
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  :			Dict     				=				logging.getLogger(__name__)







@dataclass
class     __A:
				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     __A:
				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=1_2_8						,     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    (    a__						) ->						Dict[str, torch.Tensor]:
				__a    							=					default_data_collator(snake_case_						)
				__a    							=					batch.pop('''pairID'''		,					snake_case_						)
				return batch


def 					__lowerCAmelCase    (    ) ->						Optional[int]:
				# 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    							=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
				__a    							=					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_						)

				# Set seed
				set_seed(training_args.seed						)

				try:
								__a    							=					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    							=					AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,					num_labels=snake_case_		,					finetuning_task=data_args.task_name		,					cache_dir=model_args.cache_dir		,					)
				__a    							=					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    							=					AutoModelForSequenceClassification.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
				__a    							=					(
				    HansDataset(
				        data_dir=data_args.data_dir		,					tokenizer=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
				)
				__a    							=					(
				    HansDataset(
				        data_dir=data_args.data_dir		,					tokenizer=snake_case_		,					task=data_args.task_name		,					max_seq_length=data_args.max_seq_length		,					overwrite_cache=data_args.overwrite_cache		,					evaluate=snake_case_		,					)
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				__a    							=					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						)

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

								__a    							=					trainer.predict(snake_case_						)
								__a    							=					output.predictions
								__a    							=					np.argmax(snake_case_		,					axis=1						)

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

								trainer._log(output.metrics						)


def 					__lowerCAmelCase    (    a__						) ->						Union[str, Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()








from itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()