



'''simple docstring'''




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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase							:Union[str, Any]				    =					logging.getLogger(__name__)







@dataclass
class 	_lowerCAmelCase							:
	__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=__UpperCAmelCase      ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
	__SCREAMING_SNAKE_CASE   :		Optional[str]					=					field(
	    default=__UpperCAmelCase      ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
	__SCREAMING_SNAKE_CASE   :		Optional[str]					=					field(
	    default=__UpperCAmelCase      ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,			)
	__SCREAMING_SNAKE_CASE   :		bool					=					field(default=__UpperCAmelCase      ,			metadata={'help': 'Whether tp freeze the encoder.'} )
	__SCREAMING_SNAKE_CASE   :		bool					=					field(default=__UpperCAmelCase      ,			metadata={'help': 'Whether  to freeze the embeddings.'} )







@dataclass
class 	_lowerCAmelCase							:
	__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   :		Optional[str]					=					field(
	    default='summarization'      ,			metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,			)
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(
	    default=1_024      ,			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   :		Optional[int]					=					field(
	    default=128      ,			metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }      ,			)
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(
	    default=142      ,			metadata={
	        'help': (
	            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded. '
	            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
	            'during ``evaluate`` and ``predict``.'
	        )
	    }      ,			)
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(
	    default=142      ,			metadata={
	        'help': (
	            'The maximum total sequence length for test target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }      ,			)
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(default=-1      ,			metadata={'help': '# training examples. -1 means use all.'} )
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(default=-1      ,			metadata={'help': '# validation examples. -1 means use all.'} )
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(default=-1      ,			metadata={'help': '# test examples. -1 means use all.'} )
	__SCREAMING_SNAKE_CASE   :		Optional[str]					=					field(default=__UpperCAmelCase      ,			metadata={'help': 'Source language id for translation.'} )
	__SCREAMING_SNAKE_CASE   :		Optional[str]					=					field(default=__UpperCAmelCase      ,			metadata={'help': 'Target language id for translation.'} )
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(default=__UpperCAmelCase      ,			metadata={'help': '# num_beams to use for evaluation.'} )
	__SCREAMING_SNAKE_CASE   :		bool					=					field(
	    default=__UpperCAmelCase      ,			metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,			)






def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			logger.info(f'***** {split} metrics *****'    )
			for key in sorted(metrics.keys()    ):
						logger.info(f'  {key} = {metrics[key]}'    )
			save_json(lowerCamelCase__						,      os.path.join(lowerCamelCase__						,      f'{split}_results.json'    )    )



def 	a			( ):
			'''simple docstring'''





			A_	:						Dict	=     HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)    )

			if len(sys.argv    ) == 2 and sys.argv[1].endswith(""".json"""    ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						A_,    A_,    A_	:						int	=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
			else:
						A_,    A_,    A_	:						int	=     parser.parse_args_into_dataclasses()

			check_output_dir(lowerCamelCase__    )

			# 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.parallel_mode == ParallelMode.DISTRIBUTED    )						,      training_args.fpaa						,      )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()
			# 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()
			logger.info("""Training/evaluation parameters %s"""						,      lowerCamelCase__    )

			# Set seed
			set_seed(training_args.seed    )

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

			A_	:						List[str]	=     AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,      cache_dir=model_args.cache_dir						,      )

			A_	:						Optional[Any]	=     ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
			for p in extra_model_params:
						if getattr(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
									assert hasattr(lowerCamelCase__						,      lowerCamelCase__    ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
									setattr(lowerCamelCase__						,      lowerCamelCase__						,      getattr(lowerCamelCase__						,      lowerCamelCase__    )    )

			A_	:						Optional[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						,      )
			A_	:						List[Any]	=     AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path						,      from_tf=""".ckpt""" in model_args.model_name_or_path						,      config=lowerCamelCase__						,      cache_dir=model_args.cache_dir						,      )

			# use task specific params
			use_task_specific_params(lowerCamelCase__						,      data_args.task    )

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						A_	:						Tuple	=     model.config.num_beams

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(lowerCamelCase__						,      (MBartTokenizer, MBartTokenizerFast)    ):
						assert (
						    data_args.tgt_lang is not None and data_args.src_lang is not None
						), "mBart requires --tgt_lang and --src_lang"
						if isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
									A_	:						List[str]	=     tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									A_	:						Dict	=     tokenizer.convert_tokens_to_ids(data_args.tgt_lang    )

			if model_args.freeze_embeds:
						freeze_embeds(lowerCamelCase__    )
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder()    )
						assert_all_frozen(model.get_encoder()    )

			A_	:						Optional[int]	=     SeqaSeqDataset

			# Get datasets
			A_	:						Union[str, Any]	=     (
			    dataset_class(
			        lowerCamelCase__						,      type_path="""train"""						,      data_dir=data_args.data_dir						,      n_obs=data_args.n_train						,      max_target_length=data_args.max_target_length						,      max_source_length=data_args.max_source_length						,      prefix=model.config.prefix or """"""						,      )
			    if training_args.do_train
			    else None
			)
			A_	:						Tuple	=     (
			    dataset_class(
			        lowerCamelCase__						,      type_path="""val"""						,      data_dir=data_args.data_dir						,      n_obs=data_args.n_val						,      max_target_length=data_args.val_max_target_length						,      max_source_length=data_args.max_source_length						,      prefix=model.config.prefix or """"""						,      )
			    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
			    else None
			)
			A_	:						Dict	=     (
			    dataset_class(
			        lowerCamelCase__						,      type_path="""test"""						,      data_dir=data_args.data_dir						,      n_obs=data_args.n_test						,      max_target_length=data_args.test_max_target_length						,      max_source_length=data_args.max_source_length						,      prefix=model.config.prefix or """"""						,      )
			    if training_args.do_predict
			    else None
			)

			# Initialize our Trainer
			A_	:						Union[str, Any]	=     (
			    build_compute_metrics_fn(data_args.task						,      lowerCamelCase__    ) if training_args.predict_with_generate else None
			)
			A_	:						Any	=     SeqaSeqTrainer(
			    model=lowerCamelCase__						,      args=lowerCamelCase__						,      data_args=lowerCamelCase__						,      train_dataset=lowerCamelCase__						,      eval_dataset=lowerCamelCase__						,      data_collator=SeqaSeqDataCollator(
			        lowerCamelCase__						,      lowerCamelCase__						,      model.config.decoder_start_token_id						,      training_args.tpu_num_cores    )						,      compute_metrics=lowerCamelCase__						,      tokenizer=lowerCamelCase__						,      )

			A_	:						List[str]	=     {}
			# Training
			if training_args.do_train:
						logger.info("""*** Train ***"""    )

						A_	:						int	=     trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
						A_	:						str	=     train_result.metrics
						A_	:						Optional[int]	=     data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics("""train"""						,      lowerCamelCase__						,      training_args.output_dir    )
									all_metrics.update(lowerCamelCase__    )

									# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir						,      """trainer_state.json"""    )    )

									# For convenience, we also re-save the tokenizer to the same directory,
									# so that you can share your model easily on huggingface.co/models =)
									tokenizer.save_pretrained(training_args.output_dir    )

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

						A_	:						Dict	=     trainer.evaluate(metric_key_prefix="""val"""    )
						A_	:						List[Any]	=     data_args.n_val
						A_	:						str	=     round(metrics["""val_loss"""]						,      4    )

						if trainer.is_world_process_zero():
									handle_metrics("""val"""						,      lowerCamelCase__						,      training_args.output_dir    )
									all_metrics.update(lowerCamelCase__    )

			if training_args.do_predict:
						logger.info("""*** Predict ***"""    )

						A_	:						Optional[int]	=     trainer.predict(test_dataset=lowerCamelCase__						,      metric_key_prefix="""test"""    )
						A_	:						Any	=     test_output.metrics
						A_	:						List[Any]	=     data_args.n_test

						if trainer.is_world_process_zero():
									A_	:						List[Any]	=     round(metrics["""test_loss"""]						,      4    )
									handle_metrics("""test"""						,      lowerCamelCase__						,      training_args.output_dir    )
									all_metrics.update(lowerCamelCase__    )

									if training_args.predict_with_generate:
												A_	:						List[str]	=     tokenizer.batch_decode(
												    test_output.predictions						,      skip_special_tokens=lowerCamelCase__						,      clean_up_tokenization_spaces=lowerCamelCase__    )
												A_	:						Union[str, Any]	=     lmap(str.strip						,      lowerCamelCase__    )
												write_txt_file(lowerCamelCase__						,      os.path.join(training_args.output_dir						,      """test_generations.txt"""    )    )

			if trainer.is_world_process_zero():
						save_json(lowerCamelCase__						,      os.path.join(training_args.output_dir						,      """all_results.json"""    )    )

			return all_metrics



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			main()


if __name__ == "__main__":
						main()




'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if num <= 0:
						A_	:						List[Any]	=     f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(lowerCamelCase__    )

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(lowerCamelCase__    )

									# Set multiples of start be False
									for i in range(start * start						,      num + 1						,      lowerCamelCase__    ):
												if sieve[i] is True:
															A_	:						List[Any]	=     False

						start += 1

			for j in range(end + 1						,      num + 1    ):
						if sieve[j] is True:
									prime.append(lowerCamelCase__    )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))