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__  (					__A   , __A   , __A   , __A   , __A   , __A = None   , ):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}

    if train_file is not None:
        _lowerCamelCase     :		Union[str, Any] 						=						[train_file]
    if eval_file is not None:
        _lowerCamelCase     :		Dict 						=						[eval_file]
    if test_file is not None:
        _lowerCamelCase     :		Dict 						=						[test_file]

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

    if len(__A	) == 1:
        for k in files.keys():
            _lowerCamelCase     :		Optional[Any] 						=						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():
            _lowerCamelCase     :		Tuple 						=						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]:
            _lowerCamelCase     :		Optional[int] 						=						{k: v for k, v in ex.items() if k in input_names}
            _lowerCamelCase     :		Dict 						=						labelaid[ex[label_name]]
            yield (d, label)

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

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

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

    _lowerCamelCase     :		int 						=						(
        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:
        _lowerCamelCase     :		List[Any] 						=						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase			:							List[str]     =logging.getLogger(__name__)




@dataclass
class 	__snake_case	:





      '''simple docstring'''






      _snake_case    		=       field(metadata={'help': 'Which column contains the label'}		)
      _snake_case    		=       field(default=__lowerCAmelCase ,  metadata={'help': 'The path of the training file'}		)
      _snake_case    		=       field(default=__lowerCAmelCase ,  metadata={'help': 'The path of the development file'}		)
      _snake_case    		=       field(default=__lowerCAmelCase ,  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=__lowerCAmelCase ,  metadata={'help': 'Overwrite the cached training and evaluation sets'}		)





@dataclass
class 	__snake_case	:





      '''simple docstring'''






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







def 						A__  (					):







    '''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.
    _lowerCamelCase     :		Tuple 						=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		str 						=						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     :		Optional[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     :		Optional[int] 						=						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   , )

    _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(__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():
        _lowerCamelCase     :		int 						=						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:
        _lowerCamelCase     :		str 						=						np.argmax(p.predictions   , axis=1	)

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

    # Initialize our Trainer
    _lowerCamelCase     :		Any 						=						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
    _lowerCamelCase     :		Any 						=						{}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""	)
        _lowerCamelCase     :		List[str] 						=						trainer.evaluate()
        _lowerCamelCase     :		int 						=						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 json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as f:
              f.write(json.dumps(self.vocab    ,  indent=2    ,  sort_keys=_UpperCamelCase    ,  ensure_ascii=_UpperCamelCase) + """\n""")

          return (vocab_file,)




