


'''simple docstring'''



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     k_size // 2
			UpperCAmelCase__  :					str        =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			UpperCAmelCase__  :					Dict        =     1 / (2 * pi * sigma) * exp(-(square(UpperCamelCase__					) + square(UpperCamelCase__					)) / (2 * square(UpperCamelCase__					))					)
			return g







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     image.shape[0], image.shape[1]
			# dst image height and width
			UpperCAmelCase__  :					Any        =     height - k_size + 1
			UpperCAmelCase__  :					Optional[int]        =     width - k_size + 1

			# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
			UpperCAmelCase__  :					Optional[Any]        =     zeros((dst_height * dst_width, k_size * k_size)					)
			UpperCAmelCase__  :					List[str]        =     0
			for i, j in product(range(UpperCamelCase__					) ,      range(UpperCamelCase__					)					):
						UpperCAmelCase__  :					List[str]        =     ravel(image[i : i + k_size, j : j + k_size]					)
						UpperCAmelCase__  :					Union[str, Any]        =     window
						row += 1

			#  turn the kernel into shape(k*k, 1)
			UpperCAmelCase__  :					Tuple        =     gen_gaussian_kernel(UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     ravel(UpperCamelCase__					)

			# reshape and get the dst image
			UpperCAmelCase__  :					List[str]        =     dot(UpperCamelCase__ ,      UpperCamelCase__					).reshape(UpperCamelCase__ ,      UpperCamelCase__					).astype(UpperCamelCase__					)

			return dst


if __name__ == "__main__":
		# read original image
		__A	=imread(R'../image_data/lena.jpg')
		# turn image in gray scale value
		__A	=cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		__A	=gaussian_filter(gray, 3, sigma=1)
		__A	=gaussian_filter(gray, 5, sigma=0.8)

		# show result images
		imshow('gaussian filter with 3x3 mask', gaussianaxa)
		imshow('gaussian filter with 5x5 mask', gaussianaxa)
		waitKey()



'''simple docstring'''



import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__A	=logging.getLogger()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     argparse.ArgumentParser()
			parser.add_argument("""-f"""					)
			UpperCAmelCase__  :					Optional[Any]        =     parser.parse_args()
			return args.f


class 		_snake_case					(     a__	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     logging.StreamHandler(sys.stdout)
								logger.addHandler(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     get_gpu_count()

								if n_gpu > 1:
											pass
											# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
											# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
											# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
											# cmd = [sys.executable] + distributed_args + args
											# execute_subprocess_async(cmd, env=self.get_env())
											# XXX: test the results - need to save them first into .json file
								else:
											args.insert(0							,							"""run_glue_deebert.py""")
											with patch.object(_lowerCamelCase							,							"""argv"""							,							_lowerCamelCase):
														UpperCAmelCase__  :					Union[str, Any]        =     run_glue_deebert.main()
														for value in result.values():
																	self.assertGreaterEqual(_lowerCamelCase							,							0.666)




					@slow
					@require_torch_non_multi_gpu
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Dict        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)

								UpperCAmelCase__  :					Any        =     """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
								self.run_and_check(_lowerCamelCase)