



"""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


snake_case_								=			logging.getLogger(__name__)







@dataclass
class  A_     :



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







@dataclass
class  A_     :



						"""simple docstring"""
						__UpperCamelCase													=				field(
						    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()							)}							)
						__UpperCamelCase													=				field(
						    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}							)
						__UpperCamelCase													=				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."""
						        )
						    }      ,  )
						__UpperCamelCase													=				field(
						    default=SCREAMING_SNAKE_CASE_      ,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)


def 					_lowerCAmelCase (  lowercase_		):
						UpperCAmelCase			 =					default_data_collator(lowercase_		)
						UpperCAmelCase			 =					batch.pop('pairID'						,      lowercase_		)
						return batch

def 					_lowerCAmelCase (  ):
						# 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.

						UpperCAmelCase			 =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
						UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					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'						,      lowercase_		)

						# Set seed
						set_seed(training_args.seed		)

						try:
												UpperCAmelCase			 =					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.

						UpperCAmelCase			 =					AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,      num_labels=lowercase_						,      finetuning_task=data_args.task_name						,      cache_dir=model_args.cache_dir						,      )
						UpperCAmelCase			 =					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						,      )
						UpperCAmelCase			 =					AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path						,      from_tf=bool('.ckpt' in model_args.model_name_or_path		)						,      config=lowercase_						,      cache_dir=model_args.cache_dir						,      )

						# Get datasets
						UpperCAmelCase			 =					(
						    HansDataset(
						        data_dir=data_args.data_dir						,      tokenizer=lowercase_						,      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
						)
						UpperCAmelCase			 =					(
						    HansDataset(
						        data_dir=data_args.data_dir						,      tokenizer=lowercase_						,      task=data_args.task_name						,      max_seq_length=data_args.max_seq_length						,      overwrite_cache=data_args.overwrite_cache						,      evaluate=lowercase_						,      )
						    if training_args.do_eval
						    else None
						)

						# Initialize our Trainer
						UpperCAmelCase			 =					Trainer(
						    model=lowercase_						,      args=lowercase_						,      train_dataset=lowercase_						,      eval_dataset=lowercase_						,      data_collator=lowercase_						,      )

						# 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 ***'		)

												UpperCAmelCase			 =					trainer.predict(lowercase_		)
												UpperCAmelCase			 =					output.predictions
												UpperCAmelCase			 =					np.argmax(lowercase_						,      axis=1		)

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

												trainer._log(output.metrics		)

def 					_lowerCAmelCase (  lowercase_		):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
				main()






'''simple docstring'''




import itertools





def 	_UpperCAmelCase       (				_UpperCamelCase       :      int = 1_00_00_00  )      -> int:
    A_      =  [1]

    for i in itertools.count(len(_UpperCamelCase  )  ):
        A_      =  0
        for j in itertools.count(1  ):
            A_      =  -1 if j % 2 == 0 else +1
            A_      =  (j * j * 3 - j) // 2
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number
            index += j
            if index > i:
                break
            item += partitions[i - index] * sign
            item %= number

        if item == 0:
            return i
        partitions.append(_UpperCamelCase  )

    return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")






