

"""simple docstring"""




import logging
import os
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 classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


__A	:       Dict        =						logging.getLogger(__name__)





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





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

def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					# 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.
					A     =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
					A     ,       A     ,       A     =   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." )

					A     =   import_module("tasks" )

					try:
										A     =   getattr(lowercase__     ,				model_args.task_type )
										A     =   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     ,				)
					logger.info(
					    "n_replicas: %s, distributed training: %s, 16-bits training: %s"     ,				training_args.n_replicas     ,				bool(training_args.n_replicas > 1 )     ,				training_args.fpaa     ,				)
					logger.info("Training/evaluation parameters %s"     ,				lowercase__ )

					# Prepare Token Classification task
					A     =   token_classification_task.get_labels(data_args.labels )
					A     =   dict(enumerate(lowercase__ ) )
					A     =   len(lowercase__ )

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

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

					with training_args.strategy.scope():
										A     =   TFAutoModelForTokenClassification.from_pretrained(
										    model_args.model_name_or_path     ,				from_pt=bool(".bin" in model_args.model_name_or_path )     ,				config=lowercase__     ,				cache_dir=model_args.cache_dir     ,				)

					# Get datasets
					A     =   (
					    TFTokenClassificationDataset(
					        token_classification_task=lowercase__     ,				data_dir=data_args.data_dir     ,				tokenizer=lowercase__     ,				labels=lowercase__     ,				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
					)
					A     =   (
					    TFTokenClassificationDataset(
					        token_classification_task=lowercase__     ,				data_dir=data_args.data_dir     ,				tokenizer=lowercase__     ,				labels=lowercase__     ,				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(lowercase__     ,				lowercase__ ) -> Tuple[List[int], List[int]]:
										A     =   np.argmax(lowercase__     ,				axis=2 )
										A     ,       A     =   preds.shape
										A     =   [[] for _ in range(lowercase__ )]
										A     =   [[] for _ in range(lowercase__ )]

										for i in range(lowercase__ ):
															for j in range(lowercase__ ):
																				if label_ids[i, j] != -100:
																									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(lowercase__ ) -> Dict:
										A     ,       A     =   align_predictions(p.predictions     ,				p.label_ids )

										return {
										    "precision": precision_score(lowercase__     ,				lowercase__ ),
										    "recall": recall_score(lowercase__     ,				lowercase__ ),
										    "f1": fa_score(lowercase__     ,				lowercase__ ),
										}

					# Initialize our Trainer
					A     =   TFTrainer(
					    model=lowercase__     ,				args=lowercase__     ,				train_dataset=train_dataset.get_dataset() if train_dataset else None     ,				eval_dataset=eval_dataset.get_dataset() if eval_dataset else None     ,				compute_metrics=lowercase__     ,				)

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir )

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

										A     =   trainer.evaluate()
										A     =   os.path.join(training_args.output_dir     ,				"eval_results.txt" )

										with open(lowercase__     ,				"w" ) as writer:
															logger.info("***** Eval results *****" )

															for key, value in result.items():
																				logger.info("  %s = %s"     ,				lowercase__     ,				lowercase__ )
																				writer.write("%s = %s\n" % (key, value) )

															results.update(lowercase__ )

    # Predict
					if training_args.do_predict:
										A     =   TFTokenClassificationDataset(
										    token_classification_task=lowercase__     ,				data_dir=data_args.data_dir     ,				tokenizer=lowercase__     ,				labels=lowercase__     ,				model_type=config.model_type     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.test     ,				)

										A     ,       A     ,       A     =   trainer.predict(test_dataset.get_dataset() )
										A     ,       A     =   align_predictions(lowercase__     ,				lowercase__ )
										A     =   classification_report(lowercase__     ,				lowercase__ )

										logger.info("\n%s"     ,				lowercase__ )

										A     =   os.path.join(training_args.output_dir     ,				"test_results.txt" )

										with open(lowercase__     ,				"w" ) as writer:
															writer.write("%s\n" % report )

										# Save predictions
										A     =   os.path.join(training_args.output_dir     ,				"test_predictions.txt" )

										with open(lowercase__     ,				"w" ) as writer:
															with open(os.path.join(data_args.data_dir     ,				"test.txt" )     ,				"r" ) as f:
																				A     =   0

																				for line in f:
																									if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
																														writer.write(lowercase__ )

																														if not preds_list[example_id]:
																																			example_id += 1
																									elif preds_list[example_id]:
																														A     =   line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"

																														writer.write(lowercase__ )
																									else:
																														logger.warning("Maximum sequence length exceeded: No prediction for '%s'."     ,				line.split()[0] )

					return results


if __name__ == "__main__":
	main()












from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      List[str]       					='''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION     )
class    __A  (    datasets.Metric     ):


				def _snake_case							(		self				):
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,			citation=_CITATION ,			inputs_description=_KWARGS_DESCRIPTION ,			features=datasets.Features(
								        {
								            """predictions""": datasets.Sequence(datasets.Value("""int32"""				)				),
								            """references""": datasets.Sequence(datasets.Value("""int32"""				)				),
								        }
								        if self.config_name == """multilabel"""
								        else {
								            """predictions""": datasets.Value("""int32"""				),
								            """references""": datasets.Value("""int32"""				),
								        }				) ,			reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





