


"""simple docstring"""



import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
  import numpy as np
  import torch

  from . import (
      AlbertForPreTraining,
      BartForConditionalGeneration,
      BertForPreTraining,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      CamembertForMaskedLM,
      CTRLLMHeadModel,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DPRContextEncoder,
      DPRQuestionEncoder,
      DPRReader,
      ElectraForPreTraining,
      FlaubertWithLMHeadModel,
      GPTaLMHeadModel,
      LayoutLMForMaskedLM,
      LxmertForPreTraining,
      LxmertVisualFeatureEncoder,
      OpenAIGPTLMHeadModel,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      TaForConditionalGeneration,
      TransfoXLLMHeadModel,
      XLMRobertaForMaskedLM,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
  )


logging.set_verbosity_info()

__snake_case   			=    {
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  str						,							lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Any=False						,							lowercase  :  str=True )	->						Optional[int]:


    """simple docstring"""





    if model_type not in MODEL_CLASSES:
        raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.' )

    snake_case					,snake_case					,snake_case					,snake_case    :      Optional[int]      					=    MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        snake_case    :      Any      					=    cached_file(lowercase						,							lowercase						,							force_download=not use_cached_models )
    snake_case    :      Optional[int]      					=    config_class.from_json_file(lowercase )
    snake_case    :      Union[str, Any]      					=    True
    snake_case    :      Union[str, Any]      					=    True
    print(F'Building TensorFlow model from configuration: {config}' )
    snake_case    :      Union[str, Any]      					=    model_class(lowercase )

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        snake_case    :      Optional[int]      					=    cached_file(
            lowercase						,							lowercase						,							force_download=not use_cached_models )
    # Load PyTorch checkpoint in tf2 model:
    snake_case    :      Optional[Any]      					=    load_pytorch_checkpoint_in_tfa_model(lowercase						,							lowercase )

    if compare_with_pt_model:
        snake_case    :      Optional[Any]      					=    tf_model(tf_model.dummy_inputs						,							training=lowercase )  # build the network

        snake_case    :      List[str]      					=    torch.load(lowercase						,							map_location="cpu" )
        snake_case    :      str      					=    pt_model_class.from_pretrained(
            pretrained_model_name_or_path=lowercase						,							config=lowercase						,							state_dict=lowercase )

        with torch.no_grad():
            snake_case    :      Any      					=    pt_model(**pt_model.dummy_inputs )

        snake_case    :      Union[str, Any]      					=    pto[0].numpy()
        snake_case    :      Optional[int]      					=    tfo[0].numpy()
        snake_case    :      List[str]      					=    np.amax(np.abs(np_pt - np_tf ) )
        print(F'Max absolute difference between models outputs {diff}' )
        assert diff <= 2e-2, F'Error, model absolute difference is >2e-2: {diff}'

    # Save pytorch-model
    print(F'Save TensorFlow model to {tf_dump_path}' )
    tf_model.save_weights(lowercase						,							save_format="h5" )







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  Any						,							lowercase  :  str=None						,							lowercase  :  Optional[int]=None						,							lowercase  :  Dict=False						,							lowercase  :  Optional[Any]=False						,							lowercase  :  Optional[int]=False						,							lowercase  :  Union[str, Any]=False						,							)	->						Any:


    """simple docstring"""





    if args_model_type is None:
        snake_case    :      Any      					=    list(MODEL_CLASSES.keys() )
    else:
        snake_case    :      Optional[Any]      					=    [args_model_type]

    for j, model_type in enumerate(lowercase						,							start=1 ):
        print("=" * 100 )
        print(F' Converting model type {j}/{len(lowercase )}: {model_type}' )
        print("=" * 100 )
        if model_type not in MODEL_CLASSES:
            raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.' )

        snake_case					,snake_case					,snake_case					,snake_case					,snake_case    :      Union[str, Any]      					=    MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            snake_case    :      Dict      					=    list(aws_model_maps.keys() )
        if config_shortcut_names_or_path is None:
            snake_case    :      Union[str, Any]      					=    model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(lowercase						,							lowercase )						,							start=1 ):
            print("-" * 100 )
            if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
                if not only_convert_finetuned_models:
                    print(F'    Skipping finetuned checkpoint {model_shortcut_name}' )
                    continue
                snake_case    :      Dict      					=    model_shortcut_name
            elif only_convert_finetuned_models:
                print(F'    Skipping not finetuned checkpoint {model_shortcut_name}' )
                continue
            print(
                F'    Converting checkpoint {i}/{len(lowercase )}: {model_shortcut_name} - model_type {model_type}' )
            print("-" * 100 )

            if config_shortcut_name in aws_config_map:
                snake_case    :      List[str]      					=    cached_file(lowercase						,							lowercase						,							force_download=not use_cached_models )
            else:
                snake_case    :      str      					=    config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                snake_case    :      List[str]      					=    cached_file(lowercase						,							lowercase						,							force_download=not use_cached_models )
            else:
                snake_case    :      Union[str, Any]      					=    model_shortcut_name

            if os.path.isfile(lowercase ):
                snake_case    :      Dict      					=    "converted_model"

            convert_pt_checkpoint_to_tf(
                model_type=lowercase						,							pytorch_checkpoint_path=lowercase						,							config_file=lowercase						,							tf_dump_path=os.path.join(lowercase						,							model_shortcut_name + "-tf_model.h5" )						,							compare_with_pt_model=lowercase						,							)
            if remove_cached_files:
                os.remove(lowercase )
                os.remove(lowercase )


if __name__ == "__main__":
  __snake_case   			=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_dump_path""", default=None, type=str, required=True, help="""Path to the output Tensorflow dump file."""
  )
  parser.add_argument(
      """--model_type""",
      default=None,
      type=str,
      help=(
          F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
          """convert all the models from AWS."""
      ),
  )
  parser.add_argument(
      """--pytorch_checkpoint_path""",
      default=None,
      type=str,
      help=(
          """Path to the PyTorch checkpoint path or shortcut name to download from AWS. """
          """If not given, will download and convert all the checkpoints from AWS."""
      ),
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      help=(
          """The config json file corresponding to the pre-trained model. \n"""
          """This specifies the model architecture. If not given and """
          """--pytorch_checkpoint_path is not given or is a shortcut name """
          """use the configuration associated to the shortcut name on the AWS"""
      ),
  )
  parser.add_argument(
      """--compare_with_pt_model""", action="""store_true""", help="""Compare Tensorflow and PyTorch model predictions."""
  )
  parser.add_argument(
      """--use_cached_models""",
      action="""store_true""",
      help="""Use cached models if possible instead of updating to latest checkpoint versions.""",
  )
  parser.add_argument(
      """--remove_cached_files""",
      action="""store_true""",
      help="""Remove pytorch models after conversion (save memory when converting in batches).""",
  )
  parser.add_argument("""--only_convert_finetuned_models""", action="""store_true""", help="""Only convert finetuned models.""")
  __snake_case   			=    parser.parse_args()

  # if args.pytorch_checkpoint_path is not None:
  #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
  #                                 args.pytorch_checkpoint_path,
  #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
  #                                 args.tf_dump_path,
  #                                 compare_with_pt_model=args.compare_with_pt_model,
  #                                 use_cached_models=args.use_cached_models)
  # else:
  convert_all_pt_checkpoints_to_tf(
      args.model_type.lower() if args.model_type is not None else None,
      args.tf_dump_path,
      model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
      if args.pytorch_checkpoint_path is not None
      else None,
      config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
      compare_with_pt_model=args.compare_with_pt_model,
      use_cached_models=args.use_cached_models,
      remove_cached_files=args.remove_cached_files,
      only_convert_finetuned_models=args.only_convert_finetuned_models,
  )









"""simple docstring"""



import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__snake_case   			=    logging.getLogger(__name__)







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       Optional[int]							  =  '''token-classification'''







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

       '''simple docstring'''




       if type(UpperCamelCase__						) == dict:
           snake_case    :      Optional[int]      					=    Namespace(**UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    import_module("tasks"						)
       try:
           snake_case    :      Optional[int]      					=    getattr(UpperCamelCase__						,	hparams.task_type						)
           snake_case    :      TokenClassificationTask      					=    token_classification_task_clazz()
       except AttributeError:
           raise ValueError(
               F'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
               F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'						)
       snake_case    :      str      					=    self.token_classification_task.get_labels(hparams.labels						)
       snake_case    :      Union[str, Any]      					=    CrossEntropyLoss().ignore_index
       super().__init__(UpperCamelCase__						,	len(self.labels						)						,	self.mode						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       return self.model(**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
       if self.config.model_type != "distilbert":
           snake_case    :      Optional[int]      					=    (
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids

       snake_case    :      List[Any]      					=    self(**UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    outputs[0]
       # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
       return {"loss": loss}







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    self.hparams
       for mode in ["train", "dev", "test"]:
           snake_case    :      Optional[Any]      					=    self._feature_file(UpperCamelCase__						)
           if os.path.exists(UpperCamelCase__						) and not args.overwrite_cache:
               logger.info("Loading features from cached file %s"						,	UpperCamelCase__						)
               snake_case    :      List[str]      					=    torch.load(UpperCamelCase__						)
           else:
               logger.info("Creating features from dataset file at %s"						,	args.data_dir						)
               snake_case    :      Optional[Any]      					=    self.token_classification_task.read_examples_from_file(args.data_dir						,	UpperCamelCase__						)
               snake_case    :      Dict      					=    self.token_classification_task.convert_examples_to_features(
                   UpperCamelCase__						,	self.labels						,	args.max_seq_length						,	self.tokenizer						,	cls_token_at_end=bool(self.config.model_type in ["xlnet"]						)						,	cls_token=self.tokenizer.cls_token						,	cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0						,	sep_token=self.tokenizer.sep_token						,	sep_token_extra=UpperCamelCase__						,	pad_on_left=bool(self.config.model_type in ["xlnet"]						)						,	pad_token=self.tokenizer.pad_token_id						,	pad_token_segment_id=self.tokenizer.pad_token_type_id						,	pad_token_label_id=self.pad_token_label_id						,	)
               logger.info("Saving features into cached file %s"						,	UpperCamelCase__						)
               torch.save(UpperCamelCase__						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = False						)	->		DataLoader:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    self._feature_file(UpperCamelCase__						)
       logger.info("Loading features from cached file %s"						,	UpperCamelCase__						)
       snake_case    :      Any      					=    torch.load(UpperCamelCase__						)
       snake_case    :      Tuple      					=    torch.tensor([f.input_ids for f in features]						,	dtype=torch.long						)
       snake_case    :      List[str]      					=    torch.tensor([f.attention_mask for f in features]						,	dtype=torch.long						)
       if features[0].token_type_ids is not None:
           snake_case    :      Tuple      					=    torch.tensor([f.token_type_ids for f in features]						,	dtype=torch.long						)
       else:
           snake_case    :      Union[str, Any]      					=    torch.tensor([0 for f in features]						,	dtype=torch.long						)
           # HACK(we will not use this anymore soon)
       snake_case    :      Optional[int]      					=    torch.tensor([f.label_ids for f in features]						,	dtype=torch.long						)
       return DataLoader(
           TensorDataset(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)						,	batch_size=UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Optional[Any]:

       '''simple docstring'''




       """Compute validation""" ""
       snake_case    :      Any      					=    {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
       if self.config.model_type != "distilbert":
           snake_case    :      Optional[int]      					=    (
               batch[2] if self.config.model_type in ["bert", "xlnet"] else None
           )  # XLM and RoBERTa don"t use token_type_ids
       snake_case    :      Optional[int]      					=    self(**UpperCamelCase__						)
       snake_case					,snake_case    :      str      					=    outputs[:2]
       snake_case    :      Optional[int]      					=    logits.detach().cpu().numpy()
       snake_case    :      str      					=    inputs["labels"].detach().cpu().numpy()
       return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Dict      					=    torch.stack([x["val_loss"] for x in outputs]						).mean()
       snake_case    :      List[str]      					=    np.concatenate([x["pred"] for x in outputs]						,	axis=0						)
       snake_case    :      Any      					=    np.argmax(UpperCamelCase__						,	axis=2						)
       snake_case    :      Dict      					=    np.concatenate([x["target"] for x in outputs]						,	axis=0						)

       snake_case    :      Tuple      					=    dict(enumerate(self.labels						)						)
       snake_case    :      str      					=    [[] for _ in range(out_label_ids.shape[0]						)]
       snake_case    :      List[Any]      					=    [[] for _ in range(out_label_ids.shape[0]						)]

       for i in range(out_label_ids.shape[0]						):
           for j in range(out_label_ids.shape[1]						):
               if out_label_ids[i, j] != self.pad_token_label_id:
                   out_label_list[i].append(label_map[out_label_ids[i][j]]						)
                   preds_list[i].append(label_map[preds[i][j]]						)

       snake_case    :      Union[str, Any]      					=    {
           "val_loss": val_loss_mean,
           "accuracy_score": accuracy_score(UpperCamelCase__						,	UpperCamelCase__						),
           "precision": precision_score(UpperCamelCase__						,	UpperCamelCase__						),
           "recall": recall_score(UpperCamelCase__						,	UpperCamelCase__						),
           "f1": fa_score(UpperCamelCase__						,	UpperCamelCase__						),
       }

       snake_case    :      int      					=    dict(results.items()						)
       snake_case    :      Union[str, Any]      					=    results
       return ret, preds_list, out_label_list







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       snake_case					,snake_case					,snake_case    :      Optional[Any]      					=    self._eval_end(UpperCamelCase__						)
       snake_case    :      Tuple      					=    ret["log"]
       return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case					,snake_case					,snake_case    :      List[Any]      					=    self._eval_end(UpperCamelCase__						)

       # Converting to the dict required by pl
       # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
       # pytorch_lightning/trainer/logging.py#L139
       snake_case    :      Optional[Any]      					=    ret["log"]
       # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
       return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







   @staticmethod
   def  lowerCamelCase		(	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       BaseTransformer.add_model_specific_args(UpperCamelCase__						,	UpperCamelCase__						)
       parser.add_argument(
           "--task_type"						,	default="NER"						,	type=UpperCamelCase__						,	help="Task type to fine tune in training (e.g. NER, POS, etc)"						)
       parser.add_argument(
           "--max_seq_length"						,	default=128						,	type=UpperCamelCase__						,	help=(
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )						,	)

       parser.add_argument(
           "--labels"						,	default=""						,	type=UpperCamelCase__						,	help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."						,	)
       parser.add_argument(
           "--gpus"						,	default=0						,	type=UpperCamelCase__						,	help="The number of GPUs allocated for this, it is by default 0 meaning none"						,	)

       parser.add_argument(
           "--overwrite_cache"						,	action="store_true"						,	help="Overwrite the cached training and evaluation sets"						)

       return parser


if __name__ == "__main__":
  __snake_case   			=    argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  __snake_case   			=    NERTransformer.add_model_specific_args(parser, os.getcwd())
  __snake_case   			=    parser.parse_args()
  __snake_case   			=    NERTransformer(args)
  __snake_case   			=    generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    __snake_case   			=    sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    __snake_case   			=    model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)





