





import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm



def _a       (			lowerCamelCase:     List[str]      ,       lowerCamelCase:     Any				)			->		Any:







      '''simple docstring'''






      if isinstance(lowerCamelCase      ,       (list, tuple)				):
            return honor_type(lowerCamelCase      ,       (move_to_device(lowerCamelCase      ,       lowerCamelCase				) for t in state)				)
      elif isinstance(lowerCamelCase      ,       lowerCamelCase				):
            return type(lowerCamelCase				)({k: move_to_device(lowerCamelCase      ,       lowerCamelCase				) for k, v in state.items()}				)
      elif isinstance(lowerCamelCase      ,       torch.Tensor				):
            return state.to(lowerCamelCase				)
      return state







class A_      (    torch.optim.Optimizer     ):




   def __init__(self			:Dict					,     _UpperCamelCase			:List[str]					,     _UpperCamelCase			:Optional[int]=True					,     _UpperCamelCase			:List[Any]=None				)->      List[Any]:
         __A      =       optimizer
         __A      =       scaler
         __A      =       AcceleratorState()
         __A      =       GradientState()
         __A      =       device_placement
         __A      =       False
         __A      =       None

         # Handle device placement
         if device_placement:
               __A      =       self.optimizer.state_dict()
               if self.accelerator_state.distributed_type == DistributedType.TPU:
                     xm.send_cpu_data_to_device(_UpperCamelCase					,     self.accelerator_state.device				)
               else:
                     __A      =       move_to_device(_UpperCamelCase					,     self.accelerator_state.device				)
               self.optimizer.load_state_dict(_UpperCamelCase				)




   @property
   def _lowerCAmelCase   (self			:Optional[Any]				)->      Optional[Any]:
         return self.optimizer.state




   @state.setter
   def _lowerCAmelCase   (self			:Optional[Any]					,     _UpperCamelCase			:Dict				)->      Union[str, Any]:
         __A      =       state




   @property
   def _lowerCAmelCase   (self			:Optional[int]				)->      Tuple:
         return self.optimizer.param_groups




   @param_groups.setter
   def _lowerCAmelCase   (self			:Optional[Any]					,     _UpperCamelCase			:int				)->      int:
         __A      =       param_groups




   @property
   def _lowerCAmelCase   (self			:Dict				)->      List[Any]:
         return self.optimizer.defaults




   @defaults.setter
   def _lowerCAmelCase   (self			:Any					,     _UpperCamelCase			:int				)->      Optional[Any]:
         __A      =       defaults




   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:Dict				)->      List[Any]:
         self.optimizer.add_param_group(_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Dict					,     _UpperCamelCase			:Union[str, Any]				)->      Tuple:
         if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
               xm.send_cpu_data_to_device(_UpperCamelCase					,     self.accelerator_state.device				)
         self.optimizer.load_state_dict(_UpperCamelCase				)




   def _lowerCAmelCase   (self			:Union[str, Any]				)->      Any:
         return self.optimizer.state_dict()




   def _lowerCAmelCase   (self			:str					,     _UpperCamelCase			:Dict=None				)->      Dict:
         if self.gradient_state.sync_gradients:
               __A      =       '''set_to_none''' in inspect.signature(self.optimizer.zero_grad				).parameters
               if accept_arg:
                     if set_to_none is None:
                           __A      =       False
                     self.optimizer.zero_grad(set_to_none=_UpperCamelCase				)
               else:
                     if set_to_none is not None:
                           raise ValueError('''`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'''				)
                     self.optimizer.zero_grad()




   def _lowerCAmelCase   (self			:Tuple					,     _UpperCamelCase			:Optional[Any]=None				)->      Union[str, Any]:
         if self.gradient_state.sync_gradients:
               if self.accelerator_state.distributed_type == DistributedType.TPU:
                     __A      =       {'''closure''': closure} if closure is not None else {}
                     xm.optimizer_step(self.optimizer					,     optimizer_args=_UpperCamelCase				)
               elif self.scaler is not None:
                     __A      =       False
                     if self._last_scale is None:
                           # `get_scale` is an async operation requiring full synchronization
                           # on CPU and GPUs before finishing. As a result, we store away
                           # the prior one to reduce the call overhead
                           __A      =       self.scaler.get_scale()
                           __A      =       True
                     self.scaler.step(self.optimizer					,     _UpperCamelCase				)
                     self.scaler.update()
                     __A      =       self.scaler.get_scale()
                     if not new_scale:
                           # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                           __A      =       scale_after < self._last_scale
                     __A      =       scale_after

               else:
                     self.optimizer.step(_UpperCamelCase				)




   def _lowerCAmelCase   (self			:List[str]					,     _UpperCamelCase			:Union[str, Any]				)->      Any:
         for param_group in self.optimizer.param_groups:
               __A      =       [parameters_map.get(_UpperCamelCase					,     _UpperCamelCase				) for p in param_group['''params''']]




   @property
   def _lowerCAmelCase   (self			:Union[str, Any]				)->      Any:
         warnings.warn(
             '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
             '''`optimizer.step_was_skipped` instead.'''					,     _UpperCamelCase					,     )
         return self._is_overflow




   @property
   def _lowerCAmelCase   (self			:List[str]				)->      Any:
         return self._is_overflow




   def __getstate__(self			:Optional[int]				)->      str:
         return self.__dict__.copy()






   def __setstate__(self			:Optional[int]					,     _UpperCamelCase			:List[str]				)->      List[str]:
         self.__dict__.update(_UpperCamelCase				)












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__      :							str									=  logging.getLogger(__name__)





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





@dataclass
class A_      :
   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=_lowerCamelCase				,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )





def _a       (			)			->		int:







      '''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(lowerCamelCase:     np.ndarray      ,       lowerCamelCase:     np.ndarray				) -> 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] != -1_00:
                              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:     EvalPrediction				) -> 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()





