"""simple docstring"""







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


_a					:					Optional[int]=			logging.getLogger(__name__)

@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				str   =		field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
				UpperCAmelCase    :				Optional[str]   =		field(
				    default=lowercase				,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}						)
				UpperCAmelCase    :				Optional[str]   =		field(
				    default="""NER"""				,     metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}						)
				UpperCAmelCase    :				Optional[str]   =		field(
				    default=lowercase				,     metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}						)
				UpperCAmelCase    :				bool   =		field(default=lowercase				,     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.
				UpperCAmelCase    :				Optional[str]   =		field(
				    default=lowercase				,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,     )







@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				str   =		field(
				    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}						)
				UpperCAmelCase    :				Optional[str]   =		field(
				    default=lowercase				,     metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}				,     )
				UpperCAmelCase    :				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."""
				        )
				    }				,     )
				UpperCAmelCase    :				bool   =		field(
				    default=lowercase				,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}						)







def   __UpperCAmelCase    (   )							->      str:

	'''simple docstring'''





	__snake_case      :							Optional[int]						=			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.
		__snake_case      :							int						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
	else:
		__snake_case      :							Union[str, Any]						=			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.'  )

	__snake_case      :							Optional[int]						=			import_module('tasks'  )
	try:
		__snake_case      :							List[Any]						=			getattr(UpperCAmelCase_      ,							model_args.task_type  )
		__snake_case      :							TokenClassificationTask						=			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
	__snake_case      :							int						=			token_classification_task.get_labels(data_args.labels  )
	__snake_case      :							Dict[int, str]						=			dict(enumerate(UpperCAmelCase_  )  )
	__snake_case      :							Any						=			len(UpperCAmelCase_  )

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

	__snake_case      :							Optional[int]						=			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      ,							)
	__snake_case      :							Union[str, 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      ,							use_fast=model_args.use_fast      ,							)
	__snake_case      :							Optional[int]						=			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
	__snake_case      :							Any						=			(
	    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
	)
	__snake_case      :							List[str]						=			(
	    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]]:
		__snake_case      :							List[str]						=			np.argmax(UpperCAmelCase_      ,							axis=2  )

		__snake_case      :							int						=			preds.shape

		__snake_case      :							str						=			[[] for _ in range(UpperCAmelCase_  )]
		__snake_case      :							Optional[Any]						=			[[] 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:
		__snake_case      :							Optional[int]						=			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
	__snake_case      :							List[Any]						=			DataCollatorWithPadding(UpperCAmelCase_      ,							pad_to_multiple_of=8  ) if training_args.fpaa else None

	# Initialize our Trainer
	__snake_case      :							int						=			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
	__snake_case      :							Tuple						=			{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'  )

		__snake_case      :							List[Any]						=			trainer.evaluate()

		__snake_case      :							Optional[Any]						=			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:
		__snake_case      :							Optional[Any]						=			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      ,							)

		__snake_case      :							List[str]						=			trainer.predict(UpperCAmelCase_  )
		__snake_case      :							Any						=			align_predictions(UpperCAmelCase_      ,							UpperCAmelCase_  )

		__snake_case      :							Tuple						=			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
		__snake_case      :							List[Any]						=			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   __UpperCAmelCase    (   UpperCAmelCase_   :    List[str]  )							->      int:

	'''simple docstring'''





	main()


if __name__ == "__main__":
	main()

"""simple docstring"""







from decimal import Decimal, getcontext
from math import ceil, factorial
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	if not isinstance(UpperCAmelCase_      ,							UpperCAmelCase_  ):
		raise TypeError('Undefined for non-integers'  )
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'  )

	__snake_case      :							Any						=			precision
	__snake_case      :							Optional[int]						=			ceil(precision / 14  )
	__snake_case      :							List[Any]						=			42_68_80 * Decimal(1_00_05  ).sqrt()
	__snake_case      :							Optional[Any]						=			1
	__snake_case      :							Union[str, Any]						=			13_59_14_09
	__snake_case      :							int						=			Decimal(UpperCAmelCase_  )
	for k in range(1      ,							UpperCAmelCase_  ):
		__snake_case      :							Union[str, Any]						=			factorial(6 * k  ) // (factorial(3 * k  ) * factorial(UpperCAmelCase_  ) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
	return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
	_a					:					List[Any]=			50
	print(f'''The first {n} digits of pi is: {pi(n)}''')





