



import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

__snake_case     :Dict			       =logging.getLogger(__name__)



@dataclass
class        lowerCAmelCase__					:
				A_   :     Optional[int]								=     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.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={
				        'help': (
				            'Whether to pad all samples to `max_seq_length`. '
				            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
				        )
				    } ,				)
				A_   :     Optional[int]								=     field(
				    default=_lowerCamelCase ,				metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of training examples to this '
				            'value if set.'
				        )
				    } ,				)
				A_   :     Optional[int]								=     field(
				    default=_lowerCamelCase ,				metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
				            'value if set.'
				        )
				    } ,				)
				A_   :     Optional[int]								=     field(
				    default=_lowerCamelCase ,				metadata={
				        'help': (
				            'For debugging purposes or quicker training, truncate the number of prediction examples to this '
				            'value if set.'
				        )
				    } ,				)





@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'}       )
				A_   :     Optional[str]								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Train language if it is different from the evaluation language.'}       )
				A_   :     Optional[str]								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
				A_   :     Optional[str]								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
				A_   :     Optional[str]								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,				)
				A_   :     Optional[bool]								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} ,				)
				A_   :     str								=     field(
				    default='main' ,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={
				        'help': (
				            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
				            'with private models).'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} ,				)






def  lowerCamelCase_	(      )       ->							List[str]:

							'''simple docstring'''






							A      						=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
							A						,   A						,   A      						=    parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry('run_xnli'			,	lowerCAmelCase__				)

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,	datefmt='%m/%d/%Y %H:%M:%S'			,	handlers=[logging.StreamHandler(sys.stdout				)]			,	)

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							A      						=    training_args.get_process_log_level()
							logger.setLevel(lowerCAmelCase__				)
							datasets.utils.logging.set_verbosity(lowerCAmelCase__				)
							transformers.utils.logging.set_verbosity(lowerCAmelCase__				)
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + F'''distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'''				)
							logger.info(F'''Training/evaluation parameters {training_args}'''				)

							# Detecting last checkpoint.
							A      						=    None
							if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
														A      						=    get_last_checkpoint(training_args.output_dir				)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
																					raise ValueError(
																					    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    'Use --overwrite_output_dir to overcome.'				)
														elif last_checkpoint is not None:
																					logger.info(
																					    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Set seed before initializing model.
							set_seed(training_args.seed				)

							# In distributed training, the load_dataset function guarantees that only one local process can concurrently
							# download the dataset.
							# Downloading and loading xnli dataset from the hub.
							if training_args.do_train:
														if model_args.train_language is None:
																					A      						=    load_dataset(
																					    'xnli'			,	model_args.language			,	split='train'			,	cache_dir=model_args.cache_dir			,	use_auth_token=True if model_args.use_auth_token else None			,	)
														else:
																					A      						=    load_dataset(
																					    'xnli'			,	model_args.train_language			,	split='train'			,	cache_dir=model_args.cache_dir			,	use_auth_token=True if model_args.use_auth_token else None			,	)
														A      						=    train_dataset.features['label'].names

							if training_args.do_eval:
														A      						=    load_dataset(
														    'xnli'			,	model_args.language			,	split='validation'			,	cache_dir=model_args.cache_dir			,	use_auth_token=True if model_args.use_auth_token else None			,	)
														A      						=    eval_dataset.features['label'].names

							if training_args.do_predict:
														A      						=    load_dataset(
														    'xnli'			,	model_args.language			,	split='test'			,	cache_dir=model_args.cache_dir			,	use_auth_token=True if model_args.use_auth_token else None			,	)
														A      						=    predict_dataset.features['label'].names

							# Labels
							A      						=    len(lowerCAmelCase__				)

							# Load pretrained model and tokenizer
							# In 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=lowerCAmelCase__			,	idalabel={str(lowerCAmelCase__				): label for i, label in enumerate(lowerCAmelCase__				)}			,	labelaid={label: i for i, label in enumerate(lowerCAmelCase__				)}			,	finetuning_task='xnli'			,	cache_dir=model_args.cache_dir			,	revision=model_args.model_revision			,	use_auth_token=True if model_args.use_auth_token else None			,	)
							A      						=    AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,	do_lower_case=model_args.do_lower_case			,	cache_dir=model_args.cache_dir			,	use_fast=model_args.use_fast_tokenizer			,	revision=model_args.model_revision			,	use_auth_token=True if model_args.use_auth_token else None			,	)
							A      						=    AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path			,	from_tf=bool('.ckpt' in model_args.model_name_or_path				)			,	config=lowerCAmelCase__			,	cache_dir=model_args.cache_dir			,	revision=model_args.model_revision			,	use_auth_token=True if model_args.use_auth_token else None			,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes			,	)

							# Preprocessing the datasets
							# Padding strategy
							if data_args.pad_to_max_length:
														A      						=    'max_length'
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														A      						=    False

							def preprocess_function(lowerCAmelCase__	:	Dict				):
														# Tokenize the texts
														return tokenizer(
														    examples['premise']			,	examples['hypothesis']			,	padding=lowerCAmelCase__			,	max_length=data_args.max_seq_length			,	truncation=lowerCAmelCase__			,	)

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					A      						=    min(len(lowerCAmelCase__				)			,	data_args.max_train_samples				)
																					A      						=    train_dataset.select(range(lowerCAmelCase__				)				)
														with training_args.main_process_first(desc='train dataset map pre-processing'				):
																					A      						=    train_dataset.map(
																					    lowerCAmelCase__			,	batched=lowerCAmelCase__			,	load_from_cache_file=not data_args.overwrite_cache			,	desc='Running tokenizer on train dataset'			,	)
														# Log a few random samples from the training set:
														for index in random.sample(range(len(lowerCAmelCase__				)				)			,	3				):
																					logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.'''				)

							if training_args.do_eval:
														if data_args.max_eval_samples is not None:
																					A      						=    min(len(lowerCAmelCase__				)			,	data_args.max_eval_samples				)
																					A      						=    eval_dataset.select(range(lowerCAmelCase__				)				)
														with training_args.main_process_first(desc='validation dataset map pre-processing'				):
																					A      						=    eval_dataset.map(
																					    lowerCAmelCase__			,	batched=lowerCAmelCase__			,	load_from_cache_file=not data_args.overwrite_cache			,	desc='Running tokenizer on validation dataset'			,	)

							if training_args.do_predict:
														if data_args.max_predict_samples is not None:
																					A      						=    min(len(lowerCAmelCase__				)			,	data_args.max_predict_samples				)
																					A      						=    predict_dataset.select(range(lowerCAmelCase__				)				)
														with training_args.main_process_first(desc='prediction dataset map pre-processing'				):
																					A      						=    predict_dataset.map(
																					    lowerCAmelCase__			,	batched=lowerCAmelCase__			,	load_from_cache_file=not data_args.overwrite_cache			,	desc='Running tokenizer on prediction dataset'			,	)

    # Get the metric function
							A      						=    evaluate.load('xnli'				)

							# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(lowerCAmelCase__	:	EvalPrediction				):
														A      						=    p.predictions[0] if isinstance(p.predictions			,	lowerCAmelCase__				) else p.predictions
														A      						=    np.argmax(lowerCAmelCase__			,	axis=1				)
														return metric.compute(predictions=lowerCAmelCase__			,	references=p.label_ids				)

							# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
							if data_args.pad_to_max_length:
														A      						=    default_data_collator
							elif training_args.fpaa:
														A      						=    DataCollatorWithPadding(lowerCAmelCase__			,	pad_to_multiple_of=8				)
							else:
														A      						=    None

							# Initialize our Trainer
							A      						=    Trainer(
							    model=lowerCAmelCase__			,	args=lowerCAmelCase__			,	train_dataset=train_dataset if training_args.do_train else None			,	eval_dataset=eval_dataset if training_args.do_eval else None			,	compute_metrics=lowerCAmelCase__			,	tokenizer=lowerCAmelCase__			,	data_collator=lowerCAmelCase__			,	)

							# Training
							if training_args.do_train:
														A      						=    None
														if training_args.resume_from_checkpoint is not None:
																					A      						=    training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					A      						=    last_checkpoint
														A      						=    trainer.train(resume_from_checkpoint=lowerCAmelCase__				)
														A      						=    train_result.metrics
														A      						=    (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase__				)
														)
														A      						=    min(lowerCAmelCase__			,	len(lowerCAmelCase__				)				)

														trainer.save_model()  # Saves the tokenizer too for easy upload

														trainer.log_metrics('train'			,	lowerCAmelCase__				)
														trainer.save_metrics('train'			,	lowerCAmelCase__				)
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														logger.info('*** Evaluate ***'				)
														A      						=    trainer.evaluate(eval_dataset=lowerCAmelCase__				)

														A      						=    data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase__				)
														A      						=    min(lowerCAmelCase__			,	len(lowerCAmelCase__				)				)

														trainer.log_metrics('eval'			,	lowerCAmelCase__				)
														trainer.save_metrics('eval'			,	lowerCAmelCase__				)

							# Prediction
							if training_args.do_predict:
														logger.info('*** Predict ***'				)
														A						,   A						,   A      						=    trainer.predict(lowerCAmelCase__			,	metric_key_prefix='predict'				)

														A      						=    (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCAmelCase__				)
														)
														A      						=    min(lowerCAmelCase__			,	len(lowerCAmelCase__				)				)

														trainer.log_metrics('predict'			,	lowerCAmelCase__				)
														trainer.save_metrics('predict'			,	lowerCAmelCase__				)

														A      						=    np.argmax(lowerCAmelCase__			,	axis=1				)
														A      						=    os.path.join(training_args.output_dir			,	'predictions.txt'				)
														if trainer.is_world_process_zero():
																					with open(lowerCAmelCase__			,	'w'				) as writer:
																												writer.write('index\tprediction\n'				)
																												for index, item in enumerate(lowerCAmelCase__				):
																																			A      						=    label_list[item]
																																			writer.write(F'''{index}\t{item}\n'''				)


if __name__ == "__main__":
				main()


import math


def 							__lowerCAmelCase     (							_UpperCamelCase		:	int						)		->				list[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      2
 SCREAMING_SNAKE_CASE		    =      int(math.sqrt(_UpperCamelCase						)						)  # Size of every segment
 SCREAMING_SNAKE_CASE		    =      [True] * (end + 1)
 SCREAMING_SNAKE_CASE		    =      []

 while start <= end:
  if temp[start] is True:
   in_prime.append(_UpperCamelCase						)
   for i in range(start * start		,				end + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False
  start += 1
 prime += in_prime

 SCREAMING_SNAKE_CASE		    =      end + 1
 SCREAMING_SNAKE_CASE		    =      min(2 * end		,				_UpperCamelCase						)

 while low <= n:
  SCREAMING_SNAKE_CASE		    =      [True] * (high - low + 1)
  for each in in_prime:
   SCREAMING_SNAKE_CASE		    =      math.floor(low / each						) * each
   if t < low:
    t += each

   for j in range(_UpperCamelCase		,				high + 1		,				_UpperCamelCase						):
    SCREAMING_SNAKE_CASE		    =      False

  for j in range(len(_UpperCamelCase						)						):
   if temp[j] is True:
    prime.append(j + low						)

  SCREAMING_SNAKE_CASE		    =      high + 1
  SCREAMING_SNAKE_CASE		    =      min(high + end		,				_UpperCamelCase						)

 return prime




print(sieve(10**6))

