


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()


snake_case__     							= logging.getLogger(__name__)



@dataclass
class     snake_case__  :
				__lowerCAmelCase		=      field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
				__lowerCAmelCase		=      field(
				    default=_A						,       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=_A						,       metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
				__lowerCAmelCase		=      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.
				__lowerCAmelCase		=      field(
				    default=_A						,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}						,       )






@dataclass
class     snake_case__  :
				__lowerCAmelCase		=      field(
				    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}  )
				__lowerCAmelCase		=      field(
				    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=_A						,       metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )






def  __SCREAMING_SNAKE_CASE       (				)       ->  Optional[int]:
						__magic_name__				:							Tuple				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
						__magic_name__				:							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.'				)

						__magic_name__				:							str				=      import_module('tasks'				)

						try:
												__magic_name__				:							Dict				=      getattr(_UpperCAmelCase      ,						model_args.task_type				)
												__magic_name__				:							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      ,						)
						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'      ,						_UpperCAmelCase				)

						# Prepare Token Classification task
						__magic_name__				:							Tuple				=      token_classification_task.get_labels(data_args.labels				)
						__magic_name__				:							Dict[int, str]				=      dict(enumerate(_UpperCAmelCase				)				)
						__magic_name__				:							Dict				=      len(_UpperCAmelCase				)

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

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

						with training_args.strategy.scope():
												__magic_name__				:							Optional[int]				=      TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path      ,						from_pt=bool('.bin' in model_args.model_name_or_path				)      ,						config=_UpperCAmelCase      ,						cache_dir=model_args.cache_dir      ,						)

						# Get datasets
						__magic_name__				:							str				=      (
						    TFTokenClassificationDataset(
						        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
						)
						__magic_name__				:							List[str]				=      (
						    TFTokenClassificationDataset(
						        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(lowercase__  :Optional[int]      ,						lowercase__  :Tuple				) -> Tuple[List[int], List[int]]:
												__magic_name__				:							Optional[Any]				=      np.argmax(_UpperCAmelCase      ,						axis=2				)
												__magic_name__				:							Tuple				=      preds.shape
												__magic_name__				:							Tuple				=      [[] for _ in range(_UpperCAmelCase				)]
												__magic_name__				:							Optional[int]				=      [[] for _ in range(_UpperCAmelCase				)]

												for i in range(_UpperCAmelCase				):
																		for j in range(_UpperCAmelCase				):
																								if label_ids[i, j] != -1_0_0:
																														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__  :Optional[Any]				) -> Dict:
												__magic_name__				:							str				=      align_predictions(p.predictions      ,						p.label_ids				)

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

						# Initialize our Trainer
						__magic_name__				:							Tuple				=      TFTrainer(
						    model=_UpperCAmelCase      ,						args=_UpperCAmelCase      ,						train_dataset=train_dataset.get_dataset() if train_dataset else None      ,						eval_dataset=eval_dataset.get_dataset() if eval_dataset else None      ,						compute_metrics=_UpperCAmelCase      ,						)

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

						# Evaluation
						__magic_name__				:							List[str]				=      {}
						if training_args.do_eval:
												logger.info('*** Evaluate ***'				)

												__magic_name__				:							Tuple				=      trainer.evaluate()
												__magic_name__				:							Optional[int]				=      os.path.join(training_args.output_dir      ,						'eval_results.txt'				)

												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:
												__magic_name__				:							List[Any]				=      TFTokenClassificationDataset(
												    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      ,						)

												__magic_name__				:							Optional[int]				=      trainer.predict(test_dataset.get_dataset()				)
												__magic_name__				:							List[str]				=      align_predictions(_UpperCAmelCase      ,						_UpperCAmelCase				)
												__magic_name__				:							int				=      classification_report(_UpperCAmelCase      ,						_UpperCAmelCase				)

												logger.info('\n%s'      ,						_UpperCAmelCase				)

												__magic_name__				:							Optional[int]				=      os.path.join(training_args.output_dir      ,						'test_results.txt'				)

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

												# Save predictions
												__magic_name__				:							int				=      os.path.join(training_args.output_dir      ,						'test_predictions.txt'				)

												with open(_UpperCAmelCase      ,						'w'				) as writer:
																		with open(os.path.join(data_args.data_dir      ,						'test.txt'				)      ,						'r'				) as f:
																								__magic_name__				:							int				=      0

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

																																				if not preds_list[example_id]:
																																										example_id += 1
																														elif preds_list[example_id]:
																																				__magic_name__				:							Optional[int]				=      line.split()[0] + ' ' + preds_list[example_id].pop(0				) + '\n'

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

						return results


if __name__ == "__main__":
			main()






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets
@contextmanager
def  __SCREAMING_SNAKE_CASE       (				)       ->  Tuple:
						def filter_log(lowercase__  :List[Any]				):
												return False if "This IS expected if you are initializing" in record.msg else True

						__magic_name__				:							Any				=      datasets.utils.logging.get_logger('transformers.modeling_utils'				)
						logger.addFilter(lowercase__				)
						try:
												yield
						finally:
												logger.removeFilter(lowercase__				)


snake_case__     							= '\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

snake_case__     							= '\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

snake_case__     							= '\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION  )
class     snake_case__  (      datasets.Metric  ):
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,      citation=_CITATION     ,      homepage='https://github.com/Tiiiger/bert_score'     ,      inputs_description=_KWARGS_DESCRIPTION     ,      features=datasets.Features(
										        {
										            'predictions': datasets.Value('string'     ,      id='sequence'      ),
										            'references': datasets.Sequence(datasets.Value('string'     ,      id='sequence'      )     ,      id='references'      ),
										        }      )     ,      codebase_urls=['https://github.com/Tiiiger/bert_score']     ,      reference_urls=[
										        'https://github.com/Tiiiger/bert_score',
										        'https://arxiv.org/abs/1904.09675',
										    ]     ,      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=64     ,      _UpperCAmelCase=4     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=False     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=False     ,      ):




										"""simple docstring"""




										__magic_name__				:							List[str]				=      bert_score.utils.get_hash
										__magic_name__				:							Dict				=      bert_score.BERTScorer

										if version.parse(bert_score.__version__      ) >= version.parse('0.3.10'      ):
																__magic_name__				:							Any				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
																__magic_name__				:							Tuple				=      functools.partial(_UpperCAmelCase     ,      use_fast_tokenizer=_UpperCAmelCase      )
										elif use_fast_tokenizer:
																raise ImportWarning(
																    'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
																    'You can install it with `pip install "bert-score>=0.3.10"`.'      )

										if model_type is None:
																assert lang is not None, "either lang or model_type should be specified"
																__magic_name__				:							List[Any]				=      bert_score.utils.langamodel[lang.lower()]

										if num_layers is None:
																__magic_name__				:							Optional[Any]				=      bert_score.utils.modelalayers[model_type]

										__magic_name__				:							List[str]				=      get_hash(
										    model=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      use_custom_baseline=baseline_path is not None     ,      )

										with filter_logging_context():
																if not hasattr(self     ,      'cached_bertscorer'      ) or self.cached_bertscorer.hash != hashcode:
																						__magic_name__				:							Optional[int]				=      scorer(
																						    model_type=_UpperCAmelCase     ,      num_layers=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      nthreads=_UpperCAmelCase     ,      all_layers=_UpperCAmelCase     ,      idf=_UpperCAmelCase     ,      device=_UpperCAmelCase     ,      lang=_UpperCAmelCase     ,      rescale_with_baseline=_UpperCAmelCase     ,      baseline_path=_UpperCAmelCase     ,      )

										((__magic_name__)		, (__magic_name__)		, (__magic_name__))				:							List[Any]				=      self.cached_bertscorer.score(
										    cands=_UpperCAmelCase     ,      refs=_UpperCAmelCase     ,      verbose=_UpperCAmelCase     ,      batch_size=_UpperCAmelCase     ,      )
										__magic_name__				:							int				=      {
										    'precision': P.tolist(),
										    'recall': R.tolist(),
										    'f1': F.tolist(),
										    'hashcode': hashcode,
										}
										return output_dict
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if references is not None:
																__magic_name__				:							List[str]				=      [[ref] if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else ref for ref in references]
										super().add_batch(predictions=_UpperCAmelCase     ,      references=_UpperCAmelCase     ,      **_UpperCAmelCase      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None     ,      _UpperCAmelCase=None     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																__magic_name__				:							List[Any]				=      [reference]
										super().add(prediction=_UpperCAmelCase     ,      reference=_UpperCAmelCase     ,      **_UpperCAmelCase      )


