


'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    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()
def 				lowerCAmelCase__	(    lowerCamelCase					:      str  ,lowerCamelCase					:      str  ,lowerCamelCase					:      str  ,lowerCamelCase					:      PreTrainedTokenizer  ,lowerCamelCase					:      int  ,lowerCamelCase					:      Optional[int] = None  ,):
       _A					:     Tuple     = {}

       if train_file is not None:
              _A					:     Optional[int]     = [train_file]
       if eval_file is not None:
              _A					:     Optional[int]     = [eval_file]
       if test_file is not None:
              _A					:     Dict     = [test_file]

       _A					:     List[str]     = datasets.load_dataset('csv'  ,data_files=lowerCamelCase       )
       _A					:     Union[str, Any]     = list(ds[list(files.keys()       )[0]].features.keys()       )
       _A					:     Any     = features_name.pop(lowerCamelCase       )
       _A					:     Optional[int]     = list(set(ds[list(files.keys()       )[0]][label_name]       )       )
       _A					:     Tuple     = {label: i for i, label in enumerate(lowerCamelCase       )}
       _A					:     str     = tokenizer.model_input_names
       _A					:     Tuple     = {}

       if len(lowerCamelCase       ) == 1:
              for k in files.keys():
                     _A					:     Optional[Any]     = ds[k].map(
                         lambda lowerCamelCase       : tokenizer.batch_encode_plus(
                             example[features_name[0]]  ,truncation=lowerCamelCase  ,max_length=lowerCamelCase  ,padding='max_length'       )  ,batched=lowerCamelCase  ,)
       elif len(lowerCamelCase       ) == 2:
              for k in files.keys():
                     _A					:     Optional[int]     = ds[k].map(
                         lambda lowerCamelCase       : tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])  ,truncation=lowerCamelCase  ,max_length=lowerCamelCase  ,padding='max_length'  ,)  ,batched=lowerCamelCase  ,)

       def gen_train():
              for ex in transformed_ds[datasets.Split.TRAIN]:
                     _A					:     List[str]     = {k: v for k, v in ex.items() if k in input_names}
                     _A					:     Tuple     = labelaid[ex[label_name]]
                     yield (d, label)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     _A					:     Any     = {k: v for k, v in ex.items() if k in input_names}
                     _A					:     Union[str, Any]     = labelaid[ex[label_name]]
                     yield (d, label)

       def gen_test():
              for ex in transformed_ds[datasets.Split.TEST]:
                     _A					:     Optional[Any]     = {k: v for k, v in ex.items() if k in input_names}
                     _A					:     Any     = labelaid[ex[label_name]]
                     yield (d, label)

       _A					:     Optional[Any]     = (
           tf.data.Dataset.from_generator(
               lowerCamelCase  ,({k: tf.intaa for k in input_names}, tf.intaa)  ,({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,)
           if datasets.Split.TRAIN in transformed_ds
           else None
       )

       if train_ds is not None:
              _A					:     Optional[Any]     = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

       _A					:     Union[str, Any]     = (
           tf.data.Dataset.from_generator(
               lowerCamelCase  ,({k: tf.intaa for k in input_names}, tf.intaa)  ,({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,)
           if datasets.Split.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              _A					:     Optional[int]     = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

       _A					:     Dict     = (
           tf.data.Dataset.from_generator(
               lowerCamelCase  ,({k: tf.intaa for k in input_names}, tf.intaa)  ,({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,)
           if datasets.Split.TEST in transformed_ds
           else None
       )

       if test_ds is not None:
              _A					:     int     = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

       return train_ds, val_ds, test_ds, labelaid


A				:					str    						=			logging.getLogger(__name__)


@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(metadata={"help": "Which column contains the label"}	)
    a													=   field(default=a_    , metadata={"help": "The path of the training file"}	)
    a													=   field(default=a_    , metadata={"help": "The path of the development file"}	)
    a													=   field(default=a_    , metadata={"help": "The path of the test file"}	)
    a													=   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."
            )
        }    , )
    a													=   field(
        default=a_    , metadata={"help": "Overwrite the cached training and evaluation sets"}	)




@dataclass
class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
    a													=   field(
        default=a_    , metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
    a													=   field(
        default=a_    , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
    a													=   field(default=a_    , 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.
    a													=   field(
        default=a_    , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    , )




def 				lowerCAmelCase__	(    ):
       # 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.
       _A					:     List[Any]     = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
       _A					:     Optional[int]     = 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.'       )

       # 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(
           F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, '
           F'16-bits training: {training_args.fpaa}'       )
       logger.info(F'Training/evaluation parameters {training_args}'       )

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

       _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  ,)

       _A					:     Dict     = get_tfds(
           train_file=data_args.train_file  ,eval_file=data_args.dev_file  ,test_file=data_args.test_file  ,tokenizer=lowerCamelCase  ,label_column_id=data_args.label_column_id  ,max_seq_length=data_args.max_seq_length  ,)

       _A					:     List[str]     = AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,num_labels=len(lowerCamelCase       )  ,labelaid=lowerCamelCase  ,idalabel={id: label for label, id in labelaid.items()}  ,finetuning_task='text-classification'  ,cache_dir=model_args.cache_dir  ,)

       with training_args.strategy.scope():
              _A					:     Any     = TFAutoModelForSequenceClassification.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  ,)

       def compute_metrics(lowerCamelCase					:      EvalPrediction       ) -> Dict:
              _A					:     List[str]     = np.argmax(p.predictions  ,axis=1       )

              return {"acc": (preds == p.label_ids).mean()}

       # Initialize our Trainer
       _A					:     str     = TFTrainer(
           model=lowerCamelCase  ,args=lowerCamelCase  ,train_dataset=lowerCamelCase  ,eval_dataset=lowerCamelCase  ,compute_metrics=lowerCamelCase  ,)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              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					:     List[str]     = 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(F'  {key} = {value}'       )
                            writer.write(F'{key} = {value}\n'       )

                     results.update(lowerCamelCase       )

       return results


if __name__ == "__main__":
   main()










'''simple docstring'''







from typing import TYPE_CHECKING

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


A				:					List[str]    						=			{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[str]    						=			[
       '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ElectraForCausalLM''',
       '''ElectraForMaskedLM''',
       '''ElectraForMultipleChoice''',
       '''ElectraForPreTraining''',
       '''ElectraForQuestionAnswering''',
       '''ElectraForSequenceClassification''',
       '''ElectraForTokenClassification''',
       '''ElectraModel''',
       '''ElectraPreTrainedModel''',
       '''load_tf_weights_in_electra''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFElectraForMaskedLM''',
       '''TFElectraForMultipleChoice''',
       '''TFElectraForPreTraining''',
       '''TFElectraForQuestionAnswering''',
       '''TFElectraForSequenceClassification''',
       '''TFElectraForTokenClassification''',
       '''TFElectraModel''',
       '''TFElectraPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''FlaxElectraForCausalLM''',
       '''FlaxElectraForMaskedLM''',
       '''FlaxElectraForMultipleChoice''',
       '''FlaxElectraForPreTraining''',
       '''FlaxElectraForQuestionAnswering''',
       '''FlaxElectraForSequenceClassification''',
       '''FlaxElectraForTokenClassification''',
       '''FlaxElectraModel''',
       '''FlaxElectraPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   A				:					Tuple    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






