


'''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()


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





@dataclass
class    lowercase__  :





							'''simple docstring'''


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





@dataclass
class    lowercase__  :





							'''simple docstring'''


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







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Dict							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
				_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_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."""					)

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				import_module("""tasks"""					)

				try:
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				getattr(SCREAMING_SNAKE_CASE__							,				model_args.task_type					)
								_SCREAMING_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							,				)
				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"""							,				SCREAMING_SNAKE_CASE__					)

				# Prepare Token Classification task
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				token_classification_task.get_labels(data_args.labels					)
				_SCREAMING_SNAKE_CASE			:	Dict[int, str]							=				dict(enumerate(SCREAMING_SNAKE_CASE__					)					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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.

				_SCREAMING_SNAKE_CASE			:	int							=				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							,				)
				_SCREAMING_SNAKE_CASE			:	str							=				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():
								_SCREAMING_SNAKE_CASE			:	str							=				TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path							,				from_pt=bool(""".bin""" in model_args.model_name_or_path					)							,				config=SCREAMING_SNAKE_CASE__							,				cache_dir=model_args.cache_dir							,				)

				# Get datasets
				_SCREAMING_SNAKE_CASE			:	int							=				(
				    TFTokenClassificationDataset(
				        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
				)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				(
				    TFTokenClassificationDataset(
				        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(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					) -> Tuple[List[int], List[int]]:
								_SCREAMING_SNAKE_CASE			:	Dict							=				np.argmax(SCREAMING_SNAKE_CASE__							,				axis=2					)
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Dict							=				preds.shape
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				[[] for _ in range(SCREAMING_SNAKE_CASE__					)]
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				[[] 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] != -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(SCREAMING_SNAKE_CASE__					) -> Dict:
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				align_predictions(p.predictions							,				p.label_ids					)

								return {
								    "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__					),
								}

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

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

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

								_SCREAMING_SNAKE_CASE			:	List[Any]							=				trainer.evaluate()
								_SCREAMING_SNAKE_CASE			:	List[str]							=				os.path.join(training_args.output_dir							,				"""eval_results.txt"""					)

								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:
								_SCREAMING_SNAKE_CASE			:	Dict							=				TFTokenClassificationDataset(
								    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							,				)

								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				trainer.predict(test_dataset.get_dataset()					)
								_SCREAMING_SNAKE_CASE		,	_SCREAMING_SNAKE_CASE			:	str							=				align_predictions(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				classification_report(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

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

								_SCREAMING_SNAKE_CASE			:	Dict							=				os.path.join(training_args.output_dir							,				"""test_results.txt"""					)

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

								# Save predictions
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				os.path.join(training_args.output_dir							,				"""test_predictions.txt"""					)

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

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

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

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

				return results


if __name__ == "__main__":
				main()








'''simple docstring'''


import sys







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if not numbers:
								return 0
				if target == 0 and target in numbers:
								return 0
				if not isinstance(SCREAMING_SNAKE_CASE__							,				(list, tuple)					) or not all(
				    isinstance(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					) for number in numbers					):
								raise ValueError("""numbers must be an iterable of integers"""					)

				_SCREAMING_SNAKE_CASE			:	str							=				0
				_SCREAMING_SNAKE_CASE			:	int							=				sys.maxsize

				while right < len(SCREAMING_SNAKE_CASE__					):
								curr_sum += numbers[right]
								while curr_sum >= target and left <= right:
												_SCREAMING_SNAKE_CASE			:	Any							=				min(SCREAMING_SNAKE_CASE__							,				right - left + 1					)
												curr_sum -= numbers[left]
												left += 1
								right += 1

				return 0 if min_len == sys.maxsize else min_len




