
'''simple docstring'''


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


snake_case_ :  str     =					logging.getLogger(__name__)


@dataclass
class 						lowercase__	:






   '''simple docstring'''







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




@dataclass
class 						lowercase__	:






   '''simple docstring'''







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




def  __snake_case     (    ):
   UpperCamelCase       =  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.
      UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
   else:
      UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  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.''')

   UpperCamelCase       =  import_module('''tasks''')
   try:
      UpperCamelCase       =  getattr(A_,      model_args.task_type)
      UpperCamelCase       =  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''',      A_)

   # Set seed
   set_seed(training_args.seed)

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

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

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

   # Get datasets
   UpperCamelCase       =  (
       TokenClassificationDataset(
           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
   )
   UpperCamelCase       =  (
       TokenClassificationDataset(
           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					:       Any,      _UpperCAmelCase					:       int) -> Tuple[List[int], List[int]]:
      UpperCamelCase       =  np.argmax(A_,      axis=2)

      UpperCamelCase     , UpperCamelCase       =  preds.shape

      UpperCamelCase       =  [[] for _ in range(A_)]
      UpperCamelCase       =  [[] for _ in range(A_)]

      for i in range(A_):
         for j in range(A_):
            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(_UpperCAmelCase					:       Optional[int]) -> Dict:
      UpperCamelCase     , UpperCamelCase       =  align_predictions(p.predictions,      p.label_ids)
      return {
          "accuracy_score": accuracy_score(A_,      A_),
          "precision": precision_score(A_,      A_),
          "recall": recall_score(A_,      A_),
          "f1": fa_score(A_,      A_),
      }

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

   # Initialize our Trainer
   UpperCamelCase       =  Trainer(
       model=A_,      args=A_,      train_dataset=A_,      eval_dataset=A_,      compute_metrics=A_,      data_collator=A_,      )

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

      UpperCamelCase       =  trainer.evaluate()

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

      UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  trainer.predict(A_)
      UpperCamelCase     , UpperCamelCase       =  align_predictions(A_,      A_)

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

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

   return results



def  __snake_case     (    _UpperCAmelCase					:       List[str]):
   main()


if __name__ == "__main__":
   main()







'''simple docstring'''


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
   from ..models.auto import AutoTokenizer


class 						lowercase__	:






   '''simple docstring'''







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      raise NotImplementedError()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      raise NotImplementedError()




class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  tokenizer
      UpperCamelCase       =  skip_prompt
      UpperCamelCase       =  decode_kwargs

      # variables used in the streaming process
      UpperCamelCase       =  []
      UpperCamelCase       =  0
      UpperCamelCase       =  True







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if len(value.shape   ) > 1 and value.shape[0] > 1:
         raise ValueError('''TextStreamer only supports batch size 1'''   )
      elif len(value.shape   ) > 1:
         UpperCamelCase       =  value[0]

      if self.skip_prompt and self.next_tokens_are_prompt:
         UpperCamelCase       =  False
         return

      # Add the new token to the cache and decodes the entire thing.
      self.token_cache.extend(value.tolist()   )
      UpperCamelCase       =  self.tokenizer.decode(self.token_cache ,   **self.decode_kwargs   )

      # After the symbol for a new line, we flush the cache.
      if text.endswith('''\n'''   ):
         UpperCamelCase       =  text[self.print_len :]
         UpperCamelCase       =  []
         UpperCamelCase       =  0
      # If the last token is a CJK character, we print the characters.
      elif len(lowerCamelCase__   ) > 0 and self._is_chinese_char(ord(text[-1]   )   ):
         UpperCamelCase       =  text[self.print_len :]
         self.print_len += len(lowerCamelCase__   )
      # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
      # which may change with the subsequent token -- there are probably smarter ways to do this!)
      else:
         UpperCamelCase       =  text[self.print_len : text.rfind(''' '''   ) + 1]
         self.print_len += len(lowerCamelCase__   )

      self.on_finalized_text(lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if len(self.token_cache   ) > 0:
         UpperCamelCase       =  self.tokenizer.decode(self.token_cache ,   **self.decode_kwargs   )
         UpperCamelCase       =  text[self.print_len :]
         UpperCamelCase       =  []
         UpperCamelCase       =  0
      else:
         UpperCamelCase       =  ''''''

      UpperCamelCase       =  True
      self.on_finalized_text(lowerCamelCase__ ,   stream_end=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      print(lowerCamelCase__ ,   flush=lowerCamelCase__ ,   end='''''' if not stream_end else None   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      if (
          (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
          or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
          or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
          or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
          or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
          or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
          or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
          or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
      ):  #
         return True

      return False




class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   **lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__ ,   **lowerCamelCase__   )
      UpperCamelCase       =  Queue()
      UpperCamelCase       =  None
      UpperCamelCase       =  timeout







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      self.text_queue.put(lowerCamelCase__ ,   timeout=self.timeout   )
      if stream_end:
         self.text_queue.put(self.stop_signal ,   timeout=self.timeout   )







   def __iter__(			self   ):

      '''simple docstring'''







      return self







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.text_queue.get(timeout=self.timeout   )
      if value == self.stop_signal:
         raise StopIteration()
      else:
         return value





