




"""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()


_SCREAMING_SNAKE_CASE						 =     logging.getLogger(__name__)







@dataclass
class  _SCREAMING_SNAKE_CASE       :



 '''simple docstring'''





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







@dataclass
class  _SCREAMING_SNAKE_CASE       :



 '''simple docstring'''





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






def 				__lowerCAmelCase					(					)    ->       int:
 # 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      :	Optional[Any]					      =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
 _UpperCamelCase      ,	_UpperCamelCase      ,	_UpperCamelCase      :	str					      =  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      :	str					      =  import_module("tasks"						)

 try:
  _UpperCamelCase      :	str					      =  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      :	Any					      =  token_classification_task.get_labels(data_args.labels						)
 _UpperCamelCase      :	Dict[int, str]					      =  dict(enumerate(__lowerCAmelCase						)						)
 _UpperCamelCase      :	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      :	Optional[Any]					      =  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      :	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  ,	)

 with training_args.strategy.scope():
  _UpperCamelCase      :	Tuple					      =  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      :	int					      =  (
     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      :	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.dev  ,	)
     if training_args.do_eval
     else None
 )

 def align_predictions(__lowerCAmelCase			:		np.ndarray  ,	__lowerCAmelCase			:		np.ndarray						) -> Tuple[List[int], List[int]]:
  _UpperCamelCase      :	int					      =  np.argmax(__lowerCAmelCase  ,	axis=2						)
  _UpperCamelCase      ,	_UpperCamelCase      :	List[str]					      =  preds.shape
  _UpperCamelCase      :	Any					      =  [[] for _ in range(__lowerCAmelCase						)]
  _UpperCamelCase      :	Any					      =  [[] 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      ,	_UpperCamelCase      :	Dict					      =  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      :	List[Any]					      =  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      :	Union[str, Any]					      =  {}
 if training_args.do_eval:
  logger.info("*** Evaluate ***"						)

  _UpperCamelCase      :	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      ,	_UpperCamelCase      ,	_UpperCamelCase      :	Dict					      =  trainer.predict(test_dataset.get_dataset()						)
  _UpperCamelCase      ,	_UpperCamelCase      :	Union[str, Any]					      =  align_predictions(__lowerCAmelCase  ,	__lowerCAmelCase						)
  _UpperCamelCase      :	Union[str, Any]					      =  classification_report(__lowerCAmelCase  ,	__lowerCAmelCase						)

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

  _UpperCamelCase      :	str					      =  os.path.join(training_args.output_dir  ,	"test_results.txt"						)

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

  # Save predictions
  _UpperCamelCase      :	Union[str, Any]					      =  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      :	Optional[int]					      =  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      :	List[Any]					      =  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()









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE						 =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5Tokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """T5EncoderModel""",
        """T5ForConditionalGeneration""",
        """T5Model""",
        """T5PreTrainedModel""",
        """load_tf_weights_in_t5""",
        """T5ForQuestionAnswering""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFT5EncoderModel""",
        """TFT5ForConditionalGeneration""",
        """TFT5Model""",
        """TFT5PreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """FlaxT5EncoderModel""",
        """FlaxT5ForConditionalGeneration""",
        """FlaxT5Model""",
        """FlaxT5PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta import TaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta_fast import TaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ta import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TaEncoderModel,
            TaForConditionalGeneration,
            TaForQuestionAnswering,
            TaModel,
            TaPreTrainedModel,
            load_tf_weights_in_ta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ta import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTaEncoderModel,
            TFTaForConditionalGeneration,
            TFTaModel,
            TFTaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_ta import (
            FlaxTaEncoderModel,
            FlaxTaForConditionalGeneration,
            FlaxTaModel,
            FlaxTaPreTrainedModel,
        )


else:
    import sys

    _SCREAMING_SNAKE_CASE						 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



