



def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[int]:






      '''simple docstring'''







      if hue < 0 or hue > 3_6_0:
            raise Exception("hue should be between 0 and 360"     )

      if saturation < 0 or saturation > 1:
            raise Exception("saturation should be between 0 and 1"     )

      if value < 0 or value > 1:
            raise Exception("value should be between 0 and 1"     )

      UpperCAmelCase_	:						Tuple					   =		value * saturation
      UpperCAmelCase_	:						Union[str, Any]					   =		hue / 6_0
      UpperCAmelCase_	:						Tuple					   =		chroma * (1 - abs(hue_section % 2 - 1     ))
      UpperCAmelCase_	:						Tuple					   =		value - chroma

      if hue_section >= 0 and hue_section <= 1:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 1 and hue_section <= 2:
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Union[str, Any]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						int					   =		round(2_5_5 * (match_value)     )
      elif hue_section > 2 and hue_section <= 3:
            UpperCAmelCase_	:						List[str]					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )
      elif hue_section > 3 and hue_section <= 4:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Optional[Any]					   =		round(2_5_5 * (chroma + match_value)     )
      elif hue_section > 4 and hue_section <= 5:
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (second_largest_component + match_value)     )
            UpperCAmelCase_	:						Any					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						str					   =		round(2_5_5 * (chroma + match_value)     )
      else:
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (chroma + match_value)     )
            UpperCAmelCase_	:						Dict					   =		round(2_5_5 * (match_value)     )
            UpperCAmelCase_	:						Optional[int]					   =		round(2_5_5 * (second_largest_component + match_value)     )

      return [red, green, blue]







def  _snake_case			(							__snake_case       ,	__snake_case       ,	__snake_case     )  ->  list[float]:






      '''simple docstring'''







      if red < 0 or red > 2_5_5:
            raise Exception("red should be between 0 and 255"     )

      if green < 0 or green > 2_5_5:
            raise Exception("green should be between 0 and 255"     )

      if blue < 0 or blue > 2_5_5:
            raise Exception("blue should be between 0 and 255"     )

      UpperCAmelCase_	:						str					   =		red / 2_5_5
      UpperCAmelCase_	:						Dict					   =		green / 2_5_5
      UpperCAmelCase_	:						int					   =		blue / 2_5_5
      UpperCAmelCase_	:						Any					   =		max(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Union[str, Any]					   =		value - min(__snake_case       ,	__snake_case       ,	__snake_case     )
      UpperCAmelCase_	:						Optional[Any]					   =		0 if value == 0 else chroma / value

      if chroma == 0:
            UpperCAmelCase_	:						Tuple					   =		0.0
      elif value == float_red:
            UpperCAmelCase_	:						List[str]					   =		6_0 * (0 + (float_green - float_blue) / chroma)
      elif value == float_green:
            UpperCAmelCase_	:						Union[str, Any]					   =		6_0 * (2 + (float_blue - float_red) / chroma)
      else:
            UpperCAmelCase_	:						Dict					   =		6_0 * (4 + (float_red - float_green) / chroma)

      UpperCAmelCase_	:						int					   =		(hue + 3_6_0) % 3_6_0

      return [hue, saturation, value]







def  _snake_case			(							__snake_case       ,	__snake_case     )  ->  bool:






      '''simple docstring'''







      UpperCAmelCase_	:						List[str]					   =		abs(hsv_a[0] - hsv_a[0]     ) < 0.2
      UpperCAmelCase_	:						Optional[Any]					   =		abs(hsv_a[1] - hsv_a[1]     ) < 0.002
      UpperCAmelCase_	:						Tuple					   =		abs(hsv_a[2] - hsv_a[2]     ) < 0.002

      return check_hue and check_saturation and check_value





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


__lowerCamelCase								=      logging.getLogger(__name__)


@dataclass
class 	snake_case_			:




       """simple docstring"""






       _lowerCamelCase           =					field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
       _lowerCamelCase           =					field(default=lowercase__			,			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.
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,			)







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






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		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.
            UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Any					   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Union[str, Any]					   =		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"       ,	__snake_case     )

      # 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.

      UpperCAmelCase_	:						Any					   =		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       ,	)
      UpperCAmelCase_	:						List[str]					   =		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=__snake_case       ,	)
      UpperCAmelCase_	:						Optional[int]					   =		AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path       ,	from_tf=bool(".ckpt" in model_args.model_name_or_path     )       ,	config=__snake_case       ,	cache_dir=model_args.cache_dir       ,	)

      # Get datasets
      UpperCAmelCase_	:						str					   =		hasattr(model.config       ,	"lang2id"     )
      UpperCAmelCase_	:						Optional[Any]					   =		(
          SquadDataset(
              __snake_case       ,	tokenizer=__snake_case       ,	is_language_sensitive=__snake_case       ,	cache_dir=model_args.cache_dir     )
          if training_args.do_train
          else None
      )
      UpperCAmelCase_	:						Optional[Any]					   =		(
          SquadDataset(
              __snake_case       ,	tokenizer=__snake_case       ,	mode="dev"       ,	is_language_sensitive=__snake_case       ,	cache_dir=model_args.cache_dir       ,	)
          if training_args.do_eval
          else None
      )

      # Data collator
      UpperCAmelCase_	:						Optional[Any]					   =		DataCollatorWithPadding(__snake_case       ,	pad_to_multiple_of=8     ) if training_args.fpaa else None

      # Initialize our Trainer
      UpperCAmelCase_	:						Any					   =		Trainer(
          model=__snake_case       ,	args=__snake_case       ,	train_dataset=__snake_case       ,	eval_dataset=__snake_case       ,	data_collator=__snake_case       ,	)

      # 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  _snake_case			(							__snake_case     )  ->  List[str]:






      '''simple docstring'''







      main()


if __name__ == "__main__":
   main()
