

'''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()


A_      =							logging.getLogger(__name__)





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





@dataclass
class 							_snake_case	:
   _A    :       str      				=   field(
       metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}  )
   _A    :       Optional[str]      				=   field(
       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}  )
   _A    :       int      				=   field(
       default=1_2_8		,					metadata={
           '''help''': (
               '''The maximum total input sequence length after tokenization. Sequences longer '''
               '''than this will be truncated, sequences shorter will be padded.'''
           )
       }		,					)
   _A    :       bool      				=   field(
       default=_a		,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )





def 				A_					(	):
  # 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.
  SCREAMING_SNAKE_CASE:Dict   =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
  SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:Optional[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."	)

  SCREAMING_SNAKE_CASE:List[str]   =   import_module("tasks"	)

  try:
    SCREAMING_SNAKE_CASE:int   =   getattr(snake_case ,    model_args.task_type	)
    SCREAMING_SNAKE_CASE: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" ,    snake_case	)

  # Prepare Token Classification task
  SCREAMING_SNAKE_CASE:Optional[int]   =   token_classification_task.get_labels(data_args.labels	)
  SCREAMING_SNAKE_CASE:Dict[int, str]   =   dict(enumerate(snake_case	)	)
  SCREAMING_SNAKE_CASE:str   =   len(snake_case	)

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

  SCREAMING_SNAKE_CASE:str   =   AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path ,    num_labels=snake_case ,    idalabel=snake_case ,    labelaid={label: i for i, label in enumerate(snake_case	)} ,    cache_dir=model_args.cache_dir ,    )
  SCREAMING_SNAKE_CASE: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():
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   TFAutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path ,    from_pt=bool(".bin" in model_args.model_name_or_path	) ,    config=snake_case ,    cache_dir=model_args.cache_dir ,    )

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

    for i in range(snake_case	):
      for j in range(snake_case	):
        if label_ids[i, j] != -100:
          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(snake_case	) -> Dict:
    SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:Union[str, Any]   =   align_predictions(p.predictions ,    p.label_ids	)

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

  # Initialize our Trainer
  SCREAMING_SNAKE_CASE:Any   =   TFTrainer(
      model=snake_case ,    args=snake_case ,    train_dataset=train_dataset.get_dataset() if train_dataset else None ,    eval_dataset=eval_dataset.get_dataset() if eval_dataset else None ,    compute_metrics=snake_case ,    )

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

  # Evaluation
  SCREAMING_SNAKE_CASE:Optional[Any]   =   {}
  if training_args.do_eval:
    logger.info("*** Evaluate ***"	)

    SCREAMING_SNAKE_CASE:Optional[Any]   =   trainer.evaluate()
    SCREAMING_SNAKE_CASE:Union[str, Any]   =   os.path.join(training_args.output_dir ,    "eval_results.txt"	)

    with open(snake_case ,    "w"	) as writer:
      logger.info("***** Eval results *****"	)

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

      results.update(snake_case	)

    # Predict
  if training_args.do_predict:
    SCREAMING_SNAKE_CASE:Optional[Any]   =   TFTokenClassificationDataset(
        token_classification_task=snake_case ,    data_dir=data_args.data_dir ,    tokenizer=snake_case ,    labels=snake_case ,    model_type=config.model_type ,    max_seq_length=data_args.max_seq_length ,    overwrite_cache=data_args.overwrite_cache ,    mode=Split.test ,    )

    SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:Optional[Any]   =   trainer.predict(test_dataset.get_dataset()	)
    SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:int   =   align_predictions(snake_case ,    snake_case	)
    SCREAMING_SNAKE_CASE:Optional[int]   =   classification_report(snake_case ,    snake_case	)

    logger.info("\n%s" ,    snake_case	)

    SCREAMING_SNAKE_CASE:Any   =   os.path.join(training_args.output_dir ,    "test_results.txt"	)

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

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

    with open(snake_case ,    "w"	) as writer:
      with open(os.path.join(data_args.data_dir ,    "test.txt"	) ,    "r"	) as f:
        SCREAMING_SNAKE_CASE:int   =   0

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

            if not preds_list[example_id]:
              example_id += 1
          elif preds_list[example_id]:
            SCREAMING_SNAKE_CASE:int   =   line.split()[0] + " " + preds_list[example_id].pop(0	) + "\n"

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

  return results


if __name__ == "__main__":
    main()









'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A_      =							logging.get_logger(__name__)

A_      =							["UtteranceLevel"]





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(snake_case ,    map_location="cpu"	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

  if hf_congfig.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Featurizer"]["weights"]

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(snake_case	)


if __name__ == "__main__":
    A_      =							argparse.ArgumentParser()
    parser.add_argument(
        "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
    )
    parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
    parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
    A_      =							parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






