'''simple docstring'''




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


_a   :	List[Any] 							=   logging.getLogger(__name__)






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






@dataclass
class 		__A       :
       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(
           default=__magic_name__ ,					metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} ,					)
       snake_case       :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."
               )
           } ,					)
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={"help": "Overwrite the cached training and evaluation sets"}   )





def _lowercase	(      )					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		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.
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase  :				str       				=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
       else:
              __UpperCAmelCase				, __UpperCAmelCase				, __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  :				List[str]       				=		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 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"							,    lowerCamelCase__						)

       # Set seed
       set_seed(training_args.seed						)

       # Prepare CONLL-2003 task
       __UpperCAmelCase  :				Any       				=		token_classification_task.get_labels(data_args.labels						)
       __UpperCAmelCase  :				Dict[int, str]       				=		dict(enumerate(lowerCamelCase__						)						)
       __UpperCAmelCase  :				Any       				=		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  :				List[str]       				=		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[Any]       				=		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							,    )
       __UpperCAmelCase  :				Union[str, Any]       				=		AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path							,    from_tf=bool(".ckpt" in model_args.model_name_or_path						)							,    config=lowerCamelCase__							,    cache_dir=model_args.cache_dir							,    )

       # Get datasets
       __UpperCAmelCase  :				Tuple       				=		(
           TokenClassificationDataset(
               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  :				int       				=		(
           TokenClassificationDataset(
               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__							,    lowerCamelCase__						) -> Tuple[List[int], List[int]]:
              __UpperCAmelCase  :				Tuple       				=		np.argmax(lowerCamelCase__							,    axis=2						)

              __UpperCAmelCase				, __UpperCAmelCase  :				str       				=		preds.shape

              __UpperCAmelCase  :				List[Any]       				=		[[] for _ in range(lowerCamelCase__						)]
              __UpperCAmelCase  :				Union[str, Any]       				=		[[] for _ in range(lowerCamelCase__						)]

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

       # Data collator
       __UpperCAmelCase  :				Dict       				=		DataCollatorWithPadding(lowerCamelCase__							,    pad_to_multiple_of=8						) if training_args.fpaa else None

       # Initialize our Trainer
       __UpperCAmelCase  :				List[str]       				=		Trainer(
           model=lowerCamelCase__							,    args=lowerCamelCase__							,    train_dataset=lowerCamelCase__							,    eval_dataset=lowerCamelCase__							,    compute_metrics=lowerCamelCase__							,    data_collator=lowerCamelCase__							,    )

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

              __UpperCAmelCase  :				Tuple       				=		trainer.evaluate()

              __UpperCAmelCase  :				List[str]       				=		os.path.join(training_args.output_dir							,    "eval_results.txt"						)
              if trainer.is_world_process_zero():
                     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  :				List[Any]       				=		TokenClassificationDataset(
                  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  :				List[Any]       				=		trainer.predict(lowerCamelCase__						)
              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		align_predictions(lowerCamelCase__							,    lowerCamelCase__						)

              __UpperCAmelCase  :				str       				=		os.path.join(training_args.output_dir							,    "test_results.txt"						)
              if trainer.is_world_process_zero():
                     with open(lowerCamelCase__							,    "w"						) as writer:
                            for key, value in metrics.items():
                                   logger.info("  %s = %s"							,    lowerCamelCase__							,    lowerCamelCase__						)
                                   writer.write("%s = %s\n" % (key, value)						)

        # Save predictions
              __UpperCAmelCase  :				Any       				=		os.path.join(training_args.output_dir							,    "test_predictions.txt"						)
              if trainer.is_world_process_zero():
                     with open(lowerCamelCase__							,    "w"						) as writer:
                            with open(os.path.join(data_args.data_dir							,    "test.txt"						)							,    "r"						) as f:
                                   token_classification_task.write_predictions_to_file(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)

       return results

def _lowercase	(      lowerCamelCase__						)					->       Optional[int]:





       """simple docstring"""


       main()


if __name__ == "__main__":
   main()







'''simple docstring'''




from typing import TYPE_CHECKING

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


_a   :	str 							=   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	str 							=   ["ViTFeatureExtractor"]
   _a   :	Dict 							=   ["ViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ViTForImageClassification",
       "ViTForMaskedImageModeling",
       "ViTModel",
       "ViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[str] 							=   [
       "TFViTForImageClassification",
       "TFViTModel",
       "TFViTPreTrainedModel",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   [
       "FlaxViTForImageClassification",
       "FlaxViTModel",
       "FlaxViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _a   :	Dict 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






