



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  :



   '''simple docstring'''






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







@dataclass
class 						snake_case  :



   '''simple docstring'''






   snake_case_   :	str       =						field(
       metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}					)
   snake_case_   :	Optional[str]       =						field(
       metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}					)
   snake_case_   :	int       =						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."""
           )
       }   ,)
   snake_case_   :	bool       =						field(
       default=SCREAMING_SNAKE_CASE_   ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)



def  lowercase    (				)					->       Union[str, 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.
 _snake_case      :					List[Any]					=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
 _snake_case		,      _snake_case		,      _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."""			)

 _snake_case      :					Union[str, Any]					=   import_module("""tasks"""			)

 try:
  _snake_case      :					List[str]					=   getattr(SCREAMING_SNAKE_CASE__			,    model_args.task_type			)
  _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"""			,    SCREAMING_SNAKE_CASE__			)

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

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

 _snake_case      :					int					=   AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path			,    num_labels=SCREAMING_SNAKE_CASE__			,    idalabel=SCREAMING_SNAKE_CASE__			,    labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__			)}			,    cache_dir=model_args.cache_dir			,    )
 _snake_case      :					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():
  _snake_case      :					str					=   TFAutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path			,    from_pt=bool(""".bin""" in model_args.model_name_or_path			)			,    config=SCREAMING_SNAKE_CASE__			,    cache_dir=model_args.cache_dir			,    )

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

  for i in range(SCREAMING_SNAKE_CASE__			):
   for j in range(SCREAMING_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(SCREAMING_SNAKE_CASE__    :		EvalPrediction			) -> Dict:
  _snake_case		,      _snake_case      :					Optional[int]					=   align_predictions(p.predictions			,    p.label_ids			)

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

 # Initialize our Trainer
 _snake_case      :					Any					=   TFTrainer(
     model=SCREAMING_SNAKE_CASE__			,    args=SCREAMING_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=SCREAMING_SNAKE_CASE__			,    )

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

 # Evaluation
 _snake_case      :					Any					=   {}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""			)

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

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

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

   results.update(SCREAMING_SNAKE_CASE__			)

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

  _snake_case		,      _snake_case		,      _snake_case      :					Tuple					=   trainer.predict(test_dataset.get_dataset()			)
  _snake_case		,      _snake_case      :					int					=   align_predictions(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)
  _snake_case      :					Any					=   classification_report(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			)

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

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

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

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

  with open(SCREAMING_SNAKE_CASE__			,    """w"""			) as writer:
   with open(os.path.join(data_args.data_dir			,    """test.txt"""			)			,    """r"""			) as f:
    _snake_case      :					Optional[int]					=   0

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

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

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

 return results


if __name__ == "__main__":
  main()





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


a__      =		logging.get_logger(__name__)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     Tuple)			->			Optional[int]:

    """simple docstring"""







    super().__init__()
    if version.parse(torch.__version__) < version.parse("""1.12.0"""):
     raise ImportError(
         F'''You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use '''
         """PytorchGELUTanh. Please upgrade torch.""")


   def        UpperCamelCase_					(   self							:     Tuple			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return nn.functional.gelu(lowerCAmelCase			,						approximate="""tanh""")







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044_715 * torch.pow(lowerCAmelCase			,						3.0))))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     str			,						lowerCAmelCase							:     bool = False)			->			Optional[Any]:

    """simple docstring"""







    super().__init__()
    if use_gelu_python:
     _snake_case      :					Optional[int]					=   self._gelu_python
    else:
     _snake_case      :					Any					=   nn.functional.gelu

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))


   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return self.act(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     int			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1.0 + torch.tanh(input * 0.7_978_845_608 * (1.0 + 0.044_715 * input * input)))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     int			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * torch.sigmoid(1.702 * input)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     int			,						lowerCAmelCase							:     float			,						lowerCAmelCase							:     float)			->			Optional[int]:

    """simple docstring"""







    if min > max:
     raise ValueError(F'''min should be < max (got min: {min}, max: {max})''')

    super().__init__()
    _snake_case      :					Dict					=   min
    _snake_case      :					Optional[int]					=   max


   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return torch.clip(gelu(lowerCAmelCase)			,						self.min			,						self.max)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     Tuple)			->			List[str]:

    """simple docstring"""







    super().__init__()
    _snake_case      :					Union[str, Any]					=   math.sqrt(2 / math.pi)


   def        UpperCamelCase_					(   self							:     Dict			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044_715 * torch.pow(lowerCAmelCase			,						3))))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return nn.functional.silu(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def __init__(   self							:     List[Any])			->			str:

    """simple docstring"""







    super().__init__()
    if version.parse(torch.__version__) < version.parse("""1.9.0"""):
     _snake_case      :					Optional[int]					=   self._mish_python
    else:
     _snake_case      :					List[str]					=   nn.functional.mish

   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input * torch.tanh(nn.functional.softplus(lowerCAmelCase))


   def        UpperCamelCase_					(   self							:     str			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return self.act(lowerCAmelCase)







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     Tensor)			->			Tensor:

    """simple docstring"""







    return input







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     List[Any]			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     List[Any]=0.707_107			,						lowerCAmelCase							:     Optional[Any]=0.282_095)			->			List[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   (input - mu).div(sigma * math.sqrt(2.0))
    return 0.5 * (1.0 + torch.erf(lowerCAmelCase))







class 						snake_case  (		nn.Module					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Any			,						lowerCAmelCase							:     Dict)			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   nn.functional.relu(lowerCAmelCase)
    _snake_case      :					Tuple					=   torch.square(lowerCAmelCase)
    return squared







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   def __getitem__(   self							:     Dict			,						lowerCAmelCase							:     Tuple)			->			str:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   super().__getitem__(lowerCAmelCase)
    _snake_case		,      _snake_case      :					List[str]					=   content if isinstance(lowerCAmelCase			,						lowerCAmelCase) else (content, {})
    return cls(**lowerCAmelCase)
a__      =		{
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -10, """max""": 10}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
a__      =		ClassInstantier(ACTaCLS)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			)					->       int:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()			)}'''			)


# For backwards compatibility with: from activations import gelu_python
a__      =		get_activation("""gelu_python""")
a__      =		get_activation("""gelu_new""")
a__      =		get_activation("""gelu""")
a__      =		get_activation("""gelu_fast""")
a__      =		get_activation("""quick_gelu""")
a__      =		get_activation("""silu""")
a__      =		get_activation("""mish""")

a__      =		get_activation("""linear""")
