






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

__UpperCamelCase      :      List[Any]     						=						imread(r"""digital_image_processing/image_data/lena_small.jpg""")
__UpperCamelCase      :      Any     						=						cvtColor(img, COLOR_BGR2GRAY)





def 				snake_case  (     ):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''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 				snake_case  (     ):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase       ,						__lowercase			       =  sob.sobel_filter(lowerCamelCase					)
	assert grad.any() and theta.any()





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase			       =  sp.make_sepia(lowerCamelCase				,  20					)
	assert sepia.all()





def 				snake_case  (     lowerCamelCase = "digital_image_processing/image_data/lena_small.jpg"					):




	'''simple docstring'''







	__lowercase			       =  bs.Burkes(imread(lowerCamelCase				,  1					)				,  120					)
	burkes.process()
	assert burkes.output_img.any()





def 				snake_case  (     lowerCamelCase = "digital_image_processing/image_data/lena_small.jpg"				,  ):




	'''simple docstring'''







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





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase			       =  """digital_image_processing/image_data/lena.jpg"""

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

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

	__lowercase			       =  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
	__lowercase			       =  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]					):
			__lowercase			       =  lbp.local_binary_value(lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)

	assert lbp_image.any()









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

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

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__UpperCamelCase      :      Any     						=						logging.get_logger(__name__)


@dataclass
class        __UpperCamelCase    :
	__snake_case	:str   =       field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()   )}   )
	__snake_case	:str   =       field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}   )
	__snake_case	: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.'
	        )
	    }   ,			)
	__snake_case	:bool   =       field(
	    default=_lowerCAmelCase   ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}   )







	def  _a   (  self      :							Dict			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  self.task_name.lower()


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Optional[int]   =       'train'
	__snake_case	:int   =       'dev'
	__snake_case	:Any   =       'test'


class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:GlueDataTrainingArguments
	__snake_case	:str
	__snake_case	:List[InputFeatures]







	def __init__(  self      :							Dict       ,				_lowerCAmelCase      :							GlueDataTrainingArguments       ,				_lowerCAmelCase      :							PreTrainedTokenizerBase       ,				_lowerCAmelCase      :							Optional[int] = None       ,				_lowerCAmelCase      :							Union[str, Split] = Split.train       ,				_lowerCAmelCase      :							Optional[str] = None       ,				)		-> List[Any]:
		"""simple docstring"""







		warnings.warn(
		    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
		    """library. You can have a look at this example script for pointers: """
		    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""       ,				_lowerCAmelCase       ,				)
		__lowercase             =  args
		__lowercase             =  glue_processors[args.task_name]()
		__lowercase             =  glue_output_modes[args.task_name]
		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			try:
				__lowercase             =  Split[mode]
			except KeyError:
				raise KeyError("""mode is not a valid split name"""			)
        # Load data features from cache or dataset file
		__lowercase             =  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}_{args.task_name}'       ,				)
		__lowercase             =  self.processor.get_labels()
		if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
		    "RobertaTokenizer",
		    "RobertaTokenizerFast",
		    "XLMRobertaTokenizer",
		    "BartTokenizer",
		    "BartTokenizerFast",
		):
			# HACK(label indices are swapped in RoBERTa pretrained model)
			__lowercase       ,						__lowercase             =  label_list[2], label_list[1]
		__lowercase             =  label_list

		# Make sure only the first process in distributed training processes the dataset,
		# and the others will use the cache.
		__lowercase             =  cached_features_file + """.lock"""
		with FileLock(_lowerCAmelCase			):
			if os.path.exists(_lowerCAmelCase			) and not args.overwrite_cache:
				__lowercase             =  time.time()
				__lowercase             =  torch.load(_lowerCAmelCase			)
				logger.info(
				    F'Loading features from cached file {cached_features_file} [took %.3f s]'       ,				time.time() - start			)
			else:
				logger.info(F'Creating features from dataset file at {args.data_dir}'			)

				if mode == Split.dev:
					__lowercase             =  self.processor.get_dev_examples(args.data_dir			)
				elif mode == Split.test:
					__lowercase             =  self.processor.get_test_examples(args.data_dir			)
				else:
					__lowercase             =  self.processor.get_train_examples(args.data_dir			)
				if limit_length is not None:
					__lowercase             =  examples[:limit_length]
				__lowercase             =  glue_convert_examples_to_features(
				    _lowerCAmelCase       ,				_lowerCAmelCase       ,				max_length=args.max_seq_length       ,				label_list=_lowerCAmelCase       ,				output_mode=self.output_mode       ,				)
				__lowercase             =  time.time()
				torch.save(self.features       ,				_lowerCAmelCase			)
				# ^ 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      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		return len(self.features			)







	def __getitem__(  self      :							Tuple       ,				_lowerCAmelCase      :							Optional[int]			)		-> InputFeatures:
		"""simple docstring"""







		return self.features[i]







	def  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		return self.label_list

