




'''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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			A_									= k_size // 2
			A_		, A_									= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
			A_									= 1 / (2 * pi * sigma) * exp(-(square(a__     ) + square(a__     )) / (2 * square(a__     ))     )
			return g





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		List[Any]:
			A_		, A_									= image.shape[0], image.shape[1]
			# dst image height and width
			A_									= height - k_size + 1
			A_									= width - k_size + 1

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

			#  turn the kernel into shape(k*k, 1)
			A_									= gen_gaussian_kernel(a__       ,a__     )
			A_									= ravel(a__     )

			# reshape and get the dst image
			A_									= dot(a__       ,a__     ).reshape(a__       ,a__     ).astype(a__     )

			return dst


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

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








import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__    :List[str]       =				logging.getLogger(__name__)

@dataclass(frozen=UpperCAmelCase     )
class 						__a :
		_a      :    str
		_a      :    str
		_a      :    Optional[str]           =      None
		_a      :    Optional[str]           =      None
		_a      :    Optional[str]           =      None



@dataclass(frozen=UpperCAmelCase     )
class 						__a :
		_a      :    List[int]
		_a      :    Optional[List[int]]           =      None
		_a      :    Optional[List[int]]           =      None
		_a      :    Optional[Union[int, float]]           =      None
		_a      :    Optional[int]           =      None


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

		class 						__a (							UpperCAmelCase     ):
				_a      :    List[InputFeatures]
				def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE = False     ,       )					->			List[Any]:




											"""simple docstring"""





											_UpperCAmelCase											=     hans_processors[task]()

											_UpperCAmelCase											=     os.path.join(
											    _SCREAMING_SNAKE_CASE     ,       'cached_{}_{}_{}_{}'.format(
											        'dev' if evaluate else 'train'     ,       tokenizer.__class__.__name__     ,       str(_SCREAMING_SNAKE_CASE		)     ,       _SCREAMING_SNAKE_CASE     ,       )     ,       )
											_UpperCAmelCase											=     processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
																		# HACK(label indices are swapped in RoBERTa pretrained model)
																		_UpperCAmelCase						,			_UpperCAmelCase											=     label_list[2], label_list[1]
											_UpperCAmelCase											=     label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											_UpperCAmelCase											=     cached_features_file + '.lock'
											with FileLock(_SCREAMING_SNAKE_CASE		):
																		if os.path.exists(_SCREAMING_SNAKE_CASE		) and not overwrite_cache:
																									logger.info(f'''Loading features from cached file {cached_features_file}'''		)
																									_UpperCAmelCase											=     torch.load(_SCREAMING_SNAKE_CASE		)
																		else:
																									logger.info(f'''Creating features from dataset file at {data_dir}'''		)

																									_UpperCAmelCase											=     (
																									    processor.get_dev_examples(_SCREAMING_SNAKE_CASE		) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE		)
																									)

																									logger.info('Training examples: %s'     ,       len(_SCREAMING_SNAKE_CASE		)		)
																									_UpperCAmelCase											=     hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																									logger.info('Saving features into cached file %s'     ,       _SCREAMING_SNAKE_CASE		)
																									torch.save(self.features     ,       _SCREAMING_SNAKE_CASE		)
				def __len__(     self		)					->			List[str]:




											"""simple docstring"""





											return len(self.features		)
				def __getitem__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			InputFeatures:




											"""simple docstring"""





											return self.features[i]






				def       UpperCAmelCase__							(     self		)					->			Optional[int]:




											"""simple docstring"""





											return self.label_list


if is_tf_available():
		import tensorflow as tf

		class 						__a :
				_a      :    List[InputFeatures]
				def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = 128     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE = False     ,       )					->			Tuple:




											"""simple docstring"""





											_UpperCAmelCase											=     hans_processors[task]()
											_UpperCAmelCase											=     processor.get_labels()
											if tokenizer.__class__ in (
											    RobertaTokenizer,
											    RobertaTokenizerFast,
											    XLMRobertaTokenizer,
											    BartTokenizer,
											    BartTokenizerFast,
											):
																		# HACK(label indices are swapped in RoBERTa pretrained model)
																		_UpperCAmelCase						,			_UpperCAmelCase											=     label_list[2], label_list[1]
											_UpperCAmelCase											=     label_list

											_UpperCAmelCase											=     processor.get_dev_examples(_SCREAMING_SNAKE_CASE		) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE		)
											_UpperCAmelCase											=     hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)

											def gen():
																		for ex_index, ex in tqdm.tqdm(enumerate(self.features		)     ,       desc='convert examples to features'		):
																									if ex_index % 10000 == 0:
																																logger.info('Writing example %d of %d' % (ex_index, len(_SCREAMING_SNAKE_CASE		))		)

																									yield (
																									    {
																									        "example_id": 0,
																									        "input_ids": ex.input_ids,
																									        "attention_mask": ex.attention_mask,
																									        "token_type_ids": ex.token_type_ids,
																									    },
																									    ex.label,
																									)

											_UpperCAmelCase											=     tf.data.Dataset.from_generator(
											    _SCREAMING_SNAKE_CASE     ,       (
											        {
											            'example_id': tf.intaa,
											            'input_ids': tf.intaa,
											            'attention_mask': tf.intaa,
											            'token_type_ids': tf.intaa,
											        },
											        tf.intaa,
											    )     ,       (
											        {
											            'example_id': tf.TensorShape([]		),
											            'input_ids': tf.TensorShape([None, None]		),
											            'attention_mask': tf.TensorShape([None, None]		),
											            'token_type_ids': tf.TensorShape([None, None]		),
											        },
											        tf.TensorShape([]		),
											    )     ,       )
				def       UpperCAmelCase__							(     self		)					->			Dict:




											"""simple docstring"""





											return self.dataset
				def __len__(     self		)					->			List[Any]:




											"""simple docstring"""





											return len(self.features		)
				def __getitem__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			InputFeatures:




											"""simple docstring"""





											return self.features[i]






				def       UpperCAmelCase__							(     self		)					->			List[Any]:




											"""simple docstring"""





											return self.label_list

class 						__a (							UpperCAmelCase     ):
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[Any]:




									"""simple docstring"""





									return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE     ,       'heuristics_train_set.txt'		)		)     ,       'train'		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE     ,       'heuristics_evaluation_set.txt'		)		)     ,       'dev'		)
		def       UpperCAmelCase__							(     self		)					->			List[str]:




									"""simple docstring"""





									return ["contradiction", "entailment", "neutral"]






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     []
									for i, line in enumerate(_SCREAMING_SNAKE_CASE		):
																if i == 0:
																							continue
																_UpperCAmelCase											=     '%s-%s' % (set_type, line[0])
																_UpperCAmelCase											=     line[5]
																_UpperCAmelCase											=     line[6]
																_UpperCAmelCase											=     line[7][2:] if line[7].startswith('ex'		) else line[7]
																_UpperCAmelCase											=     line[0]
																examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE     ,       text_a=_SCREAMING_SNAKE_CASE     ,       text_b=_SCREAMING_SNAKE_CASE     ,       label=_SCREAMING_SNAKE_CASE     ,       pairID=_SCREAMING_SNAKE_CASE		)		)
									return examples



def 			lowerCAmelCase__					(     a__:						List[InputExample]     ,    a__:						List[str]     ,    a__:						int     ,    a__:						PreTrainedTokenizer     ,    )    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     {label: i for i, label in enumerate(a__			)}

							_UpperCAmelCase											=     []
							for ex_index, example in tqdm.tqdm(enumerate(a__			)     ,    desc='convert examples to features'			):
														if ex_index % 1_0_0_0_0 == 0:
																					logger.info('Writing example %d' % (ex_index)			)

														_UpperCAmelCase											=     tokenizer(
														    example.text_a     ,    example.text_b     ,    add_special_tokens=a__     ,    max_length=a__     ,    padding='max_length'     ,    truncation=a__     ,    return_overflowing_tokens=a__     ,    )

														_UpperCAmelCase											=     label_map[example.label] if example.label in label_map else 0

														_UpperCAmelCase											=     int(example.pairID			)

														features.append(InputFeatures(**a__     ,    label=a__     ,    pairID=a__			)			)

							for i, example in enumerate(examples[:5]			):
														logger.info('*** Example ***'			)
														logger.info(F'''guid: {example}'''			)
														logger.info(F'''features: {features[i]}'''			)

							return features


lowerCAmelCase__    :Optional[Any]       =				{
    '''hans''': 3,
}







lowerCAmelCase__    :Optional[int]       =				{
    '''hans''': HansProcessor,
}
