


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


snake_case							:       str										=	logging.getLogger(__name__)







@dataclass
class 						_snake_case    :
 SCREAMING_SNAKE_CASE__ =			field(
     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
 SCREAMING_SNAKE_CASE__ =			field(
     default=_snake_case		,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
 SCREAMING_SNAKE_CASE__ =			field(
     default='NER'		,       metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}				)
 SCREAMING_SNAKE_CASE__ =			field(
     default=_snake_case		,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
 SCREAMING_SNAKE_CASE__ =			field(default=_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.
 SCREAMING_SNAKE_CASE__ =			field(
     default=_snake_case		,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}		,       )







@dataclass
class 						_snake_case    :
 SCREAMING_SNAKE_CASE__ =			field(
     metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}				)
 SCREAMING_SNAKE_CASE__ =			field(
     default=_snake_case		,       metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}		,       )
 SCREAMING_SNAKE_CASE__ =			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.'
         )
     }		,       )
 SCREAMING_SNAKE_CASE__ =			field(
     default=_snake_case		,       metadata={'help': 'Overwrite the cached training and evaluation sets'}				)







def 							__lowerCamelCase		(					):




      """simple docstring"""





      a		:Union[str, Any]									=			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		:Union[str, Any]									=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
      else:
            a				,							a				,							a		:Dict									=			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		:List[Any]									=			import_module('''tasks'''		)
      try:
            a		:Optional[int]									=			getattr(UpperCAmelCase_					,    model_args.task_type		)
            a		: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 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'''					,    UpperCAmelCase_		)

      # Set seed
      set_seed(training_args.seed		)

      # Prepare CONLL-2003 task
      a		:List[Any]									=			token_classification_task.get_labels(data_args.labels		)
      a		:Dict[int, str]									=			dict(enumerate(UpperCAmelCase_		)		)
      a		:Union[str, Any]									=			len(UpperCAmelCase_		)

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

      a		:List[str]									=			AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path					,    num_labels=UpperCAmelCase_					,    idalabel=UpperCAmelCase_					,    labelaid={label: i for i, label in enumerate(UpperCAmelCase_		)}					,    cache_dir=model_args.cache_dir					,    )
      a		:Optional[int]									=			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		:Optional[Any]									=			AutoModelForTokenClassification.from_pretrained(
          model_args.model_name_or_path					,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)					,    config=UpperCAmelCase_					,    cache_dir=model_args.cache_dir					,    )

      # Get datasets
      a		:List[Any]									=			(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase_					,    data_dir=data_args.data_dir					,    tokenizer=UpperCAmelCase_					,    labels=UpperCAmelCase_					,    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		:Optional[int]									=			(
          TokenClassificationDataset(
              token_classification_task=UpperCAmelCase_					,    data_dir=data_args.data_dir					,    tokenizer=UpperCAmelCase_					,    labels=UpperCAmelCase_					,    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(UpperCAmelCase_		:						np.ndarray					,    UpperCAmelCase_		:						np.ndarray		) -> Tuple[List[int], List[int]]:
            a		:List[Any]									=			np.argmax(UpperCAmelCase_					,    axis=2		)

            a				,							a		:Union[str, Any]									=			preds.shape

            a		:int									=			[[] for _ in range(UpperCAmelCase_		)]
            a		:str									=			[[] for _ in range(UpperCAmelCase_		)]

            for i in range(UpperCAmelCase_		):
                  for j in range(UpperCAmelCase_		):
                        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(UpperCAmelCase_		:						EvalPrediction		) -> Dict:
            a				,							a		:Dict									=			align_predictions(p.predictions					,    p.label_ids		)
            return {
                "accuracy_score": accuracy_score(UpperCAmelCase_					,    UpperCAmelCase_		),
                "precision": precision_score(UpperCAmelCase_					,    UpperCAmelCase_		),
                "recall": recall_score(UpperCAmelCase_					,    UpperCAmelCase_		),
                "f1": fa_score(UpperCAmelCase_					,    UpperCAmelCase_		),
            }

      # Data collator
      a		:Tuple									=			DataCollatorWithPadding(UpperCAmelCase_					,    pad_to_multiple_of=8		) if training_args.fpaa else None

      # Initialize our Trainer
      a		:List[Any]									=			Trainer(
          model=UpperCAmelCase_					,    args=UpperCAmelCase_					,    train_dataset=UpperCAmelCase_					,    eval_dataset=UpperCAmelCase_					,    compute_metrics=UpperCAmelCase_					,    data_collator=UpperCAmelCase_					,    )

      # 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		:List[str]									=			{}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''		)

            a		:Optional[Any]									=			trainer.evaluate()

            a		:Tuple									=			os.path.join(training_args.output_dir					,    '''eval_results.txt'''		)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase_					,    '''w'''		) as writer:
                        logger.info('''***** Eval results *****'''		)
                        for key, value in result.items():
                              logger.info('''  %s = %s'''					,    UpperCAmelCase_					,    UpperCAmelCase_		)
                              writer.write('''%s = %s\n''' % (key, value)		)

                  results.update(UpperCAmelCase_		)

    # Predict
      if training_args.do_predict:
            a		:List[str]									=			TokenClassificationDataset(
                token_classification_task=UpperCAmelCase_					,    data_dir=data_args.data_dir					,    tokenizer=UpperCAmelCase_					,    labels=UpperCAmelCase_					,    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		:Optional[int]									=			trainer.predict(UpperCAmelCase_		)
            a				,							a		:str									=			align_predictions(UpperCAmelCase_					,    UpperCAmelCase_		)

            a		:Optional[Any]									=			os.path.join(training_args.output_dir					,    '''test_results.txt'''		)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase_					,    '''w'''		) as writer:
                        for key, value in metrics.items():
                              logger.info('''  %s = %s'''					,    UpperCAmelCase_					,    UpperCAmelCase_		)
                              writer.write('''%s = %s\n''' % (key, value)		)

        # Save predictions
            a		:int									=			os.path.join(training_args.output_dir					,    '''test_predictions.txt'''		)
            if trainer.is_world_process_zero():
                  with open(UpperCAmelCase_					,    '''w'''		) as writer:
                        with open(os.path.join(data_args.data_dir					,    '''test.txt'''		)					,    '''r'''		) as f:
                              token_classification_task.write_predictions_to_file(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      return results
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      main()


if __name__ == "__main__":
     main()









from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       Dict										=	{
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'visual_bert'




 def __init__(						self   ,    _lowerCamelCase=3_0522   ,    _lowerCamelCase=768   ,    _lowerCamelCase=512   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-12   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )

       a		:Any									=			vocab_size
       a		:str									=			max_position_embeddings
       a		:str									=			hidden_size
       a		:List[Any]									=			visual_embedding_dim
       a		:str									=			num_hidden_layers
       a		:Optional[int]									=			num_attention_heads
       a		:int									=			intermediate_size
       a		:int									=			hidden_act
       a		:Union[str, Any]									=			hidden_dropout_prob
       a		:List[Any]									=			attention_probs_dropout_prob
       a		:int									=			initializer_range
       a		:List[Any]									=			type_vocab_size
       a		:str									=			layer_norm_eps
       a		:Optional[int]									=			bypass_transformer
       a		:str									=			special_visual_initialize





