


'''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


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






@dataclass
class    __snake_case       :







    """simple docstring"""



    lowercase    =					field(
        metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,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=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
    lowercase    =					field(default=_SCREAMING_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.
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,)







@dataclass
class    __snake_case       :







    """simple docstring"""



    lowercase    =					field(
        metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}	,)
    lowercase    =					field(
        default=1_28	,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=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Overwrite the cached training and evaluation sets'})







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





  lowerCAmelCase_			:      List[str]			=   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.
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Optional[int]			=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
  else:
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Union[str, Any]			=   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."""					)

  lowerCAmelCase_			:      Dict			=   import_module("""tasks"""					)
  try:
    lowerCAmelCase_			:      List[Any]			=   getattr(A__							,    model_args.task_type					)
    lowerCAmelCase_			:      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 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
  lowerCAmelCase_			:      int			=   token_classification_task.get_labels(data_args.labels					)
  lowerCAmelCase_			:      Dict[int, str]			=   dict(enumerate(A__					)					)
  lowerCAmelCase_			:      int			=   len(A__					)

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

  lowerCAmelCase_			:      Dict			=   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							,    )
  lowerCAmelCase_			:      Union[str, Any]			=   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							,    )
  lowerCAmelCase_			:      Dict			=   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
  lowerCAmelCase_			:      Any			=   (
      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
  )
  lowerCAmelCase_			:      int			=   (
      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(A__					:   np.ndarray							,    A__					:   np.ndarray					) -> Tuple[List[int], List[int]]:
    lowerCAmelCase_			:      List[Any]			=   np.argmax(A__							,    axis=2					)

    lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   preds.shape

    lowerCAmelCase_			:      Dict			=   [[] for _ in range(A__					)]
    lowerCAmelCase_			:      int			=   [[] 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(A__					:   EvalPrediction					) -> Dict:
    lowerCAmelCase_,							lowerCAmelCase_			:      int			=   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
  lowerCAmelCase_			:      str			=   DataCollatorWithPadding(A__							,    pad_to_multiple_of=8					) if training_args.fpaa else None

  # Initialize our Trainer
  lowerCAmelCase_			:      str			=   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
  lowerCAmelCase_			:      Any			=   {}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""					)

    lowerCAmelCase_			:      List[Any]			=   trainer.evaluate()

    lowerCAmelCase_			:      Any			=   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:
    lowerCAmelCase_			:      Tuple			=   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							,    )

    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   trainer.predict(A__					)
    lowerCAmelCase_,							lowerCAmelCase_			:      List[str]			=   align_predictions(A__							,    A__					)

    lowerCAmelCase_			:      Any			=   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
    lowerCAmelCase_			:      Optional[int]			=   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 				UpperCamelCase_			(    A__					:   Tuple					):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()







'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'philschmid/bart-large-cnn-samsum'
    lowercase    =					(
        'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
        'and returns a summary of the text.'
    )
    lowercase    =					'summarizer'
    lowercase    =					AutoTokenizer
    lowercase    =					AutoModelForSeqaSeqLM

    lowercase    =					['text']
    lowercase    =					['text']

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Dict		)   ->  Any:
      return self.pre_processor(lowerCamelCase    ,						return_tensors="""pt"""    ,						truncation=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Tuple		)   ->  List[str]:
      return self.model.generate(**lowerCamelCase		)[0]





    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  Dict		)   ->  List[Any]:
      return self.pre_processor.decode(lowerCamelCase    ,						skip_special_tokens=lowerCamelCase    ,						clean_up_tokenization_spaces=lowerCamelCase		)



