
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,
)


__A							=logging.getLogger(__name__)




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
	lowerCAmelCase__   =					field(
	    default=snake_case_     ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
	lowerCAmelCase__   =					field(
	    default=snake_case_     ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
	lowerCAmelCase__   =					field(
	    default=snake_case_     ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,			)
	lowerCAmelCase__   =					field(default=snake_case_     ,			metadata={'help': 'Whether tp freeze the encoder.'}		)
	lowerCAmelCase__   =					field(default=snake_case_     ,			metadata={'help': 'Whether  to freeze the embeddings.'}		)




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}		)
	lowerCAmelCase__   =					field(
	    default='summarization'     ,			metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}     ,			)
	lowerCAmelCase__   =					field(
	    default=10_24     ,			metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }     ,			)
	lowerCAmelCase__   =					field(
	    default=1_28     ,			metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }     ,			)
	lowerCAmelCase__   =					field(
	    default=1_42     ,			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``.'
	        )
	    }     ,			)
	lowerCAmelCase__   =					field(
	    default=1_42     ,			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.'
	        )
	    }     ,			)
	lowerCAmelCase__   =					field(default=-1     ,			metadata={'help': '# training examples. -1 means use all.'}		)
	lowerCAmelCase__   =					field(default=-1     ,			metadata={'help': '# validation examples. -1 means use all.'}		)
	lowerCAmelCase__   =					field(default=-1     ,			metadata={'help': '# test examples. -1 means use all.'}		)
	lowerCAmelCase__   =					field(default=snake_case_     ,			metadata={'help': 'Source language id for translation.'}		)
	lowerCAmelCase__   =					field(default=snake_case_     ,			metadata={'help': 'Target language id for translation.'}		)
	lowerCAmelCase__   =					field(default=snake_case_     ,			metadata={'help': '# num_beams to use for evaluation.'}		)
	lowerCAmelCase__   =					field(
	    default=snake_case_     ,			metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}     ,			)





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				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      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.

				lowerCamelCase_         =     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.
								lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_         =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_         =     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.

				lowerCamelCase_         =     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      ,					)

				lowerCamelCase_         =     ("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__       )       )

				lowerCamelCase_         =     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_         =     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:
								lowerCamelCase_         =     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__       ):
												lowerCamelCase_         =     tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												lowerCamelCase_         =     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()       )

				lowerCamelCase_         =     SeqaSeqDataset

				# Get datasets
				lowerCamelCase_         =     (
				    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
				)
				lowerCamelCase_         =     (
				    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
				)
				lowerCamelCase_         =     (
				    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
				lowerCamelCase_         =     (
				    build_compute_metrics_fn(data_args.task      ,					lowerCamelCase__       ) if training_args.predict_with_generate else None
				)
				lowerCamelCase_         =     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__      ,					)

				lowerCamelCase_         =     {}
				# Training
				if training_args.do_train:
								logger.info("*** Train ***"       )

								lowerCamelCase_         =     trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
								lowerCamelCase_         =     train_result.metrics
								lowerCamelCase_         =     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 ***"       )

								lowerCamelCase_         =     trainer.evaluate(metric_key_prefix="val"       )
								lowerCamelCase_         =     data_args.n_val
								lowerCamelCase_         =     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 ***"       )

								lowerCamelCase_         =     trainer.predict(test_dataset=lowerCamelCase__      ,					metric_key_prefix="test"       )
								lowerCamelCase_         =     test_output.metrics
								lowerCamelCase_         =     data_args.n_test

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

												if training_args.predict_with_generate:
																lowerCamelCase_         =     tokenizer.batch_decode(
																    test_output.predictions      ,					skip_special_tokens=lowerCamelCase__      ,					clean_up_tokenization_spaces=lowerCamelCase__       )
																lowerCamelCase_         =     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      lowerCamelCase_					(							lowerCamelCase__       ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
			main()




from __future__ import annotations

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True


__A							=[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
								raise ValueError("n must be an integer"       )
				if n <= 0:
								raise ValueError("n must be >= 0"       )

				lowerCamelCase_         =     []
				for num in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     0
								while 2 * i * i <= odd_composites[num]:
												lowerCamelCase_         =     odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__       ):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]       )
												if len(lowerCamelCase__       ) == n:
																return list_nums

				return []





def      lowerCamelCase_					(							):
				return compute_nums(1       )[0]


if __name__ == "__main__":
			print(F"""{solution() = }""")


