

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





@dataclass
class 					snake_case			:
				SCREAMING_SNAKE_CASE_     :   str        =						field(
				    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()						)}						)
				SCREAMING_SNAKE_CASE_     :   str        =						field(
				    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}						)
				SCREAMING_SNAKE_CASE_     :   int        =						field(
				    default=1_28,							metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    },							)
				SCREAMING_SNAKE_CASE_     :   bool        =						field(
				    default=__snake_case,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			Dict[str, torch.Tensor]:
		__lowerCAmelCase: int					   =    default_data_collator(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: int					   =    batch.pop("pairID"      ,				__SCREAMING_SNAKE_CASE				)
		return batch






def 					a__					(					)			->			Any:
		# 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.

		__lowerCAmelCase: Dict					   =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
		__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: Tuple					   =    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"      ,				__SCREAMING_SNAKE_CASE				)

		# Set seed
		set_seed(training_args.seed				)

		try:
				__lowerCAmelCase: Optional[Any]					   =    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.

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

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

		# Initialize our Trainer
		__lowerCAmelCase: Any					   =    Trainer(
		    model=__SCREAMING_SNAKE_CASE      ,				args=__SCREAMING_SNAKE_CASE      ,				train_dataset=__SCREAMING_SNAKE_CASE      ,				eval_dataset=__SCREAMING_SNAKE_CASE      ,				data_collator=__SCREAMING_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				)

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

				__lowerCAmelCase: str					   =    trainer.predict(__SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase: int					   =    output.predictions
				__lowerCAmelCase: Optional[int]					   =    np.argmax(__SCREAMING_SNAKE_CASE      ,				axis=1				)

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

				trainer._log(output.metrics				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			Any:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
			main()





"""simple docstring"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[float, float]:
		assert (
		    c % greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				) == 0
		)  # greatest_common_divisor(a,b) function implemented below
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # extended_gcd(a,b) function implemented below
		__lowerCAmelCase: Optional[int]					   =    c / d
		return (r * x, r * y)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = 2				)			->			None:
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)): Optional[Any]					   =    diophantine(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # Initial value
		__lowerCAmelCase: Optional[Any]					   =    greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Any					   =    a // d
		__lowerCAmelCase: str					   =    b // d

		for i in range(__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: Dict					   =    xa + i * q
				__lowerCAmelCase: List[str]					   =    ya - i * p
				print(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		if a < b:
				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    b, a

		while a % b != 0:
				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    b, a % b

		return b






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[int, int, int]:
		assert a >= 0 and b >= 0

		if b == 0:
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    a, 1, 0
		else:
				((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				a % b				)
				__lowerCAmelCase: Dict					   =    q
				__lowerCAmelCase: int					   =    p - q * (a // b)

		assert a % d == 0 and b % d == 0
		assert d == a * x + b * y

		return (d, x, y)


if __name__ == "__main__":
			from doctest import testmod

			testmod(name="diophantine", verbose=True)
			testmod(name="diophantine_all_soln", verbose=True)
			testmod(name="extended_gcd", verbose=True)
			testmod(name="greatest_common_divisor", verbose=True)


