




"""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


SCREAMING_SNAKE_CASE    =	logging.getLogger(__name__)




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






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







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 # 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.

 A__	=    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.
  A__,				A__,				A__	=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
 else:
  A__,				A__,				A__	=    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."						)

 A__	=    import_module("tasks"						)
 try:
  A__	=    getattr(lowercase_      ,			model_args.task_type						)
  A__	=    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
 A__	=    token_classification_task.get_labels(data_args.labels						)
 A__	=    dict(enumerate(lowercase_						)						)
 A__	=    len(lowercase_						)

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

 A__	=    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      ,			)
 A__	=    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      ,			)
 A__	=    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
 A__	=    (
     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
 )
 A__	=    (
     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]]:
  A__	=    np.argmax(lowercase_      ,			axis=2						)

  A__,				A__	=    preds.shape

  A__	=    [[] for _ in range(lowercase_						)]
  A__	=    [[] 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:
  A__,				A__	=    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
 A__	=    DataCollatorWithPadding(lowercase_      ,			pad_to_multiple_of=8						) if training_args.fpaa else None

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

  A__	=    trainer.evaluate()

  A__	=    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:
  A__	=    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      ,			)

  A__,				A__,				A__	=    trainer.predict(lowercase_						)
  A__,				A__	=    align_predictions(lowercase_      ,			lowercase_						)

  A__	=    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
  A__	=    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   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Optional[int]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()











"""simple docstring"""



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE    =	{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
  raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()						)}."""						)

 if tokenizer_name is None:
  A__	=    TOKENIZER_CLASSES
 else:
  A__	=    {tokenizer_name: getattr(lowercase_      ,			tokenizer_name + "Fast"						)}

 logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""						)

 for tokenizer_name in tokenizer_names:
  A__	=    TOKENIZER_CLASSES[tokenizer_name]

  A__	=    True
  if checkpoint_name is None:
   A__	=    list(tokenizer_class.max_model_input_sizes.keys()						)
  else:
   A__	=    [checkpoint_name]

  logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""						)

  for checkpoint in checkpoint_names:
   logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""						)

   # Load tokenizer
   A__	=    tokenizer_class.from_pretrained(lowercase_      ,			force_download=lowercase_						)

   # Save fast tokenizer
   logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""						)

   # For organization names we create sub-directories
   if "/" in checkpoint:
    A__,				A__	=    checkpoint.split("/"						)
    A__	=    os.path.join(lowercase_      ,			lowercase_						)
   elif add_prefix:
    A__	=    checkpoint
    A__	=    dump_path
   else:
    A__	=    None
    A__	=    dump_path

   logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()						)[0]:
    A__	=    list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
    A__	=    file_path.split(lowercase_						)[-1][0]
    if next_char == "/":
     A__	=    os.path.join(lowercase_      ,			lowercase_						)
     A__	=    None

    logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   A__	=    tokenizer.save_pretrained(
       lowercase_      ,			legacy_format=lowercase_      ,			filename_prefix=lowercase_						)
   logger.info(f"""=> File names {file_names}"""						)

   for file_name in file_names:
    if not file_name.endswith("tokenizer.json"						):
     os.remove(lowercase_						)
     logger.info(f"""=> removing {file_name}"""						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
      )
      parser.add_argument(
          "--tokenizer_name",
          default=None,
          type=str,
          help=(
              f'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
              "download and convert all the checkpoints from AWS."
          ),
      )
      parser.add_argument(
          "--checkpoint_name",
          default=None,
          type=str,
          help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
      )
      parser.add_argument(
          "--force_download",
          action="store_true",
          help="Re-download checkpoints.",
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





