






'''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	: str      =				logging.getLogger(__name__)
@dataclass
class 					a					:



      """simple docstring"""


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


@dataclass
class 					a					:



      """simple docstring"""


      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=128							, 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=lowercase__							, 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							:			List[str]     =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
  __UpperCAmelCase							:			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."""	)

  __UpperCAmelCase							:			Tuple     =						import_module("""tasks"""	)

  try:
    __UpperCAmelCase							:			Optional[int]     =						getattr(__lowerCamelCase						,	model_args.task_type	)
    __UpperCAmelCase							:			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"""						,	__lowerCamelCase	)

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

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

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

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

    for i in range(__lowerCamelCase	):
      for j in range(__lowerCamelCase	):
        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(__lowerCamelCase    :  EvalPrediction	) -> Dict:
    __UpperCAmelCase							:			Union[str, Any]     =						align_predictions(p.predictions						,	p.label_ids	)

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

  # Initialize our Trainer
  __UpperCAmelCase							:			int     =						TFTrainer(
      model=__lowerCamelCase						,	args=__lowerCamelCase						,	train_dataset=train_dataset.get_dataset() if train_dataset else None						,	eval_dataset=eval_dataset.get_dataset() if eval_dataset else None						,	compute_metrics=__lowerCamelCase						,	)

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

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

    __UpperCAmelCase							:			Optional[int]     =						trainer.evaluate()
    __UpperCAmelCase							:			Optional[int]     =						os.path.join(training_args.output_dir						,	"""eval_results.txt"""	)

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

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

      results.update(__lowerCamelCase	)

    # Predict
  if training_args.do_predict:
    __UpperCAmelCase							:			str     =						TFTokenClassificationDataset(
        token_classification_task=__lowerCamelCase						,	data_dir=data_args.data_dir						,	tokenizer=__lowerCamelCase						,	labels=__lowerCamelCase						,	model_type=config.model_type						,	max_seq_length=data_args.max_seq_length						,	overwrite_cache=data_args.overwrite_cache						,	mode=Split.test						,	)

    __UpperCAmelCase							:			Tuple     =						trainer.predict(test_dataset.get_dataset()	)
    __UpperCAmelCase							:			str     =						align_predictions(__lowerCamelCase						,	__lowerCamelCase	)
    __UpperCAmelCase							:			Optional[Any]     =						classification_report(__lowerCamelCase						,	__lowerCamelCase	)

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

    __UpperCAmelCase							:			List[Any]     =						os.path.join(training_args.output_dir						,	"""test_results.txt"""	)

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

    # Save predictions
    __UpperCAmelCase							:			Tuple     =						os.path.join(training_args.output_dir						,	"""test_predictions.txt"""	)

    with open(__lowerCamelCase						,	"""w"""	) as writer:
      with open(os.path.join(data_args.data_dir						,	"""test.txt"""	)						,	"""r"""	) as f:
        __UpperCAmelCase							:			Union[str, Any]     =						0

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

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

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

  return results


if __name__ == "__main__":
       main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a	: Union[str, Any]      =				{
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				["MobileNetV1FeatureExtractor"]
       a	: int      =				["MobileNetV1ImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: int      =				[
           "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileNetV1ForImageClassification",
           "MobileNetV1Model",
           "MobileNetV1PreTrainedModel",
           "load_tf_weights_in_mobilenet_v1",
       ]


if TYPE_CHECKING:
       from .configuration_mobilenet_va import (
           MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
           MobileNetVaConfig,
           MobileNetVaOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
              from .image_processing_mobilenet_va import MobileNetVaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilenet_va import (
                  MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileNetVaForImageClassification,
                  MobileNetVaModel,
                  MobileNetVaPreTrainedModel,
                  load_tf_weights_in_mobilenet_va,
              )


else:
       import sys

       a	: str      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


