






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

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

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= cn.convert_to_negative(_A			)
						# assert negative_img array for at least one True
						assert negative_img.any()

def    UpperCamelCase						(	):






						"""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(_A,   110			)			).startswith(
												    """<PIL.Image.Image image mode=RGB size=100x100 at"""			)

def    UpperCamelCase						(	):






						"""simple docstring"""







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

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       List[Any]       						= imread("""digital_image_processing/image_data/lena_small.jpg""",   0			)
						# assert ambiguous array for all == True
						assert canny_img.all()
						__magic_name__      :       Optional[int]       						= canny.canny(_A			)
						# assert canny array for at least one True
						assert canny_array.any()

def    UpperCamelCase						(	):






						"""simple docstring"""







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

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Any       						= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
						__magic_name__      :       int       						= conv.img_convolve(_A,   _A			).astype(_A			)
						assert res.any()

def    UpperCamelCase						(	):






						"""simple docstring"""







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

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__						,__magic_name__      :       Tuple       						= sob.sobel_filter(_A			)
						assert grad.any() and theta.any()

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       Any       						= sp.make_sepia(_A,   20			)
						assert sepia.all()

def    UpperCamelCase						(	_A = "digital_image_processing/image_data/lena_small.jpg"			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= bs.Burkes(imread(_A,   1			),   120			)
						burkes.process()
						assert burkes.output_img.any()

def    UpperCamelCase						(	_A = "digital_image_processing/image_data/lena_small.jpg",   ):






						"""simple docstring"""







						__magic_name__      :       int       						= rs.NearestNeighbour(imread(_A,   1			),   400,   200			)
						nn.process()
						assert nn.output.any()

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       List[Any]       						= """digital_image_processing/image_data/lena.jpg"""

						# Reading the image and converting it to grayscale.
						__magic_name__      :       Tuple       						= imread(_A,   0			)

						# Test for get_neighbors_pixel function() return not None
						__magic_name__      :       str       						= 0
						__magic_name__      :       List[Any]       						= 0
						__magic_name__      :       Optional[Any]       						= image[x_coordinate][y_coordinate]

						__magic_name__      :       Dict       						= lbp.get_neighbors_pixel(
						    _A,   _A,   _A,   _A			)

						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
						__magic_name__      :       int       						= 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]			):
																		__magic_name__      :       Any       						= lbp.local_binary_value(_A,   _A,   _A			)

						assert lbp_image.any()








import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__magic_name__:					str							=     logging.getLogger(__name__)

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       int       						= argparse.ArgumentParser(
						    description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""			)
						parser.add_argument("""--file_path""",   type=_A,   default="""data/dump.txt""",   help="""The path to the data."""			)
						parser.add_argument("""--tokenizer_type""",   type=_A,   default="""bert""",   choices=["""bert""", """roberta""", """gpt2"""]			)
						parser.add_argument("""--tokenizer_name""",   type=_A,   default="""bert-base-uncased""",   help="""The tokenizer to use."""			)
						parser.add_argument("""--dump_file""",   type=_A,   default="""data/dump""",   help="""The dump file prefix."""			)
						__magic_name__      :       Dict       						= parser.parse_args()

						logger.info(f'Loading Tokenizer ({args.tokenizer_name})'			)
						if args.tokenizer_type == "bert":
												__magic_name__      :       Tuple       						= BertTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       List[Any]       						= tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
												__magic_name__      :       Optional[int]       						= tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
						elif args.tokenizer_type == "roberta":
												__magic_name__      :       Optional[int]       						= RobertaTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       List[Any]       						= tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
												__magic_name__      :       Any       						= tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
						elif args.tokenizer_type == "gpt2":
												__magic_name__      :       Any       						= GPTaTokenizer.from_pretrained(args.tokenizer_name			)
												__magic_name__      :       int       						= tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
												__magic_name__      :       Optional[int]       						= tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

						logger.info(f'Loading text from {args.file_path}'			)
						with open(args.file_path,   """r""",   encoding="""utf8"""			) as fp:
												__magic_name__      :       Tuple       						= fp.readlines()

						logger.info("""Start encoding"""			)
						logger.info(f'{len(_A			)} examples to process.'			)

						__magic_name__      :       List[Any]       						= []
						__magic_name__      :       str       						= 0
						__magic_name__      :       str       						= 10000
						__magic_name__      :       Dict       						= time.time()
						for text in data:
												__magic_name__      :       Tuple       						= f'{bos} {text.strip()} {sep}'
												__magic_name__      :       Optional[int]       						= tokenizer.encode(_A,   add_special_tokens=_A			)
												rslt.append(_A			)

												iter += 1
												if iter % interval == 0:
																		__magic_name__      :       Union[str, Any]       						= time.time()
																		logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
																		__magic_name__      :       Any       						= time.time()
						logger.info("""Finished binarization"""			)
						logger.info(f'{len(_A			)} examples processed.'			)

						__magic_name__      :       Tuple       						= f'{args.dump_file}.{args.tokenizer_name}.pickle'
						__magic_name__      :       Tuple       						= tokenizer.vocab_size
						if vocab_size < (1 << 16):
												__magic_name__      :       Optional[int]       						= [np.uintaa(_A			) for d in rslt]
						else:
												__magic_name__      :       str       						= [np.intaa(_A			) for d in rslt]
						random.shuffle(rslt_			)
						logger.info(f'Dump to {dp_file}'			)
						with open(_A,   """wb"""			) as handle:
												pickle.dump(rslt_,   _A,   protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
		main()
