
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    snake_case						=   np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]						)
    snake_case						=   np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]						)

    snake_case						=   np.abs(img_convolve(A__					,						A__						)						)
    snake_case						=   np.abs(img_convolve(A__					,						A__						)						)
    # modify the pix within [0, 255]
    snake_case						=   dst_x * 255 / np.max(A__						)
    snake_case						=   dst_y * 255 / np.max(A__						)

    snake_case						=   np.sqrt((np.square(A__						)) + (np.square(A__						))						)
    snake_case						=   dst_xy * 255 / np.max(A__						)
    snake_case						=   dst_xy.astype(np.uinta						)

    snake_case						=   np.arctana(A__					,						A__						)
    return dst, theta


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

   _A   ,    _A       =					sobel_filter(gray)

   # show result images
   imshow("sobel filter", sobel_grad)
   imshow("sobel theta", sobel_theta)
   waitKey(0)



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_A       =					logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase						:
     UpperCAmelCase__							:				str 	=						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				bool 	=						field(default=A_				,					metadata={"help": "Set this flag to use fast tokenization."}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)



def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

    if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case	,				snake_case	,				snake_case						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        snake_case	,				snake_case	,				snake_case						=   parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir						)
        and os.listdir(training_args.output_dir						)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            " --overwrite_output_dir to overcome."						)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,						datefmt="%m/%d/%Y %H:%M:%S"					,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,						)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,						training_args.local_rank					,						training_args.device					,						training_args.n_gpu					,						bool(training_args.local_rank != -1						)					,						training_args.fpaa					,						)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank						):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"					,						A__						)

    # Prepare Question-Answering task
    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case						=   AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						)
    snake_case						=   AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						use_fast=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path					,						from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,						config=A__					,						cache_dir=model_args.cache_dir					,						)

    # Get datasets
    snake_case						=   hasattr(model.config					,						"lang2id"						)
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir						)
        if training_args.do_train
        else None
    )
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						mode="dev"					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir					,						)
        if training_args.do_eval
        else None
    )

    # Data collator
    snake_case						=   DataCollatorWithPadding(A__					,						pad_to_multiple_of=8						) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case						=   Trainer(
        model=A__					,						args=A__					,						train_dataset=A__					,						eval_dataset=A__					,						data_collator=A__					,						)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir						)



def 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

