



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()


_lowercase				:					int     					=logging.getLogger(__name__)
@dataclass
class lowerCAmelCase_       :







	'''simple docstring'''




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


@dataclass
class lowerCAmelCase_       :







	'''simple docstring'''




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




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			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(lowercase__						:					Union[str, Any]				,					lowercase__						:					Any	) -> 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(lowercase__						:					int	) -> 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()



import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =CustomConfig



       def __init__(							self ,     _A	)				->				int:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.hidden_size ,     config.hidden_size	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Optional[int]:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          pass







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       =NoSuperInitConfig



       def __init__(							self ,     _A	)				->				Optional[int]:
          super().__init__(_A	)
          SCREAMING_SNAKE_CASE_	=      torch.nn.Linear(config.attribute ,     config.attribute	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Any:
          return self.linear(_A	)




       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          pass


