






"""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     _UpperCamelCase      (	A     ,							A     ,							A     ,							A     ,							A     ,							A = None     ,							):
   UpperCamelCase_			     ={}

   if train_file is not None:
      UpperCamelCase_			     =[train_file]
   if eval_file is not None:
      UpperCamelCase_			     =[eval_file]
   if test_file is not None:
      UpperCamelCase_			     =[test_file]

   UpperCamelCase_			     =datasets.load_dataset("csv"     ,							data_files=A      )
   UpperCamelCase_			     =list(ds[list(files.keys()      )[0]].features.keys()      )
   UpperCamelCase_			     =features_name.pop(A      )
   UpperCamelCase_			     =list(set(ds[list(files.keys()      )[0]][label_name]      )      )
   UpperCamelCase_			     ={label: i for i, label in enumerate(A      )}
   UpperCamelCase_			     =tokenizer.model_input_names
   UpperCamelCase_			     ={}

   if len(A      ) == 1:
      for k in files.keys():
         UpperCamelCase_			     =ds[k].map(
             lambda A      : tokenizer.batch_encode_plus(
                 example[features_name[0]]     ,							truncation=A     ,							max_length=A     ,							padding="max_length"      )     ,							batched=A     ,							)
   elif len(A      ) == 2:
      for k in files.keys():
         UpperCamelCase_			     =ds[k].map(
             lambda A      : tokenizer.batch_encode_plus(
                 (example[features_name[0]], example[features_name[1]])     ,							truncation=A     ,							max_length=A     ,							padding="max_length"     ,							)     ,							batched=A     ,							)

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

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

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

   UpperCamelCase_			     =(
       tf.data.Dataset.from_generator(
           A     ,							({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:
      UpperCamelCase_			     =train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

   UpperCamelCase_			     =(
       tf.data.Dataset.from_generator(
           A     ,							({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:
      UpperCamelCase_			     =val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

   UpperCamelCase_			     =(
       tf.data.Dataset.from_generator(
           A     ,							({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:
      UpperCamelCase_			     =test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

   return train_ds, val_ds, test_ds, labelaid


A_      	=     logging.getLogger(__name__)
@dataclass
class     __lowerCAmelCase :







  '''simple docstring'''


  __lowerCamelCase							:     List[Any]  						=      field(metadata={"help": "Which column contains the label"}							)
  __lowerCamelCase							:     Any  						=      field(default=UpperCAmelCase__							, metadata={"help": "The path of the training file"}							)
  __lowerCamelCase							:     Any  						=      field(default=UpperCAmelCase__							, metadata={"help": "The path of the development file"}							)
  __lowerCamelCase							:     Union[str, Any]  						=      field(default=UpperCAmelCase__							, metadata={"help": "The path of the test file"}							)
  __lowerCamelCase							:     Optional[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."
          )
      }							, )
  __lowerCamelCase							:     Any  						=      field(
      default=UpperCAmelCase__							, metadata={"help": "Overwrite the cached training and evaluation sets"}							)




@dataclass
class     __lowerCAmelCase :







  '''simple docstring'''


  __lowerCamelCase							:     Tuple  						=      field(
      metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
  __lowerCamelCase							:     Any  						=      field(
      default=UpperCAmelCase__							, metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
  __lowerCamelCase							:     Tuple  						=      field(
      default=UpperCAmelCase__							, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
  __lowerCamelCase							:     Optional[Any]  						=      field(default=UpperCAmelCase__							, 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.
  __lowerCamelCase							:     Any  						=      field(
      default=UpperCAmelCase__							, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							, )




def     _UpperCamelCase      (	):
   UpperCamelCase_			     =HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
   UpperCamelCase_     ,				UpperCamelCase_     ,				UpperCamelCase_			     =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.

   UpperCamelCase_			     =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     ,							)

   UpperCamelCase_     ,				UpperCamelCase_     ,				UpperCamelCase_     ,				UpperCamelCase_			     =get_tfds(
       train_file=data_args.train_file     ,							eval_file=data_args.dev_file     ,							test_file=data_args.test_file     ,							tokenizer=A     ,							label_column_id=data_args.label_column_id     ,							max_seq_length=data_args.max_seq_length     ,							)

   UpperCamelCase_			     =AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,							num_labels=len(A      )     ,							labelaid=A     ,							idalabel={id: label for label, id in labelaid.items()}     ,							finetuning_task="text-classification"     ,							cache_dir=model_args.cache_dir     ,							)

   with training_args.strategy.scope():
      UpperCamelCase_			     =TFAutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path     ,							from_pt=bool(".bin" in model_args.model_name_or_path      )     ,							config=A     ,							cache_dir=model_args.cache_dir     ,							)

   def compute_metrics(A      ) -> Dict:
      UpperCamelCase_			     =np.argmax(p.predictions     ,							axis=1      )

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

   # Initialize our Trainer
   UpperCamelCase_			     =TFTrainer(
       model=A     ,							args=A     ,							train_dataset=A     ,							eval_dataset=A     ,							compute_metrics=A     ,							)

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

   # Evaluation
   UpperCamelCase_			     ={}
   if training_args.do_eval:
      logger.info("*** Evaluate ***"      )
      UpperCamelCase_			     =trainer.evaluate()
      UpperCamelCase_			     =os.path.join(training_args.output_dir     ,							"eval_results.txt"      )

      with open(A     ,							"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(A      )

   return results


if __name__ == "__main__":
    main()




import os
import sys

A			:						Tuple        =  os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A			:						Dict        =  [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoTokenizer.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModel.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Tuple	)  ->    List[str]:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Optional[Any]       ,       **__magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__       ,       **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Dict       ,       **__magic_name__	:				List[Any]	)  ->    int:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__       ,       **__magic_name__	)

