import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCamelCase					:   str							=		logging.getLogger(__name__)


@dataclass
class 						lowercase    :
							lowercase__						:       str								=							field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
							lowercase__						:       Optional[str]								=							field(
							    default=a				,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
							lowercase__						:       Optional[str]								=							field(
							    default="""NER"""				,       metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}     )
							lowercase__						:       Optional[str]								=							field(
							    default=a				,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
							lowercase__						:       bool								=							field(default=a				,       metadata={"""help""": """Set this flag to use fast tokenization."""}     )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							lowercase__						:       Optional[str]								=							field(
							    default=a				,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,       )



@dataclass
class 						lowercase    :
							lowercase__						:       str								=							field(
							    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}     )
							lowercase__						:       Optional[str]								=							field(
							    default=a				,       metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}				,       )
							lowercase__						:       int								=							field(
							    default=128				,       metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }				,       )
							lowercase__						:       bool								=							field(
							    default=a				,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )





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.

					SCREAMING_SNAKE_CASE    			=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
					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.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
					else:
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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."  )

					SCREAMING_SNAKE_CASE    			=       import_module("tasks"  )
					try:
										SCREAMING_SNAKE_CASE    			=       getattr(UpperCAmelCase__      , model_args.task_type  )
										SCREAMING_SNAKE_CASE    			=       token_classification_task_clazz()
					except AttributeError:
										raise ValueError(
										    F"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
										    F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"  )

					# 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"      , UpperCAmelCase__  )

					# Set seed
					set_seed(training_args.seed  )

					# Prepare CONLL-2003 task
					SCREAMING_SNAKE_CASE    			=       token_classification_task.get_labels(data_args.labels  )
					SCREAMING_SNAKE_CASE    			=       dict(enumerate(UpperCAmelCase__  )  )
					SCREAMING_SNAKE_CASE    			=       len(UpperCAmelCase__  )

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

					SCREAMING_SNAKE_CASE    			=       AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path      , num_labels=UpperCAmelCase__      , idalabel=UpperCAmelCase__      , labelaid={label: i for i, label in enumerate(UpperCAmelCase__  )}      , cache_dir=model_args.cache_dir      , )
					SCREAMING_SNAKE_CASE    			=       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      , use_fast=model_args.use_fast      , )
					SCREAMING_SNAKE_CASE    			=       AutoModelForTokenClassification.from_pretrained(
					    model_args.model_name_or_path      , from_tf=bool(".ckpt" in model_args.model_name_or_path  )      , config=UpperCAmelCase__      , cache_dir=model_args.cache_dir      , )

					# Get datasets
					SCREAMING_SNAKE_CASE    			=       (
					    TokenClassificationDataset(
					        token_classification_task=UpperCAmelCase__      , data_dir=data_args.data_dir      , tokenizer=UpperCAmelCase__      , labels=UpperCAmelCase__      , model_type=config.model_type      , max_seq_length=data_args.max_seq_length      , overwrite_cache=data_args.overwrite_cache      , mode=Split.train      , )
					    if training_args.do_train
					    else None
					)
					SCREAMING_SNAKE_CASE    			=       (
					    TokenClassificationDataset(
					        token_classification_task=UpperCAmelCase__      , data_dir=data_args.data_dir      , tokenizer=UpperCAmelCase__      , labels=UpperCAmelCase__      , model_type=config.model_type      , max_seq_length=data_args.max_seq_length      , overwrite_cache=data_args.overwrite_cache      , mode=Split.dev      , )
					    if training_args.do_eval
					    else None
					)

					def align_predictions(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray  ) -> Tuple[List[int], List[int]]:
										SCREAMING_SNAKE_CASE    			=       np.argmax(UpperCAmelCase__      , axis=2  )

										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       preds.shape

										SCREAMING_SNAKE_CASE    			=       [[] for _ in range(UpperCAmelCase__  )]
										SCREAMING_SNAKE_CASE    			=       [[] for _ in range(UpperCAmelCase__  )]

										for i in range(UpperCAmelCase__  ):
															for j in range(UpperCAmelCase__  ):
																				if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																									out_label_list[i].append(label_map[label_ids[i][j]]  )
																									preds_list[i].append(label_map[preds[i][j]]  )

										return preds_list, out_label_list

					def compute_metrics(UpperCAmelCase__     :							EvalPrediction  ) -> Dict:
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       align_predictions(p.predictions      , p.label_ids  )
										return {
										    "accuracy_score": accuracy_score(UpperCAmelCase__      , UpperCAmelCase__  ),
										    "precision": precision_score(UpperCAmelCase__      , UpperCAmelCase__  ),
										    "recall": recall_score(UpperCAmelCase__      , UpperCAmelCase__  ),
										    "f1": fa_score(UpperCAmelCase__      , UpperCAmelCase__  ),
										}

					# Data collator
					SCREAMING_SNAKE_CASE    			=       DataCollatorWithPadding(UpperCAmelCase__      , pad_to_multiple_of=8  ) if training_args.fpaa else None

					# Initialize our Trainer
					SCREAMING_SNAKE_CASE    			=       Trainer(
					    model=UpperCAmelCase__      , args=UpperCAmelCase__      , train_dataset=UpperCAmelCase__      , eval_dataset=UpperCAmelCase__      , compute_metrics=UpperCAmelCase__      , data_collator=UpperCAmelCase__      , )

					# 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_process_zero():
															tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
					SCREAMING_SNAKE_CASE    			=       {}
					if training_args.do_eval:
										logger.info("*** Evaluate ***"  )

										SCREAMING_SNAKE_CASE    			=       trainer.evaluate()

										SCREAMING_SNAKE_CASE    			=       os.path.join(training_args.output_dir      , "eval_results.txt"  )
										if trainer.is_world_process_zero():
															with open(UpperCAmelCase__      , "w"  ) as writer:
																				logger.info("***** Eval results *****"  )
																				for key, value in result.items():
																									logger.info("  %s = %s"      , UpperCAmelCase__      , UpperCAmelCase__  )
																									writer.write("%s = %s\n" % (key, value)  )

															results.update(UpperCAmelCase__  )

    # Predict
					if training_args.do_predict:
										SCREAMING_SNAKE_CASE    			=       TokenClassificationDataset(
										    token_classification_task=UpperCAmelCase__      , data_dir=data_args.data_dir      , tokenizer=UpperCAmelCase__      , labels=UpperCAmelCase__      , model_type=config.model_type      , max_seq_length=data_args.max_seq_length      , overwrite_cache=data_args.overwrite_cache      , mode=Split.test      , )

										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       trainer.predict(UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       align_predictions(UpperCAmelCase__      , UpperCAmelCase__  )

										SCREAMING_SNAKE_CASE    			=       os.path.join(training_args.output_dir      , "test_results.txt"  )
										if trainer.is_world_process_zero():
															with open(UpperCAmelCase__      , "w"  ) as writer:
																				for key, value in metrics.items():
																									logger.info("  %s = %s"      , UpperCAmelCase__      , UpperCAmelCase__  )
																									writer.write("%s = %s\n" % (key, value)  )

        # Save predictions
										SCREAMING_SNAKE_CASE    			=       os.path.join(training_args.output_dir      , "test_predictions.txt"  )
										if trainer.is_world_process_zero():
															with open(UpperCAmelCase__      , "w"  ) as writer:
																				with open(os.path.join(data_args.data_dir      , "test.txt"  )      , "r"  ) as f:
																									token_classification_task.write_predictions_to_file(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )

					return results





def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]  ):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
							main()





from __future__ import annotations

import math





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):

					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(UpperCAmelCase__  ) + 1  )      , 6  ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True


_lowerCamelCase					:   Tuple							=		[num for num in range(3, 10_00_01, 2) if not is_prime(num)]





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

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

					return []





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


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




