



"""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


__UpperCAmelCase   :   Dict        =       logging.getLogger(__name__)

@dataclass
class 						__lowerCAmelCase				:


					'''simple docstring'''


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



@dataclass
class 						__lowerCAmelCase				:


					'''simple docstring'''


					a__          =  field(
					    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}	)
					a__          =  field(
					    default=A__       ,metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}       ,)
					a__          =  field(
					    default=1_28       ,metadata={
					        'help': (
					            'The maximum total input sequence length after tokenization. Sequences longer '
					            'than this will be truncated, sequences shorter will be padded.'
					        )
					    }       ,)
					a__          =  field(
					    default=A__       ,metadata={'help': 'Overwrite the cached training and evaluation sets'}	)



def   A   (      ):




					"""simple docstring"""
					snake_case_    :Union[str, Any]										=    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_						,	snake_case_						,	snake_case_    :Any										=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
					else:
										snake_case_						,	snake_case_						,	snake_case_    :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[Any]										=    import_module("tasks"				)
					try:
										snake_case_    :str										=    getattr(SCREAMING_SNAKE_CASE_,  model_args.task_type				)
										snake_case_    :int										=    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",  SCREAMING_SNAKE_CASE_				)

					# Set seed
					set_seed(training_args.seed				)

					# Prepare CONLL-2003 task
					snake_case_    :str										=    token_classification_task.get_labels(data_args.labels				)
					snake_case_    :Optional[Any]										=    dict(enumerate(SCREAMING_SNAKE_CASE_				)				)
					snake_case_    :str										=    len(SCREAMING_SNAKE_CASE_				)

					# 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[Any]										=    AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path,  num_labels=SCREAMING_SNAKE_CASE_,  idalabel=SCREAMING_SNAKE_CASE_,  labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_				)},  cache_dir=model_args.cache_dir,  )
					snake_case_    :Tuple										=    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_    :List[Any]										=    AutoModelForTokenClassification.from_pretrained(
					    model_args.model_name_or_path,  from_tf=bool(".ckpt" in model_args.model_name_or_path				),  config=SCREAMING_SNAKE_CASE_,  cache_dir=model_args.cache_dir,  )

					# Get datasets
					snake_case_    :Dict										=    (
					    TokenClassificationDataset(
					        token_classification_task=SCREAMING_SNAKE_CASE_,  data_dir=data_args.data_dir,  tokenizer=SCREAMING_SNAKE_CASE_,  labels=SCREAMING_SNAKE_CASE_,  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_    :Optional[Any]										=    (
					    TokenClassificationDataset(
					        token_classification_task=SCREAMING_SNAKE_CASE_,  data_dir=data_args.data_dir,  tokenizer=SCREAMING_SNAKE_CASE_,  labels=SCREAMING_SNAKE_CASE_,  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_    :List[Any]										=    np.argmax(SCREAMING_SNAKE_CASE_,  axis=2				)

										snake_case_						,	snake_case_    :Optional[Any]										=    preds.shape

										snake_case_    :Union[str, Any]										=    [[] for _ in range(SCREAMING_SNAKE_CASE_				)]
										snake_case_    :Tuple										=    [[] for _ in range(SCREAMING_SNAKE_CASE_				)]

										for i in range(SCREAMING_SNAKE_CASE_				):
															for j in range(SCREAMING_SNAKE_CASE_				):
																				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_						,	snake_case_    :Tuple										=    align_predictions(p.predictions,  p.label_ids				)
										return {
										    "accuracy_score": accuracy_score(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_				),
										    "precision": precision_score(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_				),
										    "recall": recall_score(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_				),
										    "f1": fa_score(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_				),
										}

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

					# Initialize our Trainer
					snake_case_    :Optional[Any]										=    Trainer(
					    model=SCREAMING_SNAKE_CASE_,  args=SCREAMING_SNAKE_CASE_,  train_dataset=SCREAMING_SNAKE_CASE_,  eval_dataset=SCREAMING_SNAKE_CASE_,  compute_metrics=SCREAMING_SNAKE_CASE_,  data_collator=SCREAMING_SNAKE_CASE_,  )

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

										snake_case_    :str										=    trainer.evaluate()

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

															results.update(SCREAMING_SNAKE_CASE_				)

    # Predict
					if training_args.do_predict:
										snake_case_    :Union[str, Any]										=    TokenClassificationDataset(
										    token_classification_task=SCREAMING_SNAKE_CASE_,  data_dir=data_args.data_dir,  tokenizer=SCREAMING_SNAKE_CASE_,  labels=SCREAMING_SNAKE_CASE_,  model_type=config.model_type,  max_seq_length=data_args.max_seq_length,  overwrite_cache=data_args.overwrite_cache,  mode=Split.test,  )

										snake_case_						,	snake_case_						,	snake_case_    :Union[str, Any]										=    trainer.predict(SCREAMING_SNAKE_CASE_				)
										snake_case_						,	snake_case_    :Any										=    align_predictions(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_				)

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

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

					return results
def   A   (      _A				):




					"""simple docstring"""
					main()


if __name__ == "__main__":
				main()








import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()