

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






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

def      _lowerCAmelCase(	):
  # 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.

  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(__A   ,       __A			) -> 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(__A			) -> 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      _lowerCAmelCase(	__A			):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__       = logging.get_logger(__name__)

lowerCAmelCase__       = {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}






class 						__magic_name__    (       _snake_case	,						_snake_case    ):
 UpperCAmelCase							    =						"""convnextv2"""


 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: List[Any]=3			,						lowerCAmelCase__		: str=4			,						lowerCAmelCase__		: Dict=4			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: str="gelu"			,						lowerCAmelCase__		: Optional[int]=0.02			,						lowerCAmelCase__		: Dict=1e-1_2			,						lowerCAmelCase__		: str=0.0			,						lowerCAmelCase__		: str=2_2_4			,						lowerCAmelCase__		: int=None			,						lowerCAmelCase__		: List[Any]=None			,						**lowerCAmelCase__		: List[Any]			,						)    ->      List[Any]:
   super().__init__(**lowerCAmelCase__    )

   UpperCAmelCase					      =  num_channels
   UpperCAmelCase					      =  patch_size
   UpperCAmelCase					      =  num_stages
   UpperCAmelCase					      =  [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
   UpperCAmelCase					      =  [3, 3, 9, 3] if depths is None else depths
   UpperCAmelCase					      =  hidden_act
   UpperCAmelCase					      =  initializer_range
   UpperCAmelCase					      =  layer_norm_eps
   UpperCAmelCase					      =  drop_path_rate
   UpperCAmelCase					      =  image_size
   UpperCAmelCase					      =  ["stem"] + [f"stage{idx}" for idx in range(1			,						len(self.depths    ) + 1    )]
   UpperCAmelCase      ,	UpperCAmelCase					      =  get_aligned_output_features_output_indices(
       out_features=lowerCAmelCase__			,						out_indices=lowerCAmelCase__			,						stage_names=self.stage_names    )



