



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class 	lowerCamelCase__ :


						def __init__( self						:  List[str]      ,       _a						:  int      ,       _a						:  int			):
													a__:       int											=0
													# max greyscale value for #FFFFFF
													a__:       Tuple											=int(self.get_greyscale(2_5_5      ,       2_5_5      ,       2_5_5			)			)

													if not self.min_threshold < threshold < self.max_threshold:
																				a__:       Tuple											=F"Factor value should be from 0 to {self.max_threshold}"
																				raise ValueError(_a			)

													a__:       int											=input_img
													a__:       Optional[int]											=threshold
													a__							,			a__:       List[str]											=self.input_img.shape[1], self.input_img.shape[0]

													# error table size (+4 columns and +1 row) greater than input image because of
													# lack of if statements
													a__:       Tuple											=[
													    [0 for _ in range(self.height + 4			)] for __ in range(self.width + 1			)
													]
													a__:       List[str]											=np.ones((self.width, self.height, 3)      ,       np.uinta			) * 2_5_5


						@classmethod
						def   _lowerCamelCase			( cls						:  List[Any]      ,       _a						:  int      ,       _a						:  int      ,       _a						:  int			):
													return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







						def   _lowerCamelCase			( self						:  Any			):
													for y in range(self.height			):
																				for x in range(self.width			):
																											a__:       int											=int(self.get_greyscale(*self.input_img[y][x]			)			)
																											if self.threshold > greyscale + self.error_table[y][x]:
																																		a__:       Tuple											=(0, 0, 0)
																																		a__:       Dict											=greyscale + self.error_table[x][y]
																											else:
																																		a__:       Union[str, Any]											=(2_5_5, 2_5_5, 2_5_5)
																																		a__:       Union[str, Any]											=greyscale + self.error_table[x][y] - 2_5_5
																											self.error_table[y][x + 1] += int(8 / 3_2 * current_error			)
																											self.error_table[y][x + 2] += int(4 / 3_2 * current_error			)
																											self.error_table[y + 1][x] += int(8 / 3_2 * current_error			)
																											self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error			)
																											self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error			)
																											self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error			)
																											self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error			)


if __name__ == "__main__":
		# create Burke's instances with original images in greyscale
		__UpperCAmelCase						  =     [
		    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
		    for threshold in (1, 1_26, 1_30, 1_40)
		]

		for burkes in burkes_instances:
				burkes.process()

		for burkes in burkes_instances:
				imshow(
				    f"""Original image with dithering threshold: {burkes.threshold}""",
				    burkes.output_img,
				)

		waitKey(0)
		destroyAllWindows()










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase						  =     logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer


		class 	lowerCamelCase__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
							    F"tpu_num_cores: {training_args.tpu_num_cores}"     , )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														a__:       Union[str, Any]											=Trainer(
														    model=DummyModel()     , args=__magic_name__     , data_collator=DummyDataCollator()     , eval_dataset=__magic_name__     , compute_metrics=__magic_name__     , )
														a__:       str											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

														a__:       Optional[int]											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





