






"""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__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_ = None		,      )->							Union[str, Any]:







       """simple docstring"""
       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=_SCREAMING_SNAKE_CASE				)
       UpperCamelCase					 =		list(ds[list(files.keys()				)[0]].features.keys()				)
       UpperCamelCase					 =		features_name.pop(_SCREAMING_SNAKE_CASE				)
       UpperCamelCase					 =		list(set(ds[list(files.keys()				)[0]][label_name]				)				)
       UpperCamelCase					 =		{label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE				)}
       UpperCamelCase					 =		tokenizer.model_input_names
       UpperCamelCase					 =		{}

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

       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(
               _SCREAMING_SNAKE_CASE		,      ({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(
               _SCREAMING_SNAKE_CASE		,      ({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(
               _SCREAMING_SNAKE_CASE		,      ({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


SCREAMING_SNAKE_CASE      =     logging.getLogger(__name__)



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





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





def 	lowerCamelCase__			(							)->							Union[str, Any]:







       """simple docstring"""
       # 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.
       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=_SCREAMING_SNAKE_CASE		,      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(_SCREAMING_SNAKE_CASE				)		,      labelaid=_SCREAMING_SNAKE_CASE		,      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=_SCREAMING_SNAKE_CASE		,      cache_dir=model_args.cache_dir		,      )

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

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

       # Initialize our Trainer
       UpperCamelCase					 =		TFTrainer(
           model=_SCREAMING_SNAKE_CASE		,      args=_SCREAMING_SNAKE_CASE		,      train_dataset=_SCREAMING_SNAKE_CASE		,      eval_dataset=_SCREAMING_SNAKE_CASE		,      compute_metrics=_SCREAMING_SNAKE_CASE		,      )

       # 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(_SCREAMING_SNAKE_CASE		,      "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(_SCREAMING_SNAKE_CASE				)

       return results


if __name__ == "__main__":
      main()







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE      =     {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GraphormerForGraphClassification""",
          """GraphormerModel""",
          """GraphormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_graphormer import (
                GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                GraphormerForGraphClassification,
                GraphormerModel,
                GraphormerPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





