






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   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case = None       ,			):
     __lowerCAmelCase             =       {}

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

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

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

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

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

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

     __lowerCAmelCase             =       (
         tf.data.Dataset.from_generator(
             __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:
          __lowerCAmelCase             =       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

     __lowerCAmelCase             =       (
         tf.data.Dataset.from_generator(
             __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:
          __lowerCAmelCase             =       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

     __lowerCAmelCase             =       (
         tf.data.Dataset.from_generator(
             __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:
          __lowerCAmelCase             =       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

     return train_ds, val_ds, test_ds, labelaid


lowerCamelCase      :       Tuple    =						logging.getLogger(__name__)



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



@dataclass
class 				_UpperCamelCase      :
      snake_case_	=				field(
          metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      )
      snake_case_	=				field(
          default=a_				,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
      snake_case_	=				field(
          default=a_				,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
      snake_case_	=				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.
      snake_case_	=				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.
     __lowerCAmelCase             =       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
     __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       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             =       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             =       get_tfds(
         train_file=data_args.train_file       ,			eval_file=data_args.dev_file       ,			test_file=data_args.test_file       ,			tokenizer=__snake_case       ,			label_column_id=data_args.label_column_id       ,			max_seq_length=data_args.max_seq_length       ,			)

     __lowerCAmelCase             =       AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,			num_labels=len(__snake_case							)       ,			labelaid=__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():
          __lowerCAmelCase             =       TFAutoModelForSequenceClassification.from_pretrained(
              model_args.model_name_or_path       ,			from_pt=bool(".bin" in model_args.model_name_or_path							)       ,			config=__snake_case       ,			cache_dir=model_args.cache_dir       ,			)

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

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

     # Initialize our Trainer
     __lowerCAmelCase             =       TFTrainer(
         model=__snake_case       ,			args=__snake_case       ,			train_dataset=__snake_case       ,			eval_dataset=__snake_case       ,			compute_metrics=__snake_case       ,			)

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

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

          with open(__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(__snake_case							)

     return results


if __name__ == "__main__":
 main()














lowerCamelCase      :       List[Any]    =						{
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
lowerCamelCase      :       Union[str, Any]    =						{
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       from_type.lower().strip("s"							)
     __lowerCAmelCase             =       to_type.lower().strip("s"							)

     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)
     __lowerCAmelCase             =       UNIT_SYMBOL.get(__snake_case       ,			__snake_case							)

     if from_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'from_type' value: {from_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     if to_sanitized not in METRIC_CONVERSION:
          __lowerCAmelCase             =       (
              F"""Invalid 'to_type' value: {to_type!r}.\n"""
              F"""Conversion abbreviations are: {", ".join(__snake_case							)}"""
          )
          raise ValueError(__snake_case							)
     __lowerCAmelCase             =       METRIC_CONVERSION[from_sanitized]
     __lowerCAmelCase             =       METRIC_CONVERSION[to_sanitized]
     __lowerCAmelCase             =       1

     if from_exponent > to_exponent:
          __lowerCAmelCase             =       from_exponent - to_exponent
     else:
          __lowerCAmelCase             =       -(to_exponent - from_exponent)

     return value * pow(10       ,			__snake_case							)


if __name__ == "__main__":
 from doctest import testmod

 testmod()






