






import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__	:			Union[str, Any]			   = logging.getLogger(__name__)
@dataclass
class       _UpperCAmelCase					:

				"""simple docstring"""




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



@dataclass
class       _UpperCAmelCase					:

				"""simple docstring"""




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






def   a			(						):






				'''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.

				lowercase__    =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
				if len(sys.argv				) == 2 and sys.argv[1].endswith('''.json'''				):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase__ ,						lowercase__ ,						lowercase__    =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
				else:
								lowercase__ ,						lowercase__ ,						lowercase__    =			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.'''				)

				lowercase__    =			import_module('''tasks'''				)
				try:
								lowercase__    =			getattr(lowerCamelCase_		,   model_args.task_type				)
								lowercase__    =			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 if training_args.local_rank in [-1, 0] else logging.WARN		,   )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,   training_args.local_rank		,   training_args.device		,   training_args.n_gpu		,   bool(training_args.local_rank != -1				)		,   training_args.fpaa		,   )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank				):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('''Training/evaluation parameters %s'''		,   lowerCamelCase_				)

				# Set seed
				set_seed(training_args.seed				)

				# Prepare CONLL-2003 task
				lowercase__    =			token_classification_task.get_labels(data_args.labels				)
				lowercase__    =			dict(enumerate(lowerCamelCase_				)				)
				lowercase__    =			len(lowerCamelCase_				)

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

				lowercase__    =			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		,   )
				lowercase__    =			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		,   )
				lowercase__    =			AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path		,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)		,   config=lowerCamelCase_		,   cache_dir=model_args.cache_dir		,   )

				# Get datasets
				lowercase__    =			(
				    TokenClassificationDataset(
				        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
				)
				lowercase__    =			(
				    TokenClassificationDataset(
				        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]]:
								lowercase__    =			np.argmax(lowerCamelCase_		,   axis=2				)

								lowercase__ ,						lowercase__    =			preds.shape

								lowercase__    =			[[] for _ in range(lowerCamelCase_				)]
								lowercase__    =			[[] for _ in range(lowerCamelCase_				)]

								for i in range(lowerCamelCase_				):
												for j in range(lowerCamelCase_				):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				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:
								lowercase__ ,						lowercase__    =			align_predictions(p.predictions		,   p.label_ids				)
								return {
								    "accuracy_score": accuracy_score(lowerCamelCase_		,   lowerCamelCase_				),
								    "precision": precision_score(lowerCamelCase_		,   lowerCamelCase_				),
								    "recall": recall_score(lowerCamelCase_		,   lowerCamelCase_				),
								    "f1": fa_score(lowerCamelCase_		,   lowerCamelCase_				),
								}

				# Data collator
				lowercase__    =			DataCollatorWithPadding(lowerCamelCase_		,   pad_to_multiple_of=8				) if training_args.fpaa else None

				# Initialize our Trainer
				lowercase__    =			Trainer(
				    model=lowerCamelCase_		,   args=lowerCamelCase_		,   train_dataset=lowerCamelCase_		,   eval_dataset=lowerCamelCase_		,   compute_metrics=lowerCamelCase_		,   data_collator=lowerCamelCase_		,   )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir				)

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

								lowercase__    =			trainer.evaluate()

								lowercase__    =			os.path.join(training_args.output_dir		,   '''eval_results.txt'''				)
								if trainer.is_world_process_zero():
												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:
								lowercase__    =			TokenClassificationDataset(
								    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		,   )

								lowercase__ ,						lowercase__ ,						lowercase__    =			trainer.predict(lowerCamelCase_				)
								lowercase__ ,						lowercase__    =			align_predictions(lowerCamelCase_		,   lowerCamelCase_				)

								lowercase__    =			os.path.join(training_args.output_dir		,   '''test_results.txt'''				)
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_		,   '''w'''				) as writer:
																for key, value in metrics.items():
																				logger.info('''  %s = %s'''		,   lowerCamelCase_		,   lowerCamelCase_				)
																				writer.write('''%s = %s\n''' % (key, value)				)

        # Save predictions
								lowercase__    =			os.path.join(training_args.output_dir		,   '''test_predictions.txt'''				)
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_		,   '''w'''				) as writer:
																with open(os.path.join(data_args.data_dir		,   '''test.txt'''				)		,   '''r'''				) as f:
																				token_classification_task.write_predictions_to_file(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)

				return results






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()












from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
							from fairscale.optim import OSS


A__	:			Tuple			   = logging.get_logger(__name__)

A__	:			int			   = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Optional[int],  lowerCamelCase						:    int=None,  lowerCamelCase						:    int=None,  *lowerCamelCase						:    List[Any],  **lowerCamelCase						:    Any  ):

								'''simple docstring'''
								super().__init__(*lowerCamelCase,  **lowerCamelCase  )

								if config is None:
												assert isinstance(self.model,  lowerCamelCase  ), (
												    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
												    F""" {self.model.__class__}"""
												)
												lowercase__    =			self.model.config
								else:
												lowercase__    =			config

								lowercase__    =			data_args
								lowercase__    =			self.config.tgt_vocab_size if isinstance(self.config,  lowerCamelCase  ) else self.config.vocab_size

								if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
												assert self.config.pad_token_id is not None, (
												    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
												    " calculation or doing label smoothing."
												)

								if self.config.pad_token_id is None and self.config.eos_token_id is not None:
												logger.warning(
												    F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
												    ''' padding..'''  )

								if self.args.label_smoothing == 0:
												lowercase__    =			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id  )
								else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												lowercase__    =			label_smoothed_nll_loss




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if self.optimizer is None:
												lowercase__    =			['''bias''', '''LayerNorm.weight''']
												lowercase__    =			[
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay  )],
												        '''weight_decay''': self.args.weight_decay,
												    },
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay  )],
												        '''weight_decay''': 0.0,
												    },
												]
												lowercase__    =			Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
																lowercase__    =			Adafactor
																lowercase__    =			{'''scale_parameter''': False, '''relative_step''': False}
												else:
																lowercase__    =			AdamW
																lowercase__    =			{
																    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																    '''eps''': self.args.adam_epsilon,
																}
												lowercase__    =			self.args.learning_rate
												if self.sharded_ddp:
																lowercase__    =			OSS(
																    params=lowerCamelCase,  optim=lowerCamelCase,  **lowerCamelCase,  )
												else:
																lowercase__    =			optimizer_cls(lowerCamelCase,  **lowerCamelCase  )

								if self.lr_scheduler is None:
												lowercase__    =			self._get_lr_scheduler(lowerCamelCase  )
								else:  # ignoring --lr_scheduler
												logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
												lowercase__    =			schedule_func(self.optimizer  )
								elif self.args.lr_scheduler == "constant_w_warmup":
												lowercase__    =			schedule_func(self.optimizer,  num_warmup_steps=self.args.warmup_steps  )
								else:
												lowercase__    =			schedule_func(
												    self.optimizer,  num_warmup_steps=self.args.warmup_steps,  num_training_steps=lowerCamelCase  )
								return scheduler




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if isinstance(self.train_dataset,  torch.utils.data.IterableDataset  ):
												return None
								elif is_torch_tpu_available():
												return get_tpu_sampler(self.train_dataset  )
								else:
												if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size,  distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),  )

												return (
												    RandomSampler(self.train_dataset  )
												    if self.args.local_rank == -1
												    else DistributedSampler(self.train_dataset  )
												)




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								if self.args.label_smoothing == 0:
												if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																lowercase__    =			model(**lowerCamelCase,  use_cache=lowerCamelCase  )[0]
																lowercase__    =			self.loss_fn(logits.view(-1,  logits.shape[-1]  ),  labels.view(-1  )  )
												else:
																# compute usual loss via models
																lowercase__ ,						lowercase__    =			model(**lowerCamelCase,  labels=lowerCamelCase,  use_cache=lowerCamelCase  )[:2]
								else:
												# compute label smoothed loss
												lowercase__    =			model(**lowerCamelCase,  use_cache=lowerCamelCase  )[0]
												lowercase__    =			torch.nn.functional.log_softmax(lowerCamelCase,  dim=-1  )
												lowercase__ ,						lowercase__    =			self.loss_fn(lowerCamelCase,  lowerCamelCase,  self.args.label_smoothing,  ignore_index=self.config.pad_token_id  )
								return loss, logits




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			inputs.pop('''labels'''  )
								lowercase__ ,						lowercase__    =			self._compute_loss(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
								return loss




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    nn.Module,  lowerCamelCase						:    Dict[str, Union[torch.Tensor, Any]],  lowerCamelCase						:    bool,  lowerCamelCase						:    Optional[List[str]] = None,  ):

								'''simple docstring'''
								lowercase__    =			self._prepare_inputs(lowerCamelCase  )

								lowercase__    =			{
								    '''max_length''': self.data_args.val_max_target_length
								    if self.data_args is not None
								    else self.config.max_length,
								    '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
								}

								if self.args.predict_with_generate and not self.args.prediction_loss_only:
												lowercase__    =			self.model.generate(
												    inputs['''input_ids'''],  attention_mask=inputs['''attention_mask'''],  **lowerCamelCase,  )
												# in case the batch is shorter than max length, the output should be padded
												if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																lowercase__    =			self._pad_tensors_to_max_len(lowerCamelCase,  gen_kwargs['''max_length''']  )

								lowercase__    =			inputs.pop('''labels'''  )
								with torch.no_grad():
												# compute loss on predict data
												lowercase__ ,						lowercase__    =			self._compute_loss(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			loss.mean().detach()
								if self.args.prediction_loss_only:
												return (loss, None, None)

								lowercase__    =			generated_tokens if self.args.predict_with_generate else logits

								if labels.shape[-1] < gen_kwargs["max_length"]:
												lowercase__    =			self._pad_tensors_to_max_len(lowerCamelCase,  gen_kwargs['''max_length''']  )

								return (loss, logits, labels)




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Any  ):

								'''simple docstring'''
								# If PAD token is not defined at least EOS token has to be defined
								lowercase__    =			self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

								if pad_token_id is None:
												raise ValueError(
												    '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
												    F""" padded to `max_length`={max_length}"""  )

								lowercase__    =			pad_token_id * torch.ones(
								    (tensor.shape[0], max_length),  dtype=tensor.dtype,  device=tensor.device  )
								lowercase__    =			tensor
								return padded_tensor




