"""simple docstring"""






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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							tempfile.mkdtemp()

									UpperCAmelCase__             =							BlipImageProcessor()
									UpperCAmelCase__             =							GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'			)

									UpperCAmelCase__             =							BlipaProcessor(UpperCamelCase_    ,UpperCamelCase_			)

									processor.save_pretrained(self.tmpdirname			)







					def 		__lowerCAmelCase						(				self   :	Dict    ,**lowerCamelCase__   :	Tuple			):
									return AutoProcessor.from_pretrained(self.tmpdirname    ,**UpperCamelCase_			).tokenizer







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,**lowerCamelCase__   :	Optional[int]			):
									return AutoProcessor.from_pretrained(self.tmpdirname    ,**UpperCamelCase_			).image_processor







					def 		__lowerCAmelCase						(				self   :	Any			):
									shutil.rmtree(self.tmpdirname			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							[np.random.randint(255    ,size=(3, 30, 400)    ,dtype=np.uinta			)]

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

									return image_inputs







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							BlipaProcessor(tokenizer=self.get_tokenizer()    ,image_processor=self.get_image_processor()			)
									processor.save_pretrained(self.tmpdirname			)

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

									UpperCAmelCase__             =							BlipaProcessor.from_pretrained(
									    self.tmpdirname    ,bos_token='(BOS)'    ,eos_token='(EOS)'    ,do_normalize=UpperCamelCase_    ,padding_value=1.0			)

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

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







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							self.get_image_processor()
									UpperCAmelCase__             =							self.get_tokenizer()

									UpperCAmelCase__             =							BlipaProcessor(tokenizer=UpperCamelCase_    ,image_processor=UpperCamelCase_			)

									UpperCAmelCase__             =							self.prepare_image_inputs()

									UpperCAmelCase__             =							image_processor(UpperCamelCase_    ,return_tensors='np'			)
									UpperCAmelCase__             =							processor(images=UpperCamelCase_    ,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 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							self.get_image_processor()
									UpperCAmelCase__             =							self.get_tokenizer()

									UpperCAmelCase__             =							BlipaProcessor(tokenizer=UpperCamelCase_    ,image_processor=UpperCamelCase_			)

									UpperCAmelCase__             =							'lower newer'

									UpperCAmelCase__             =							processor(text=UpperCamelCase_			)

									UpperCAmelCase__             =							tokenizer(UpperCamelCase_    ,return_token_type_ids=UpperCamelCase_			)

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







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							self.get_image_processor()
									UpperCAmelCase__             =							self.get_tokenizer()

									UpperCAmelCase__             =							BlipaProcessor(tokenizer=UpperCamelCase_    ,image_processor=UpperCamelCase_			)

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

									UpperCAmelCase__             =							processor(text=UpperCamelCase_    ,images=UpperCamelCase_			)

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

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







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							self.get_image_processor()
									UpperCAmelCase__             =							self.get_tokenizer()

									UpperCAmelCase__             =							BlipaProcessor(tokenizer=UpperCamelCase_    ,image_processor=UpperCamelCase_			)

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

									UpperCAmelCase__             =							processor.batch_decode(UpperCamelCase_			)
									UpperCAmelCase__             =							tokenizer.batch_decode(UpperCamelCase_			)

									self.assertListEqual(UpperCamelCase_    ,UpperCamelCase_			)







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							self.get_image_processor()
									UpperCAmelCase__             =							self.get_tokenizer()

									UpperCAmelCase__             =							BlipaProcessor(tokenizer=UpperCamelCase_    ,image_processor=UpperCamelCase_			)

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

									UpperCAmelCase__             =							processor(text=UpperCamelCase_    ,images=UpperCamelCase_			)

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









from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [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_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						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.
                     __lowerCamelCase   =						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 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



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

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

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


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

