






'''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_      = logging.getLogger(__name__)





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





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





def 			__UpperCAmelCase				()		->				Optional[int]:

      '''simple docstring'''


      a_				     =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      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(_UpperCAmelCase	,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"	,_UpperCAmelCase				)

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

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

      # Get datasets
      a_				     =   (
          TFTokenClassificationDataset(
              token_classification_task=_UpperCAmelCase	,data_dir=data_args.data_dir	,tokenizer=_UpperCAmelCase	,labels=_UpperCAmelCase	,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=_UpperCAmelCase	,data_dir=data_args.data_dir	,tokenizer=_UpperCAmelCase	,labels=_UpperCAmelCase	,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__	,lowercase__				) -> Tuple[List[int], List[int]]:
            a_				     =   np.argmax(_UpperCAmelCase	,axis=2				)
            a_				     =   preds.shape
            a_				     =   [[] for _ in range(_UpperCAmelCase				)]
            a_				     =   [[] for _ in range(_UpperCAmelCase				)]

            for i in range(_UpperCAmelCase				):
                  for j in range(_UpperCAmelCase				):
                        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__				) -> Dict:
            a_				     =   align_predictions(p.predictions	,p.label_ids				)

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

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

      # 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(_UpperCAmelCase	,"w"				) as writer:
                  logger.info("***** Eval results *****"				)

                  for key, value in result.items():
                        logger.info("  %s = %s"	,_UpperCAmelCase	,_UpperCAmelCase				)
                        writer.write("%s = %s\n" % (key, value)				)

                  results.update(_UpperCAmelCase				)

    # Predict
      if training_args.do_predict:
            a_				     =   TFTokenClassificationDataset(
                token_classification_task=_UpperCAmelCase	,data_dir=data_args.data_dir	,tokenizer=_UpperCAmelCase	,labels=_UpperCAmelCase	,model_type=config.model_type	,max_seq_length=data_args.max_seq_length	,overwrite_cache=data_args.overwrite_cache	,mode=Split.test	,)

            a_				     =   trainer.predict(test_dataset.get_dataset()				)
            a_				     =   align_predictions(_UpperCAmelCase	,_UpperCAmelCase				)
            a_				     =   classification_report(_UpperCAmelCase	,_UpperCAmelCase				)

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

            a_				     =   os.path.join(training_args.output_dir	,"test_results.txt"				)

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

            # Save predictions
            a_				     =   os.path.join(training_args.output_dir	,"test_predictions.txt"				)

            with open(_UpperCAmelCase	,"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(_UpperCAmelCase				)

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

      return results


if __name__ == "__main__":
       main()











'''simple docstring'''





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						if not inplace:
												a_				     =   ma + ma
						else:
												ma += ma

						return ma



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   -1 * len(lowercase__				)
						a_				     =   list(range(len(tensor.shape[:zero_index]				)				)				)
						return tensor.permute(first_inds + [zero_index + i for i in inds]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						return t.reshape(t.shape[:-no_dims] + (-1,)				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__ = 1e-4				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   mask.expand(*value.shape				)
						return torch.sum(mask * value	,dim=lowercase__				) / (eps + torch.sum(lowercase__	,dim=lowercase__				))



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = 2.3125	,lowercase__ = 21.6875	,lowercase__ = 64				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   torch.linspace(lowercase__	,lowercase__	,no_bins - 1	,device=pts.device				)
						a_				     =   torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2	,dim=-1				)				)
						return torch.bucketize(lowercase__	,lowercase__				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						a_				     =   dicts[0]
						a_				     =   {}
						for k, v in first.items():
												a_				     =   [d[k] for d in dicts]
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_multimap(lowercase__	,lowercase__				)
												else:
																		a_				     =   fn(lowercase__				)

						return new_dict



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   v_bins.view(((1,) * len(x.shape				)) + (len(lowercase__				),)				)
						a_				     =   x[..., None] - reshaped_bins
						a_				     =   torch.argmin(torch.abs(lowercase__				)	,dim=-1				)
						return nn.functional.one_hot(lowercase__	,num_classes=len(lowercase__				)				).float()



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__ = 0	,lowercase__ = 0				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   []
						for i, s in enumerate(data.shape[:no_batch_dims]				):
												a_				     =   torch.arange(lowercase__				)
												a_				     =   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
												ranges.append(lowercase__				)

						a_				     =   [slice(lowercase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
						a_				     =   inds
						ranges.extend(lowercase__				)
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(lowercase__				)]


a_      = TypeVar('T')



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Dict[Any, Union[dict, list, tuple, Any]]:

						'''simple docstring'''


						a_				     =   {}
						for k, v in dic.items():
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_map(lowercase__	,lowercase__	,lowercase__				)
												else:
																		a_				     =   tree_map(lowercase__	,lowercase__	,lowercase__				)

						return new_dict



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Any:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				list:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				tuple:

						'''simple docstring'''


						...



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				List[Any]:

						'''simple docstring'''


						if isinstance(lowercase__	,lowercase__				):
												return dict_map(lowercase__	,lowercase__	,lowercase__				)
						elif isinstance(lowercase__	,lowercase__				):
												return [tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree]
						elif isinstance(lowercase__	,lowercase__				):
												return tuple(tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree				)
						elif isinstance(lowercase__	,lowercase__				):
												return fn(lowercase__				)
						else:
												print(type(lowercase__				)				)
												raise ValueError("Not supported"				)
a_      = partial(tree_map, leaf_type=torch.Tensor)



