





import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_SCREAMING_SNAKE_CASE 		=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class SCREAMING_SNAKE_CASE_  :
						__magic_name__:     str	 =    field(
						    default=UpperCAmelCase__    ,			metadata={"help": "Model type selected in the list: " + ", ".join(UpperCAmelCase__   )}   )
						__magic_name__:     str	 =    field(
						    default=UpperCAmelCase__    ,			metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}   )
						__magic_name__:     int	 =    field(
						    default=128    ,			metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated, sequences shorter will be padded."
						        )
						    }    ,			)
						__magic_name__:     int	 =    field(
						    default=128    ,			metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}    ,			)
						__magic_name__:     int	 =    field(
						    default=64    ,			metadata={
						        "help": (
						            "The maximum number of tokens for the question. Questions longer than this will "
						            "be truncated to this length."
						        )
						    }    ,			)
						__magic_name__:     int	 =    field(
						    default=30    ,			metadata={
						        "help": (
						            "The maximum length of an answer that can be generated. This is needed because the start "
						            "and end predictions are not conditioned on one another."
						        )
						    }    ,			)
						__magic_name__:     bool	 =    field(
						    default=UpperCAmelCase__    ,			metadata={"help": "Overwrite the cached training and evaluation sets"}   )
						__magic_name__:     bool	 =    field(
						    default=UpperCAmelCase__    ,			metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}   )
						__magic_name__:     float	 =    field(
						    default=0.0    ,			metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}   )
						__magic_name__:     int	 =    field(
						    default=20    ,			metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}   )
						__magic_name__:     int	 =    field(
						    default=0    ,			metadata={
						        "help": (
						            "language id of input for language-specific xlm models (see"
						            " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
						        )
						    }    ,			)
						__magic_name__:     int	 =    field(default=1    ,			metadata={"help": "multiple threads for converting example to features"}   )
class SCREAMING_SNAKE_CASE_  (   UpperCAmelCase__   ):
						__magic_name__:     Dict	 =    'train'
						__magic_name__:     List[Any]	 =    'dev'
class SCREAMING_SNAKE_CASE_  (   UpperCAmelCase__   ):
						__magic_name__:     SquadDataTrainingArguments
						__magic_name__:     List[SquadFeatures]
						__magic_name__:     Split
						__magic_name__:     bool






						def __init__( self : Optional[int]						,				_A : Optional[int]						,				_A : List[Any]						,				_A : List[str] = None						,				_A : Tuple = Split.train						,				_A : List[Any] = False						,				_A : Dict = None						,				_A : Optional[int] = "pt"						,				)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Tuple     =		args
							snake_case_						:     Optional[int]     =		is_language_sensitive
							snake_case_						:     int     =		SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
							if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE		):
								try:
									snake_case_						:     Union[str, Any]     =		Split[mode]
								except KeyError:
									raise KeyError('mode is not a valid split name'		)
							snake_case_						:     Any     =		mode
							# Load data features from cache or dataset file
							snake_case_						:     Union[str, Any]     =		"""v2""" if args.version_2_with_negative else """v1"""
							snake_case_						:     Tuple     =		os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir						,				F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""						,				)

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							snake_case_						:     Optional[Any]     =		cached_features_file + """.lock"""
							with FileLock(_SCREAMING_SNAKE_CASE		):
								if os.path.exists(_SCREAMING_SNAKE_CASE		) and not args.overwrite_cache:
									snake_case_						:     Dict     =		time.time()
									snake_case_						:     Union[str, Any]     =		torch.load(_SCREAMING_SNAKE_CASE		)

									# Legacy cache files have only features, while new cache files
									# will have dataset and examples also.
									snake_case_						:     str     =		self.old_features["""features"""]
									snake_case_						:     List[str]     =		self.old_features.get('dataset'						,				_SCREAMING_SNAKE_CASE		)
									snake_case_						:     str     =		self.old_features.get('examples'						,				_SCREAMING_SNAKE_CASE		)
									logger.info(
									    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""						,				time.time() - start		)

									if self.dataset is None or self.examples is None:
										logger.warning(
										    F"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
										    ' future run'		)
								else:
									if mode == Split.dev:
										snake_case_						:     int     =		self.processor.get_dev_examples(args.data_dir		)
									else:
										snake_case_						:     List[Any]     =		self.processor.get_train_examples(args.data_dir		)

									snake_case_						:     Dict     =		squad_convert_examples_to_features(
									    examples=self.examples						,				tokenizer=_SCREAMING_SNAKE_CASE						,				max_seq_length=args.max_seq_length						,				doc_stride=args.doc_stride						,				max_query_length=args.max_query_length						,				is_training=mode == Split.train						,				threads=args.threads						,				return_dataset=_SCREAMING_SNAKE_CASE						,				)

									snake_case_						:     Tuple     =		time.time()
									torch.save(
									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						,				_SCREAMING_SNAKE_CASE						,				)
									# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
									logger.info(
									    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""		)






						def __len__( self : Tuple		)							->      Tuple:







							"""simple docstring"""


							return len(self.features		)






						def __getitem__( self : Union[str, Any]						,				_A : Optional[Any]		)							->      Dict[str, torch.Tensor]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		self.features[i]

							snake_case_						:     str     =		torch.tensor(feature.input_ids						,				dtype=torch.long		)
							snake_case_						:     List[str]     =		torch.tensor(feature.attention_mask						,				dtype=torch.long		)
							snake_case_						:     Union[str, Any]     =		torch.tensor(feature.token_type_ids						,				dtype=torch.long		)
							snake_case_						:     str     =		torch.tensor(feature.cls_index						,				dtype=torch.long		)
							snake_case_						:     str     =		torch.tensor(feature.p_mask						,				dtype=torch.float		)
							snake_case_						:     Union[str, Any]     =		torch.tensor(feature.is_impossible						,				dtype=torch.float		)

							snake_case_						:     str     =		{
							    """input_ids""": input_ids,
							    """attention_mask""": attention_mask,
							    """token_type_ids""": token_type_ids,
							}

							if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
								del inputs["token_type_ids"]

							if self.args.model_type in ["xlnet", "xlm"]:
								inputs.update({'cls_index': cls_index, 'p_mask': p_mask}		)
								if self.args.version_2_with_negative:
									inputs.update({'is_impossible': is_impossible}		)
								if self.is_language_sensitive:
									inputs.update({'langs': (torch.ones(input_ids.shape						,				dtype=torch.intaa		) * self.args.lang_id)}		)

							if self.mode == Split.train:
								snake_case_						:     Dict     =		torch.tensor(feature.start_position						,				dtype=torch.long		)
								snake_case_						:     Tuple     =		torch.tensor(feature.end_position						,				dtype=torch.long		)
								inputs.update({'start_positions': start_positions, 'end_positions': end_positions}		)

							return inputs








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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     List[Any]     =		k_size // 2
	snake_case_  ,snake_case_						:     Any     =		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	snake_case_						:     Optional[Any]     =		1 / (2 * pi * sigma) * exp(-(square(__a				) + square(__a				)) / (2 * square(__a				))				)
	return g


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_  ,snake_case_						:     Union[str, Any]     =		image.shape[0], image.shape[1]
	# dst image height and width
	snake_case_						:     int     =		height - k_size + 1
	snake_case_						:     Optional[int]     =		width - k_size + 1

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

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

	# reshape and get the dst image
	snake_case_						:     Optional[int]     =		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()

