

# 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


_a:      str								=     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   __UpperCamelCase      (   lowercase  ):




					def __init__(						self		:  int						,		lowerCAmelCase		:  int = 101							):


						'''simple docstring'''







						UpperCAmelCase_    			=     length




					def __len__(						self		:  int							):


						'''simple docstring'''







						return self.length




					def __getitem__(						self		:  int						,		lowerCAmelCase		:  Optional[int]							):


						'''simple docstring'''







						return i


				class   __UpperCamelCase      :




					def __call__(						self		:  str						,		lowerCAmelCase		:  List[Any]							):


						'''simple docstring'''







						return {"input_ids": torch.tensor(lowerCAmelCase							), "labels": torch.tensor(lowerCAmelCase							)}


				class   __UpperCamelCase      (   nn.Module  ):




					def __init__(						self		:  Any							):


						'''simple docstring'''







						super().__init__()
						# Add some (unused) params otherwise DDP will complain.
						UpperCAmelCase_    			=     nn.Linear(120						,		80							)




					def  __A   (						self		:  Union[str, Any]						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  Dict=None							):


						'''simple docstring'''







						if labels is not None:
							return torch.tensor(0.0						,		device=input_ids.device							), input_ids
						else:
							return input_ids



def  __lowerCAmelCase (  ):
	UpperCAmelCase_    			=     HfArgumentParser((TrainingArguments,)      )
	sys.argv += ["--output_dir", "./examples"]
	UpperCAmelCase_    			=     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 [1001, 256, 15]:
		UpperCAmelCase_    			=     DummyDataset(A      )

		def compute_metrics(A      ) -> Dict:
			UpperCAmelCase_    			=     list(range(len(A      )      )      )
			UpperCAmelCase_    			=     p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
			return {"success": success}

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

		UpperCAmelCase_    			=     trainer.predict(A      )
		logger.info(p.metrics      )
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics      )
			exit(1      )

		UpperCAmelCase_    			=     2

		UpperCAmelCase_    			=     trainer.evaluate()
		logger.info(A      )
		if metrics["eval_success"] is not True:
			logger.error(A      )
			exit(1      )

		UpperCAmelCase_    			=     trainer.predict(A      )
		logger.info(p.metrics      )
		if p.metrics["test_success"] is not True:
			logger.error(p.metrics      )
			exit(1      )

		UpperCAmelCase_    			=     None

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



def  __lowerCAmelCase (  A      ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
				main()


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like



def  __lowerCAmelCase (  A    ,       A=3      ):
	UpperCAmelCase_    			=     int(mask / 2      )
	# copy image size
	UpperCAmelCase_    			=     zeros_like(A      )
	for i in range(A    ,       gray_img.shape[0] - bd      ):
		for j in range(A    ,       gray_img.shape[1] - bd      ):
			# get mask according with mask
			UpperCAmelCase_    			=     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
			# calculate mask median
			UpperCAmelCase_    			=     sort(A      )[inta(divide((multiply(A    ,       A      ))    ,       2      ) + 1      )]
			UpperCAmelCase_    			=     median
	return median_img


if __name__ == "__main__":
				# read original image
				_a:      Optional[int]								=     imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a:      Dict								=     cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				_a:      Tuple								=     median_filter(gray, 3)
				_a:      Dict								=     median_filter(gray, 5)

				# show result images
				imshow("""median filter with 3x3 mask""", medianaxa)
				imshow("""median filter with 5x5 mask""", medianaxa)
				waitKey(0)