



"""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()


_UpperCamelCase:					Optional[int]     				=	logging.getLogger(__name__)


@dataclass
class 	a__		:
   _lowerCamelCase       				=					field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  )
   _lowerCamelCase       				=					field(
       default=__snake_case,	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=__snake_case,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}  )
   _lowerCamelCase       				=					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.
   _lowerCamelCase       				=					field(
       default=__snake_case,	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=__snake_case,	metadata={'help': 'Overwrite the cached training and evaluation sets'}  )




def 	lowercase__  (      )  ->      Optional[Any]:






      '''simple docstring'''


      lowercase	:   List[Any]											= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
      lowercase	:   Tuple											= 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	:   int											= import_module('tasks'					)

      try:
            lowercase	:   Optional[Any]											= getattr(_a						,   model_args.task_type					)
            lowercase	:   TokenClassificationTask											= 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'						,   _a					)

      # Prepare Token Classification task
      lowercase	:   Union[str, Any]											= token_classification_task.get_labels(data_args.labels					)
      lowercase	:   Dict[int, str]											= dict(enumerate(_a					)					)
      lowercase	:   Tuple											= len(_a					)

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

      lowercase	:   List[Any]											= AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path						,   num_labels=_a						,   idalabel=_a						,   labelaid={label: i for i, label in enumerate(_a					)}						,   cache_dir=model_args.cache_dir						,   )
      lowercase	:   int											= 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():
            lowercase	:   Dict											= TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path						,   from_pt=bool('.bin' in model_args.model_name_or_path					)						,   config=_a						,   cache_dir=model_args.cache_dir						,   )

      # Get datasets
      lowercase	:   Dict											= (
          TFTokenClassificationDataset(
              token_classification_task=_a						,   data_dir=data_args.data_dir						,   tokenizer=_a						,   labels=_a						,   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	:   Tuple											= (
          TFTokenClassificationDataset(
              token_classification_task=_a						,   data_dir=data_args.data_dir						,   tokenizer=_a						,   labels=_a						,   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(_UpperCAmelCase						,   _UpperCAmelCase					) -> Tuple[List[int], List[int]]:
            lowercase	:   str											= np.argmax(_a						,   axis=2					)
            lowercase	:   Tuple											= preds.shape
            lowercase	:   Union[str, Any]											= [[] for _ in range(_a					)]
            lowercase	:   Union[str, Any]											= [[] for _ in range(_a					)]

            for i in range(_a					):
                  for j in range(_a					):
                        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(_UpperCAmelCase					) -> Dict:
            lowercase	:   Optional[int]											= align_predictions(p.predictions						,   p.label_ids					)

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

      # Initialize our Trainer
      lowercase	:   Dict											= TFTrainer(
          model=_a						,   args=_a						,   train_dataset=train_dataset.get_dataset() if train_dataset else None						,   eval_dataset=eval_dataset.get_dataset() if eval_dataset else None						,   compute_metrics=_a						,   )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir					)

      # Evaluation
      lowercase	:   Union[str, Any]											= {}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'					)

            lowercase	:   Dict											= trainer.evaluate()
            lowercase	:   Dict											= os.path.join(training_args.output_dir						,   'eval_results.txt'					)

            with open(_a						,   'w'					) as writer:
                  logger.info('***** Eval results *****'					)

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

                  results.update(_a					)

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

            lowercase	:   str											= trainer.predict(test_dataset.get_dataset()					)
            lowercase	:   List[str]											= align_predictions(_a						,   _a					)
            lowercase	:   int											= classification_report(_a						,   _a					)

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

            lowercase	:   List[Any]											= os.path.join(training_args.output_dir						,   'test_results.txt'					)

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

            # Save predictions
            lowercase	:   Optional[Any]											= os.path.join(training_args.output_dir						,   'test_predictions.txt'					)

            with open(_a						,   'w'					) as writer:
                  with open(os.path.join(data_args.data_dir						,   'test.txt'					)						,   'r'					) as f:
                        lowercase	:   Dict											= 0

                        for line in f:
                              if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
                                    writer.write(_a					)

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    lowercase	:   Union[str, Any]											= line.split()[0] + """ """ + preds_list[example_id].pop(0					) + """\n"""

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

      return results


if __name__ == "__main__":
     main()





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {}







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						"fsmt_decoder"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Tuple=0  ,lowerCamelCase_:     List[str]=0							)	->      Any:
       super().__init__()
       UpperCAmelCase_     :       Optional[Any]  		=					vocab_size
       UpperCAmelCase_     :       Union[str, Any]  		=					bos_token_id







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"fsmt"
   A__      : int       =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self:     int  ,lowerCamelCase_:     Optional[Any]=["en", "de"]  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     List[str]="relu"  ,lowerCamelCase_:     Any=1024  ,lowerCamelCase_:     Optional[int]=200  ,lowerCamelCase_:     str=1024  ,lowerCamelCase_:     Any=4096  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     Tuple=16  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     str=4096  ,lowerCamelCase_:     Union[str, Any]=12  ,lowerCamelCase_:     Any=16  ,lowerCamelCase_:     List[str]=0.0  ,lowerCamelCase_:     str=0.0  ,lowerCamelCase_:     Optional[int]=0.1  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     Tuple=0.0_2  ,lowerCamelCase_:     str=2  ,lowerCamelCase_:     List[str]=True  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     Any=False  ,lowerCamelCase_:     Any=5  ,lowerCamelCase_:     Optional[int]=1.0  ,lowerCamelCase_:     List[str]=False  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     List[str]=1  ,lowerCamelCase_:     int=0  ,lowerCamelCase_:     Optional[Any]=2  ,lowerCamelCase_:     Any=2  ,**lowerCamelCase_:     Optional[Any]  ,)	->      int:
       UpperCAmelCase_     :       Dict  		=					langs
       UpperCAmelCase_     :       Tuple  		=					src_vocab_size
       UpperCAmelCase_     :       Optional[int]  		=					tgt_vocab_size
       UpperCAmelCase_     :       Optional[Any]  		=					d_model  # encoder_embed_dim and decoder_embed_dim

       UpperCAmelCase_     :       List[str]  		=					encoder_ffn_dim
       UpperCAmelCase_     :       List[Any]  		=					encoder_layers
       UpperCAmelCase_     :       List[Any]  		=					encoder_attention_heads
       UpperCAmelCase_     :       Union[str, Any]  		=					encoder_layerdrop
       UpperCAmelCase_     :       List[Any]  		=					decoder_layerdrop
       UpperCAmelCase_     :       str  		=					decoder_ffn_dim
       UpperCAmelCase_     :       Optional[int]  		=					decoder_layers
       UpperCAmelCase_     :       int  		=					decoder_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					max_position_embeddings
       UpperCAmelCase_     :       Dict  		=					init_std  # Normal(0, this parameter)
       UpperCAmelCase_     :       Dict  		=					activation_function

       UpperCAmelCase_     :       List[str]  		=					DecoderConfig(vocab_size=lowerCamelCase_  ,bos_token_id=lowerCamelCase_							)
       if "decoder" in common_kwargs:
           del common_kwargs["decoder"]

       UpperCAmelCase_     :       Any  		=					scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       UpperCAmelCase_     :       int  		=					attention_dropout
       UpperCAmelCase_     :       int  		=					activation_dropout
       UpperCAmelCase_     :       Any  		=					dropout

       UpperCAmelCase_     :       Any  		=					use_cache
       super().__init__(
           pad_token_id=lowerCamelCase_  ,bos_token_id=lowerCamelCase_  ,eos_token_id=lowerCamelCase_  ,decoder_start_token_id=lowerCamelCase_  ,is_encoder_decoder=lowerCamelCase_  ,tie_word_embeddings=lowerCamelCase_  ,forced_eos_token_id=lowerCamelCase_  ,max_length=lowerCamelCase_  ,num_beams=lowerCamelCase_  ,length_penalty=lowerCamelCase_  ,early_stopping=lowerCamelCase_  ,**lowerCamelCase_  ,)






   def       A__    (    self:     List[str]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       str  		=					copy.deepcopy(self.__dict__							)
       UpperCAmelCase_     :       int  		=					self.decoder.to_dict()
       UpperCAmelCase_     :       Dict  		=					self.__class__.model_type
       return output
