


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a	):




		"""simple docstring"""






		if isinstance(_a     ,       _a	):
				lowerCAmelCase__       :      List[str]   					= np.full((len(_a	), sequence_length, 2)     ,       _a	)
		else:
				lowerCAmelCase__       :      Optional[Any]   					= np.full((len(_a	), sequence_length)     ,       _a	)

		for i, tensor in enumerate(_a	):
				if padding_side == "right":
						if isinstance(_a     ,       _a	):
								lowerCAmelCase__       :      Optional[Any]   					= tensor[:sequence_length]
						else:
								lowerCAmelCase__       :      int   					= tensor[:sequence_length]
				else:
						if isinstance(_a     ,       _a	):
								lowerCAmelCase__       :      Optional[int]   					= tensor[:sequence_length]
						else:
								lowerCAmelCase__       :      Optional[int]   					= tensor[:sequence_length]

		return out_tensor.tolist()






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ord(_a	)
		if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
				return True
		lowerCAmelCase__       :      int   					= unicodedata.category(_a	)
		if cat.startswith('''P'''	):
				return True
		return False





@dataclass
class  _a							(    _lowercase):
	_a  :  PreTrainedTokenizerBase
	_a  :  Union[bool, str, PaddingStrategy]				   =							True
	_a  :  Optional[int]				   =							None
	_a  :  Optional[int]				   =							None
	_a  :  int				   =							-100
	_a  :  str				   =							"pt"




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Any:
			import torch

			lowerCAmelCase__       :      Dict   					= '''label''' if '''label''' in features[0].keys() else '''labels'''
			lowerCAmelCase__       :      List[str]   					= [feature[label_name] for feature in features] if label_name in features[0].keys() else None
			lowerCAmelCase__       :      Dict   					= self.tokenizer.pad(
			    _SCREAMING_SNAKE_CASE		, padding=self.padding		, max_length=self.max_length		, pad_to_multiple_of=self.pad_to_multiple_of		, return_tensors='''pt''' if labels is None else None		, )

			if labels is None:
					return batch

			lowerCAmelCase__       :      int   					= torch.tensor(batch['''entity_ids''']		).shape[1]
			lowerCAmelCase__       :      Union[str, Any]   					= self.tokenizer.padding_side
			if padding_side == "right":
					lowerCAmelCase__       :      Dict   					= [
					    list(_SCREAMING_SNAKE_CASE		) + [self.label_pad_token_id] * (sequence_length - len(_SCREAMING_SNAKE_CASE		)) for label in labels
					]
			else:
					lowerCAmelCase__       :      int   					= [
					    [self.label_pad_token_id] * (sequence_length - len(_SCREAMING_SNAKE_CASE		)) + list(_SCREAMING_SNAKE_CASE		) for label in labels
					]

			lowerCAmelCase__       :      Optional[int]   					= [feature['''ner_tags'''] for feature in features]
			lowerCAmelCase__       :      List[str]   					= padding_tensor(_SCREAMING_SNAKE_CASE		, -1		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Union[str, Any]   					= [feature['''original_entity_spans'''] for feature in features]
			lowerCAmelCase__       :      Dict   					= padding_tensor(_SCREAMING_SNAKE_CASE		, (-1, -1)		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Any   					= {k: torch.tensor(_SCREAMING_SNAKE_CASE		, dtype=torch.intaa		) for k, v in batch.items()}

			return batch








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 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= k_size // 2
		lowerCAmelCase__					,     lowerCAmelCase__       :      Union[str, Any]   					= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowerCAmelCase__       :      str   					= 1 / (2 * pi * sigma) * exp(-(square(_a	) + square(_a	)) / (2 * square(_a	))	)
		return g






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= image.shape[0], image.shape[1]
		# dst image height and width
		lowerCAmelCase__       :      Any   					= height - k_size + 1
		lowerCAmelCase__       :      Tuple   					= width - k_size + 1

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

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase__       :      List[Any]   					= gen_gaussian_kernel(_a     ,       _a	)
		lowerCAmelCase__       :      str   					= ravel(_a	)

		# reshape and get the dst image
		lowerCAmelCase__       :      int   					= dot(_a     ,       _a	).reshape(_a     ,       _a	).astype(_a	)

		return dst


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

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




