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      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = None     ,      ):
      SCREAMING_SNAKE_CASE_          =  {}

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

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

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

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

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

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

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

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

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

      return train_ds, val_ds, test_ds, labelaid


A     :  Tuple					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					:
  """simple docstring"""







  lowerCamelCase__     =						field(metadata={'''help''': '''Which column contains the label'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The path of the training file'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The path of the development file'''}					)
  lowerCamelCase__     =						field(default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''The path of the test file'''}					)
  lowerCamelCase__     =						field(
      default=1_2_8  ,      metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      }  ,      )
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)



@dataclass
class   lowerCamelCase					:
  """simple docstring"""







  lowerCamelCase__     =						field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
  lowerCamelCase__     =						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.
  lowerCamelCase__     =						field(
      default=SCREAMING_SNAKE_CASE__  ,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}  ,      )



def      a__ (		):
      # 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.
      SCREAMING_SNAKE_CASE_          =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  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.

      SCREAMING_SNAKE_CASE_          =  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     ,      )

      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_tfds(
          train_file=data_args.train_file     ,      eval_file=data_args.dev_file     ,      test_file=data_args.test_file     ,      tokenizer=__UpperCamelCase     ,      label_column_id=data_args.label_column_id     ,      max_seq_length=data_args.max_seq_length     ,      )

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

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

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

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

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_          =  TFTrainer(
          model=__UpperCamelCase     ,      args=__UpperCamelCase     ,      train_dataset=__UpperCamelCase     ,      eval_dataset=__UpperCamelCase     ,      compute_metrics=__UpperCamelCase     ,      )

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

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

            with open(__UpperCamelCase     ,      "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(__UpperCamelCase				)

      return results


if __name__ == "__main__":
      main()



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

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


