





'''simple docstring'''





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_lowerCamelCase			       =      logging.getLogger(__name__)
_lowerCamelCase			       =      list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_lowerCamelCase			       =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={
              """help""": (
                  """The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."""
              )
          } ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__lowerCamelCase  )} ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={
              """help""": (
                  """Override some existing default config settings when a model is trained from scratch. Example: """
                  """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
              )
          } ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} ,)
      UpperCAmelCase__						=      field(
          default="""main""" ,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={
              """help""": (
                  """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                  """with private models)."""
              )
          } ,)


      def 				__snake_case			(		self):



             """simple docstring"""





             if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
                    raise ValueError(
                        '''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path''')







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
      UpperCAmelCase__						=      field(default=__lowerCamelCase ,metadata={"""help""": """The input training data file (a text file)."""}  )
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""} ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""} ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""} ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )
      UpperCAmelCase__						=      field(
          default=5 ,metadata={
              """help""": """The percentage of the train set used as validation set in case there\'s no validation split"""
          } ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated. Default to the max input length of the model."""
              )
          } ,)
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={"""help""": """The number of processes to use for the preprocessing."""} ,)
      UpperCAmelCase__						=      field(
          default=0.15 ,metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}  )
      UpperCAmelCase__						=      field(
          default=__lowerCamelCase ,metadata={
              """help""": (
                  """Whether to pad all samples to `max_seq_length`. """
                  """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
              )
          } ,)


      def 				__snake_case			(		self):



             """simple docstring"""





             if self.train_file is not None:
                    _lowerCamelCase							:     str	=		self.train_file.split('''.''')[-1]
                    assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
             if self.validation_file is not None:
                    _lowerCamelCase							:     Union[str, Any]	=		self.validation_file.split('''.''')[-1]
                    assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):


       with open(lowerCAmelCase_ ,    '''r''' ,    encoding='''utf-8'''      ) as f:
              _lowerCamelCase							:     Tuple	=		[json.loads(lowerCAmelCase_      ) for line in f.read().splitlines() if (len(lowerCAmelCase_      ) > 0 and not line.isspace())]
       assert len(lowerCAmelCase_      ) == len(lowerCAmelCase_      )

       _lowerCamelCase							:     Any	=		{c: dataset[c] for c in dataset.column_names}
       _lowerCamelCase							:     List[Any]	=		refs
       return Dataset.from_dict(lowerCAmelCase_      )




def    __UpperCAmelCase(      ):
       _lowerCamelCase							:     int	=		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							:     Optional[Any]	=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
       else:
              _lowerCamelCase							:     str	=		parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       _lowerCamelCase							:     List[str]	=		None
       if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
              _lowerCamelCase							:     Dict	=		get_last_checkpoint(training_args.output_dir      )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         '''Use --overwrite_output_dir to overcome.'''      )
              elif last_checkpoint is not None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''      )

    # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,    datefmt='''%m/%d/%Y %H:%M:%S''' ,    handlers=[logging.StreamHandler(sys.stdout      )] ,    )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank      ) else logging.WARN      )

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {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''' ,    lowerCAmelCase_      )

       # Set seed before initializing model.
       set_seed(training_args.seed      )

       # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
       # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
       # (the dataset will be downloaded automatically from the datasets Hub).
       #
       # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
       # 'text' is found. You can easily tweak this behavior (see below).
       #
       # In distributed training, the load_dataset function guarantee that only one local process can concurrently
       # download the dataset.
       if data_args.dataset_name is not None:
              # Downloading and loading a dataset from the hub.
              _lowerCamelCase							:     Optional[int]	=		load_dataset(data_args.dataset_name ,    data_args.dataset_config_name      )
              if "validation" not in datasets.keys():
                     _lowerCamelCase							:     Any	=		load_dataset(
                         data_args.dataset_name ,    data_args.dataset_config_name ,    split=F"""train[:{data_args.validation_split_percentage}%]""" ,    )
                     _lowerCamelCase							:     Optional[int]	=		load_dataset(
                         data_args.dataset_name ,    data_args.dataset_config_name ,    split=F"""train[{data_args.validation_split_percentage}%:]""" ,    )
       else:
              _lowerCamelCase							:     Dict	=		{}
              if data_args.train_file is not None:
                     _lowerCamelCase							:     str	=		data_args.train_file
              if data_args.validation_file is not None:
                     _lowerCamelCase							:     Any	=		data_args.validation_file
              _lowerCamelCase							:     Any	=		data_args.train_file.split('''.'''      )[-1]
              if extension == "txt":
                     _lowerCamelCase							:     List[str]	=		"text"
              _lowerCamelCase							:     Dict	=		load_dataset(lowerCAmelCase_ ,    data_files=lowerCAmelCase_      )
       # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
       # https://huggingface.co/docs/datasets/loading_datasets.html.

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       _lowerCamelCase							:     Optional[Any]	=		{
           "cache_dir": model_args.cache_dir,
           "revision": model_args.model_revision,
           "use_auth_token": True if model_args.use_auth_token else None,
       }
       if model_args.config_name:
              _lowerCamelCase							:     Optional[Any]	=		AutoConfig.from_pretrained(model_args.config_name ,    **lowerCAmelCase_      )
       elif model_args.model_name_or_path:
              _lowerCamelCase							:     str	=		AutoConfig.from_pretrained(model_args.model_name_or_path ,    **lowerCAmelCase_      )
       else:
              _lowerCamelCase							:     Optional[int]	=		CONFIG_MAPPING[model_args.model_type]()
              logger.warning('''You are instantiating a new config instance from scratch.'''      )
              if model_args.config_overrides is not None:
                     logger.info(F"""Overriding config: {model_args.config_overrides}"""      )
                     config.update_from_string(model_args.config_overrides      )
                     logger.info(F"""New config: {config}"""      )

       _lowerCamelCase							:     List[str]	=		{
           "cache_dir": model_args.cache_dir,
           "use_fast": model_args.use_fast_tokenizer,
           "revision": model_args.model_revision,
           "use_auth_token": True if model_args.use_auth_token else None,
       }
       if model_args.tokenizer_name:
              _lowerCamelCase							:     str	=		AutoTokenizer.from_pretrained(model_args.tokenizer_name ,    **lowerCAmelCase_      )
       elif model_args.model_name_or_path:
              _lowerCamelCase							:     Dict	=		AutoTokenizer.from_pretrained(model_args.model_name_or_path ,    **lowerCAmelCase_      )
       else:
              raise ValueError(
                  '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
                  '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''      )

       if model_args.model_name_or_path:
              _lowerCamelCase							:     Union[str, Any]	=		AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path      ) ,    config=lowerCAmelCase_ ,    cache_dir=model_args.cache_dir ,    revision=model_args.model_revision ,    use_auth_token=True if model_args.use_auth_token else None ,    )
       else:
              logger.info('''Training new model from scratch'''      )
              _lowerCamelCase							:     Dict	=		AutoModelForMaskedLM.from_config(lowerCAmelCase_      )

       model.resize_token_embeddings(len(lowerCAmelCase_      )      )

       # Preprocessing the datasets.
       # First we tokenize all the texts.
       if training_args.do_train:
              _lowerCamelCase							:     Optional[Any]	=		datasets["train"].column_names
       else:
              _lowerCamelCase							:     Dict	=		datasets["validation"].column_names
       _lowerCamelCase							:     Union[str, Any]	=		"text" if "text" in column_names else column_names[0]

       _lowerCamelCase							:     Optional[Any]	=		"max_length" if data_args.pad_to_max_length else False

       def tokenize_function(lowercase_      ):
              # Remove empty lines
              _lowerCamelCase							:     str	=		[line for line in examples["text"] if len(lowerCAmelCase_      ) > 0 and not line.isspace()]
              return tokenizer(examples['''text'''] ,    padding=lowerCAmelCase_ ,    truncation=lowerCAmelCase_ ,    max_length=data_args.max_seq_length      )

       _lowerCamelCase							:     str	=		datasets.map(
           lowerCAmelCase_ ,    batched=lowerCAmelCase_ ,    num_proc=data_args.preprocessing_num_workers ,    remove_columns=[text_column_name] ,    load_from_cache_file=not data_args.overwrite_cache ,    )

       # Add the chinese references if provided
       if data_args.train_ref_file is not None:
              _lowerCamelCase							:     List[Any]	=		add_chinese_references(tokenized_datasets['''train'''] ,    data_args.train_ref_file      )
       if data_args.validation_ref_file is not None:
              _lowerCamelCase							:     List[str]	=		add_chinese_references(
                  tokenized_datasets['''validation'''] ,    data_args.validation_ref_file      )
       # If we have ref files, need to avoid it removed by trainer
       _lowerCamelCase							:     Optional[Any]	=		data_args.train_ref_file or data_args.validation_ref_file
       if has_ref:
              _lowerCamelCase							:     Union[str, Any]	=		False

       # Data collator
       # This one will take care of randomly masking the tokens.
       _lowerCamelCase							:     Optional[Any]	=		DataCollatorForWholeWordMask(tokenizer=lowerCAmelCase_ ,    mlm_probability=data_args.mlm_probability      )

       # Initialize our Trainer
       _lowerCamelCase							:     int	=		Trainer(
           model=lowerCAmelCase_ ,    args=lowerCAmelCase_ ,    train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None ,    eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None ,    tokenizer=lowerCAmelCase_ ,    data_collator=lowerCAmelCase_ ,    )

       # Training
       if training_args.do_train:
              if last_checkpoint is not None:
                     _lowerCamelCase							:     Dict	=		last_checkpoint
              elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      ):
                     _lowerCamelCase							:     Dict	=		model_args.model_name_or_path
              else:
                     _lowerCamelCase							:     int	=		None
              _lowerCamelCase							:     Optional[Any]	=		trainer.train(resume_from_checkpoint=lowerCAmelCase_      )
              trainer.save_model()  # Saves the tokenizer too for easy upload

              _lowerCamelCase							:     Tuple	=		os.path.join(training_args.output_dir ,    '''train_results.txt'''      )
              if trainer.is_world_process_zero():
                     with open(lowerCAmelCase_ ,    '''w'''      ) as writer:
                            logger.info('''***** Train results *****'''      )
                            for key, value in sorted(train_result.metrics.items()      ):
                                   logger.info(F"""  {key} = {value}"""      )
                                   writer.write(F"""{key} = {value}\n"""      )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir ,    '''trainer_state.json'''      )      )

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

              _lowerCamelCase							:     Tuple	=		trainer.evaluate()

              _lowerCamelCase							:     str	=		math.exp(eval_output['''eval_loss''']      )
              _lowerCamelCase							:     Tuple	=		perplexity

              _lowerCamelCase							:     int	=		os.path.join(training_args.output_dir ,    '''eval_results_mlm_wwm.txt'''      )
              if trainer.is_world_process_zero():
                     with open(lowerCAmelCase_ ,    '''w'''      ) as writer:
                            logger.info('''***** Eval results *****'''      )
                            for key, value in sorted(results.items()      ):
                                   logger.info(F"""  {key} = {value}"""      )
                                   writer.write(F"""{key} = {value}\n"""      )

       return results




def    __UpperCAmelCase(      lowercase_      ):
       main()


if __name__ == "__main__":
       main()








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


_lowerCamelCase			       =      logging.getLogger(__name__)







@dataclass
class 			__A       :




      """simple docstring"""







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







@dataclass
class 			__A       :




      """simple docstring"""







      UpperCAmelCase__						=      field(
          metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}  )
      UpperCAmelCase__						=      field(
          default=lowerCamelCase__ ,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=lowerCamelCase__ ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )




def    __UpperCAmelCase(      ):
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       _lowerCamelCase							:     Dict	=		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							:     Tuple	=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
       else:
              _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase							:     List[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							:     Optional[Any]	=		import_module('''tasks'''      )
       try:
              _lowerCamelCase							:     Dict	=		getattr(lowercase_ ,    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''' ,    lowercase_      )

       # Set seed
       set_seed(training_args.seed      )

       # Prepare CONLL-2003 task
       _lowerCamelCase							:     List[str]	=		token_classification_task.get_labels(data_args.labels      )
       _lowerCamelCase							:     Dict[int, str]	=		dict(enumerate(lowercase_      )      )
       _lowerCamelCase							:     Any	=		len(lowercase_      )

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

       _lowerCamelCase							:     List[Any]	=		AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path ,    num_labels=lowercase_ ,    idalabel=lowercase_ ,    labelaid={label: i for i, label in enumerate(lowercase_      )} ,    cache_dir=model_args.cache_dir ,    )
       _lowerCamelCase							:     List[str]	=		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							:     Tuple	=		AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path      ) ,    config=lowercase_ ,    cache_dir=model_args.cache_dir ,    )

       # Get datasets
       _lowerCamelCase							:     Optional[int]	=		(
           TokenClassificationDataset(
               token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    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							:     Dict	=		(
           TokenClassificationDataset(
               token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    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]]:
              _lowerCamelCase							:     List[Any]	=		np.argmax(lowercase_ ,    axis=2      )

              _lowerCamelCase,  _lowerCamelCase							:     Optional[Any]	=		preds.shape

              _lowerCamelCase							:     Optional[Any]	=		[[] for _ in range(lowercase_      )]
              _lowerCamelCase							:     str	=		[[] for _ in range(lowercase_      )]

              for i in range(lowercase_      ):
                     for j in range(lowercase_      ):
                            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(lowercase_      ) -> Dict:
              _lowerCamelCase,  _lowerCamelCase							:     Union[str, Any]	=		align_predictions(p.predictions ,    p.label_ids      )
              return {
                  "accuracy_score": accuracy_score(lowercase_ ,    lowercase_      ),
                  "precision": precision_score(lowercase_ ,    lowercase_      ),
                  "recall": recall_score(lowercase_ ,    lowercase_      ),
                  "f1": fa_score(lowercase_ ,    lowercase_      ),
              }

       # Data collator
       _lowerCamelCase							:     Optional[int]	=		DataCollatorWithPadding(lowercase_ ,    pad_to_multiple_of=8      ) if training_args.fpaa else None

       # Initialize our Trainer
       _lowerCamelCase							:     List[Any]	=		Trainer(
           model=lowercase_ ,    args=lowercase_ ,    train_dataset=lowercase_ ,    eval_dataset=lowercase_ ,    compute_metrics=lowercase_ ,    data_collator=lowercase_ ,    )

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

              _lowerCamelCase							:     str	=		trainer.evaluate()

              _lowerCamelCase							:     Any	=		os.path.join(training_args.output_dir ,    '''eval_results.txt'''      )
              if trainer.is_world_process_zero():
                     with open(lowercase_ ,    '''w'''      ) as writer:
                            logger.info('''***** Eval results *****'''      )
                            for key, value in result.items():
                                   logger.info('''  %s = %s''' ,    lowercase_ ,    lowercase_      )
                                   writer.write('''%s = %s\n''' % (key, value)      )

                     results.update(lowercase_      )

    # Predict
       if training_args.do_predict:
              _lowerCamelCase							:     Union[str, Any]	=		TokenClassificationDataset(
                  token_classification_task=lowercase_ ,    data_dir=data_args.data_dir ,    tokenizer=lowercase_ ,    labels=lowercase_ ,    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							:     Dict	=		trainer.predict(lowercase_      )
              _lowerCamelCase,  _lowerCamelCase							:     Tuple	=		align_predictions(lowercase_ ,    lowercase_      )

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

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

       return results




def    __UpperCAmelCase(      lowercase_      ):
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
       main()

