




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


lowercase__				:     str 		=						logging.getLogger(__name__)
@dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





						lowerCAmelCase_     		=   field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
						lowerCAmelCase_     		=   field(
						    default=SCREAMING_SNAKE_CASE_       ,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
						lowerCAmelCase_     		=   field(
						    default='''NER'''       ,   metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}			)
						lowerCAmelCase_     		=   field(
						    default=SCREAMING_SNAKE_CASE_       ,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
						lowerCAmelCase_     		=   field(default=SCREAMING_SNAKE_CASE_       ,   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.
						lowerCAmelCase_     		=   field(
						    default=SCREAMING_SNAKE_CASE_       ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,   )







@dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





						lowerCAmelCase_     		=   field(
						    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}			)
						lowerCAmelCase_     		=   field(
						    default=SCREAMING_SNAKE_CASE_       ,   metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}       ,   )
						lowerCAmelCase_     		=   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.'''
						        )
						    }       ,   )
						lowerCAmelCase_     		=   field(
						    default=SCREAMING_SNAKE_CASE_       ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)







def 						lowerCamelCase__    (		):







	'''simple docstring'''





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

	snake_case_		    =     import_module("tasks"							)
	try:
		snake_case_		    =     getattr(_lowerCAmelCase     ,	model_args.task_type							)
		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"     ,	_lowerCAmelCase							)

	# Set seed
	set_seed(training_args.seed							)

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

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

	snake_case_		    =     AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,	num_labels=_lowerCAmelCase     ,	idalabel=_lowerCAmelCase     ,	labelaid={label: i for i, label in enumerate(_lowerCAmelCase							)}     ,	cache_dir=model_args.cache_dir     ,	)
	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     ,	)
	snake_case_		    =     AutoModelForTokenClassification.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     ,	)

	# Get datasets
	snake_case_		    =     (
	    TokenClassificationDataset(
	        token_classification_task=_lowerCAmelCase     ,	data_dir=data_args.data_dir     ,	tokenizer=_lowerCAmelCase     ,	labels=_lowerCAmelCase     ,	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_		    =     (
	    TokenClassificationDataset(
	        token_classification_task=_lowerCAmelCase     ,	data_dir=data_args.data_dir     ,	tokenizer=_lowerCAmelCase     ,	labels=_lowerCAmelCase     ,	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(_A     ,	_A							) -> Tuple[List[int], List[int]]:
		snake_case_		    =     np.argmax(_lowerCAmelCase     ,	axis=2							)

		snake_case_		    =     preds.shape

		snake_case_		    =     [[] for _ in range(_lowerCAmelCase							)]
		snake_case_		    =     [[] for _ in range(_lowerCAmelCase							)]

		for i in range(_lowerCAmelCase							):
			for j in range(_lowerCAmelCase							):
				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(_A							) -> Dict:
		snake_case_		    =     align_predictions(p.predictions     ,	p.label_ids							)
		return {
		    "accuracy_score": accuracy_score(_lowerCAmelCase     ,	_lowerCAmelCase							),
		    "precision": precision_score(_lowerCAmelCase     ,	_lowerCAmelCase							),
		    "recall": recall_score(_lowerCAmelCase     ,	_lowerCAmelCase							),
		    "f1": fa_score(_lowerCAmelCase     ,	_lowerCAmelCase							),
		}

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

	# Initialize our Trainer
	snake_case_		    =     Trainer(
	    model=_lowerCAmelCase     ,	args=_lowerCAmelCase     ,	train_dataset=_lowerCAmelCase     ,	eval_dataset=_lowerCAmelCase     ,	compute_metrics=_lowerCAmelCase     ,	data_collator=_lowerCAmelCase     ,	)

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

		snake_case_		    =     trainer.evaluate()

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

			results.update(_lowerCAmelCase							)

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

		snake_case_		    =     trainer.predict(_lowerCAmelCase							)
		snake_case_		    =     align_predictions(_lowerCAmelCase     ,	_lowerCAmelCase							)

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

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

	return results


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	main()


if __name__ == "__main__":
		main()





'''simple docstring'''





def        a_     (							_lowerCAmelCase       )      ->      list:
  if n_term == "":
    return []
  __lowerCamelCase       :       list           = []
  for temp in range(int(_lowerCAmelCase       )       ):
    series.append(F'1/{temp + 1}' if series else '1'       )
  return series


if __name__ == "__main__":
   _UpperCamelCase											=						input('Enter the last number (nth term) of the Harmonic Series')
   print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
   print(harmonic_series(nth_term))






