



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__snake_case     :Union[str, Any]			       =imread(r'digital_image_processing/image_data/lena_small.jpg')
__snake_case     :List[str]			       =cvtColor(img, COLOR_BGR2GRAY)






def  lowerCamelCase_	(      )       ->							Tuple:

							'''simple docstring'''






							A      						=    cn.convert_to_negative(lowerCAmelCase__				)
							# assert negative_img array for at least one True
							assert negative_img.any()






def  lowerCamelCase_	(      )       ->							List[str]:

							'''simple docstring'''






							with Image.open('digital_image_processing/image_data/lena_small.jpg'				) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(lowerCAmelCase__			,	110				)				).startswith(
														    '<PIL.Image.Image image mode=RGB size=100x100 at'				)






def  lowerCamelCase_	(      )       ->							List[Any]:

							'''simple docstring'''






							A      						=    canny.gen_gaussian_kernel(9			,	sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()






def  lowerCamelCase_	(      )       ->							Dict:

							'''simple docstring'''






							A      						=    imread('digital_image_processing/image_data/lena_small.jpg'			,	0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							A      						=    canny.canny(lowerCAmelCase__				)
							# assert canny array for at least one True
							assert canny_array.any()






def  lowerCamelCase_	(      )       ->							Union[str, Any]:

							'''simple docstring'''






							assert gg.gaussian_filter(lowerCAmelCase__			,	5			,	sigma=0.9				).all()






def  lowerCamelCase_	(      )       ->							List[Any]:

							'''simple docstring'''






							A      						=    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							A      						=    conv.img_convolve(lowerCAmelCase__			,	lowerCAmelCase__				).astype(lowerCAmelCase__				)
							assert res.any()






def  lowerCamelCase_	(      )       ->							Optional[int]:

							'''simple docstring'''






							assert med.median_filter(lowerCAmelCase__			,	3				).any()






def  lowerCamelCase_	(      )       ->							Optional[Any]:

							'''simple docstring'''






							A						,   A      						=    sob.sobel_filter(lowerCAmelCase__				)
							assert grad.any() and theta.any()






def  lowerCamelCase_	(      )       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    sp.make_sepia(lowerCAmelCase__			,	20				)
							assert sepia.all()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str = "digital_image_processing/image_data/lena_small.jpg"				)       ->							Any:

							'''simple docstring'''






							A      						=    bs.Burkes(imread(lowerCAmelCase__			,	1				)			,	120				)
							burkes.process()
							assert burkes.output_img.any()






def  lowerCamelCase_	(      lowerCAmelCase__	:	str = "digital_image_processing/image_data/lena_small.jpg"			,	)       ->							str:

							'''simple docstring'''






							A      						=    rs.NearestNeighbour(imread(lowerCAmelCase__			,	1				)			,	400			,	200				)
							nn.process()
							assert nn.output.any()






def  lowerCamelCase_	(      )       ->							int:

							'''simple docstring'''






							A      						=    'digital_image_processing/image_data/lena.jpg'

							# Reading the image and converting it to grayscale.
							A      						=    imread(lowerCAmelCase__			,	0				)

							# Test for get_neighbors_pixel function() return not None
							A      						=    0
							A      						=    0
							A      						=    image[x_coordinate][y_coordinate]

							A      						=    lbp.get_neighbors_pixel(
							    lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							assert neighbors_pixels is not None

							# Test for local_binary_pattern function()
							# Create a numpy array as the same height and width of read image
							A      						=    np.zeros((image.shape[0], image.shape[1])				)

							# Iterating through the image and calculating the local binary pattern value
							# for each pixel.
							for i in range(0			,	image.shape[0]				):
														for j in range(0			,	image.shape[1]				):
																					A      						=    lbp.local_binary_value(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							assert lbp_image.any()




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


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :int			       =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__snake_case     :Optional[int]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCamelCase       )}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}       )
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={
				        'help': (
				            'The maximum total input sequence length after tokenization. Sequences longer '
				            'than this will be truncated, sequences shorter will be padded.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} ,				)
				A_   :     int								=     field(
				    default=6_4 ,				metadata={
				        'help': (
				            'The maximum number of tokens for the question. Questions longer than this will '
				            'be truncated to this length.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=3_0 ,				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.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}       )
				A_   :     float								=     field(
				    default=0.0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=2_0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=0 ,				metadata={
				        'help': (
				            'language id of input for language-specific xlm models (see'
				            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
				        )
				    } ,				)
				A_   :     int								=     field(default=1 ,				metadata={'help': 'multiple threads for converting example to features'}       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     'train'
				A_   :     str								=     'dev'





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     SquadDataTrainingArguments
				A_   :     List[SquadFeatures]
				A_   :     Split
				A_   :     bool


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  SquadDataTrainingArguments						, __UpperCamelCase      :  PreTrainedTokenizer						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Union[str, Split] = Split.train						, __UpperCamelCase      :  Optional[bool] = False						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  Optional[str] = "pt"						, )		->     Any:
											A      						=    args
											A      						=    is_language_sensitive
											A      						=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																		try:
																									A      						=    Split[mode]
																		except KeyError:
																									raise KeyError('mode is not a valid split name'       )
											A      						=    mode
											# Load data features from cache or dataset file
											A      						=    'v2' if args.version_2_with_negative else 'v1'
											A      						=    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.
											A      						=    cached_features_file + '.lock'
											with FileLock(__UpperCamelCase       ):
																		if os.path.exists(__UpperCamelCase       ) and not args.overwrite_cache:
																									A      						=    time.time()
																									A      						=    torch.load(__UpperCamelCase       )

																									# Legacy cache files have only features, while new cache files
																									# will have dataset and examples also.
																									A      						=    self.old_features['features']
																									A      						=    self.old_features.get('dataset'						, __UpperCamelCase       )
																									A      						=    self.old_features.get('examples'						, __UpperCamelCase       )
																									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:
																																A      						=    self.processor.get_dev_examples(args.data_dir       )
																									else:
																																A      						=    self.processor.get_train_examples(args.data_dir       )

																									A						,   A      						=    squad_convert_examples_to_features(
																									    examples=self.examples						, tokenizer=__UpperCamelCase						, 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=__UpperCamelCase						, )

																									A      						=    time.time()
																									torch.save(
																									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						, __UpperCamelCase						, )
																									# ^ 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      :  Optional[Any]       )		->     Tuple:
											return len(self.features       )


				def __getitem__(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     Dict[str, torch.Tensor]:
											# Convert to Tensors and build dataset
											A      						=    self.features[i]

											A      						=    torch.tensor(feature.input_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.attention_mask						, dtype=torch.long       )
											A      						=    torch.tensor(feature.token_type_ids						, dtype=torch.long       )
											A      						=    torch.tensor(feature.cls_index						, dtype=torch.long       )
											A      						=    torch.tensor(feature.p_mask						, dtype=torch.float       )
											A      						=    torch.tensor(feature.is_impossible						, dtype=torch.float       )

											A      						=    {
											    '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:
																		A      						=    torch.tensor(feature.start_position						, dtype=torch.long       )
																		A      						=    torch.tensor(feature.end_position						, dtype=torch.long       )
																		inputs.update({'start_positions': start_positions, 'end_positions': end_positions}       )

											return inputs