save image
								images.append(__a	)
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= new_generation(__a	)
				return images


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








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =tempfile.mkdtemp()

											a__      =BlipImageProcessor()
											a__      =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

											a__      =BlipProcessor(lowercase_       ,  lowercase_)

											processor.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> str:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).tokenizer





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> List[str]:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).image_processor





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											shutil.rmtree(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =[np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta)]

											a__      =[Image.fromarray(np.moveaxis(lowercase_       ,  0       ,  -1)) for x in image_inputs]

											return image_inputs





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =BlipProcessor(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=lowercase_       ,  padding_value=1.0)

											a__      =BlipProcessor.from_pretrained(
											    self.tmpdirname       ,  bos_token='(BOS)'       ,  eos_token='(EOS)'       ,  do_normalize=lowercase_       ,  padding_value=1.0)

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

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =self.prepare_image_inputs()

											a__      =image_processor(lowercase_       ,  return_tensors='np')
											a__      =processor(images=lowercase_       ,  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)  -> List[str]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'

											a__      =processor(text=lowercase_)

											a__      =tokenizer(lowercase_       ,  return_token_type_ids=lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor(text=lowercase_       ,  images=lowercase_)

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

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





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor.batch_decode(lowercase_)
											a__      =tokenizer.batch_decode(lowercase_)

											self.assertListEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

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

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

