






"""simple docstring"""



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A_     =				logging.getLogger(__name__)






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






@dataclass
class 				__SCREAMING_SNAKE_CASE    :
							snake_case_  =				field(
							    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()		)}		)
							snake_case_  =				field(
							    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}		)
							snake_case_  =				field(
							    default=128							,		metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }							,		)
							snake_case_  =				field(
							    default=UpperCamelCase							,		metadata={'help': 'Overwrite the cached training and evaluation sets'}		)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       List[InputFeatures]   )		->Dict[str, torch.Tensor]:
						A__    :	Tuple			=	default_data_collator(UpperCAmelCase__   )
						A__    :	Optional[Any]			=	batch.pop("""pairID""",    UpperCAmelCase__   )
						return batch




def   _lowerCAmelCase		(				)		->List[str]:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						A__    :	Any			=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
						A__	,	A__	,	A__    :	Dict			=	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""",    UpperCAmelCase__   )

						# Set seed
						set_seed(training_args.seed   )

						try:
												A__    :	Optional[int]			=	hans_tasks_num_labels[data_args.task_name]
						except KeyError:
												raise ValueError("""Task not found: %s""" % (data_args.task_name)   )

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

						A__    :	Optional[int]			=	AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path,    num_labels=UpperCAmelCase__,    finetuning_task=data_args.task_name,    cache_dir=model_args.cache_dir,    )
						A__    :	Optional[int]			=	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,    )
						A__    :	Optional[Any]			=	AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path   ),    config=UpperCAmelCase__,    cache_dir=model_args.cache_dir,    )

						# Get datasets
						A__    :	Any			=	(
						    HansDataset(
						        data_dir=data_args.data_dir,    tokenizer=UpperCAmelCase__,    task=data_args.task_name,    max_seq_length=data_args.max_seq_length,    overwrite_cache=data_args.overwrite_cache,    )
						    if training_args.do_train
						    else None
						)
						A__    :	Optional[Any]			=	(
						    HansDataset(
						        data_dir=data_args.data_dir,    tokenizer=UpperCAmelCase__,    task=data_args.task_name,    max_seq_length=data_args.max_seq_length,    overwrite_cache=data_args.overwrite_cache,    evaluate=UpperCAmelCase__,    )
						    if training_args.do_eval
						    else None
						)

						# Initialize our Trainer
						A__    :	Union[str, Any]			=	Trainer(
						    model=UpperCAmelCase__,    args=UpperCAmelCase__,    train_dataset=UpperCAmelCase__,    eval_dataset=UpperCAmelCase__,    data_collator=UpperCAmelCase__,    )

						# 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   )

    # Evaluation
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""   )

												A__    :	Union[str, Any]			=	trainer.predict(UpperCAmelCase__   )
												A__    :	Dict			=	output.predictions
												A__    :	Optional[int]			=	np.argmax(UpperCAmelCase__,    axis=1   )

												A__    :	List[Any]			=	[ex.pairID for ex in eval_dataset]
												A__    :	List[Any]			=	os.path.join(training_args.output_dir,    """hans_predictions.txt"""   )
												A__    :	List[str]			=	eval_dataset.get_labels()
												if trainer.is_world_master():
																		with open(UpperCAmelCase__,    """w"""   ) as writer:
																								writer.write("""pairID,gold_label\n"""   )
																								for pid, pred in zip(UpperCAmelCase__,    UpperCAmelCase__   ):
																														writer.write("""ex""" + str(UpperCAmelCase__   ) + """,""" + label_list[int(UpperCAmelCase__   )] + """\n"""   )

												trainer._log(output.metrics   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Optional[Any]   )		->int:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()














"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float   )		->list[int]:
						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"""   )

						A__    :	int			=	value * saturation
						A__    :	Any			=	hue / 6_0
						A__    :	Any			=	chroma * (1 - abs(hue_section % 2 - 1   ))
						A__    :	Optional[Any]			=	value - chroma

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

						return [red, green, blue]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->list[float]:
						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"""   )

						A__    :	int			=	red / 2_5_5
						A__    :	List[str]			=	green / 2_5_5
						A__    :	Optional[int]			=	blue / 2_5_5
						A__    :	Optional[int]			=	max(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Tuple			=	value - min(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Any			=	0 if value == 0 else chroma / value

						if chroma == 0:
												A__    :	Optional[Any]			=	0.0
						elif value == float_red:
												A__    :	Optional[Any]			=	6_0 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												A__    :	Tuple			=	6_0 * (2 + (float_blue - float_red) / chroma)
						else:
												A__    :	int			=	6_0 * (4 + (float_red - float_green) / chroma)

						A__    :	Optional[int]			=	(hue + 3_6_0) % 3_6_0

						return [hue, saturation, value]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       list[float],    UpperCAmelCase__							:       list[float]   )		->bool:
						A__    :	Optional[Any]			=	abs(hsv_a[0] - hsv_a[0]   ) < 0.2
						A__    :	str			=	abs(hsv_a[1] - hsv_a[1]   ) < 0.002
						A__    :	Optional[int]			=	abs(hsv_a[2] - hsv_a[2]   ) < 0.002

						return check_hue and check_saturation and check_value






