






"""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							:						Tuple   							=			logging.getLogger(__name__)







@dataclass
class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





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







@dataclass
class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





							_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=__snake_case							,			metadata={'help': 'Overwrite the cached training and evaluation sets'}				)





def 							SCREAMING_SNAKE_CASE							(   ):
					# 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(lowerCamelCase_			,  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'''			,  lowerCamelCase_)

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

					# 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=lowerCamelCase_			,  idalabel=lowerCamelCase_			,  labelaid={label: i for i, label in enumerate(lowerCamelCase_)}			,  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=lowerCamelCase_			,  cache_dir=model_args.cache_dir			,  )

					# Get datasets
					a__ 							=	(
					    TFTokenClassificationDataset(
					        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
					)
					a__ 							=	(
					    TFTokenClassificationDataset(
					        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(lowerCamelCase_			,  lowerCamelCase_) -> Tuple[List[int], List[int]]:
										a__ 							=	np.argmax(lowerCamelCase_			,  axis=2)
										a__							,a__ 							=	preds.shape
										a__ 							=	[[] for _ in range(lowerCamelCase_)]
										a__ 							=	[[] for _ in range(lowerCamelCase_)]

										for i in range(lowerCamelCase_):
															for j in range(lowerCamelCase_):
																				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(lowerCamelCase_) -> Dict:
										a__							,a__ 							=	align_predictions(p.predictions			,  p.label_ids)

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

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

					# 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(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:
										a__ 							=	TFTokenClassificationDataset(
										    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			,  )

										a__							,a__							,a__ 							=	trainer.predict(test_dataset.get_dataset())
										a__							,a__ 							=	align_predictions(lowerCamelCase_			,  lowerCamelCase_)
										a__ 							=	classification_report(lowerCamelCase_			,  lowerCamelCase_)

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

										a__ 							=	os.path.join(training_args.output_dir			,  '''test_results.txt''')

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

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

										with open(lowerCamelCase_			,  '''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(lowerCamelCase_)

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

					return results


if __name__ == "__main__":
	main()










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__a							:						Tuple   							=			{}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a							:						str   							=			['CpmTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a							:						List[str]   							=			['CpmTokenizerFast']


if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_cpm import CpmTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_cpm_fast import CpmTokenizerFast

else:
	import sys

	__a							:						Tuple   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


