





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					(      lowercase__						:							Dict		,     lowercase__						:							Dict		,     lowercase__						:							List[Any]		,     lowercase__						:							List[Any]		,     lowercase__						:							int		,     lowercase__						:							Optional[Any] = None		,     )						->   int:
    '''simple docstring'''





    lowerCAmelCase_       :						str							= {}

    if train_file is not None:
        lowerCAmelCase_       :						Tuple							= [train_file]
    if eval_file is not None:
        lowerCAmelCase_       :						Any							= [eval_file]
    if test_file is not None:
        lowerCAmelCase_       :						List[Any]							= [test_file]

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

    if len(lowerCAmelCase_		) == 1:
        for k in files.keys():
            lowerCAmelCase_       :						Any							= ds[k].map(
                lambda lowercase__		: 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():
            lowerCAmelCase_       :						int							= ds[k].map(
                lambda lowercase__		: 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]:
            lowerCAmelCase_       :						str							= {k: v for k, v in ex.items() if k in input_names}
            lowerCAmelCase_       :						Optional[int]							= labelaid[ex[label_name]]
            yield (d, label)

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

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

    lowerCAmelCase_       :						List[str]							= (
        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:
        lowerCAmelCase_       :						Union[str, Any]							= train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

    lowerCAmelCase_       :						Tuple							= (
        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:
        lowerCAmelCase_       :						Optional[int]							= val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

    lowerCAmelCase_       :						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.TEST in transformed_ds
        else None
    )

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

    return train_ds, val_ds, test_ds, labelaid


__UpperCAmelCase							=   logging.getLogger(__name__)

@dataclass
class 		__a				:
       __snake_case    : int   =		field(metadata={"""help""": """Which column contains the label"""}   )
       __snake_case    : str   =		field(default=__SCREAMING_SNAKE_CASE      ,metadata={"""help""": """The path of the training file"""}   )
       __snake_case    : Optional[str]   =		field(default=__SCREAMING_SNAKE_CASE      ,metadata={"""help""": """The path of the development file"""}   )
       __snake_case    : Optional[str]   =		field(default=__SCREAMING_SNAKE_CASE      ,metadata={"""help""": """The path of the test file"""}   )
       __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=__SCREAMING_SNAKE_CASE      ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )






@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=__SCREAMING_SNAKE_CASE      ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
       __snake_case    : Optional[str]   =		field(
           default=__SCREAMING_SNAKE_CASE      ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
       __snake_case    : bool   =		field(default=__SCREAMING_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.
       __snake_case    : Optional[str]   =		field(
           default=__SCREAMING_SNAKE_CASE      ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,)






def       __UpperCamelCase					(      )						->   List[Any]:
    '''simple docstring'''





    lowerCAmelCase_       :						Tuple							= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
    lowerCAmelCase_		,		lowerCAmelCase_		,		lowerCAmelCase_       :						Optional[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."""		)

    # 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.

    lowerCAmelCase_       :						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		,     )

    lowerCAmelCase_		,		lowerCAmelCase_		,		lowerCAmelCase_		,		lowerCAmelCase_       :						Union[str, Any]							= 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		,     )

    lowerCAmelCase_       :						Optional[int]							= 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():
        lowerCAmelCase_       :						Dict							= 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(lowercase__						:							Optional[int]		) -> Dict:
        lowerCAmelCase_       :						int							= np.argmax(p.predictions		,     axis=1		)

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

    # Initialize our Trainer
    lowerCAmelCase_       :						Optional[Any]							= 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
    lowerCAmelCase_       :						Dict							= {}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""		)
        lowerCAmelCase_       :						Any							= trainer.evaluate()
        lowerCAmelCase_       :						Dict							= 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()









from typing import TYPE_CHECKING

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


_A					:				Any				=			{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Optional[Any]				=			["""XGLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Any				=			["""XGLMTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				str				=			[
      """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XGLMForCausalLM""",
      """XGLMModel""",
      """XGLMPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Tuple				=			[
      """FlaxXGLMForCausalLM""",
      """FlaxXGLMModel""",
      """FlaxXGLMPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				List[Any]				=			[
      """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXGLMForCausalLM""",
      """TFXGLMModel""",
      """TFXGLMPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm import XGLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xglm_fast import XGLMTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
        TFXGLMPreTrainedModel,
    )


else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)
