
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__							(					a		,		a		,		a		,		a		,		a		,		a = None		,		)      ->   int:
   _A:  Union[str, Any]					=       {}

   if train_file is not None:
      _A:  int					=       [train_file]
   if eval_file is not None:
      _A:  List[str]					=       [eval_file]
   if test_file is not None:
      _A:  List[Any]					=       [test_file]

   _A:  Dict					=       datasets.load_dataset('''csv'''		,		data_files=a      )
   _A:  Union[str, Any]					=       list(ds[list(files.keys()      )[0]].features.keys()      )
   _A:  Union[str, Any]					=       features_name.pop(a      )
   _A:  Optional[Any]					=       list(set(ds[list(files.keys()      )[0]][label_name]      )      )
   _A:  Union[str, Any]					=       {label: i for i, label in enumerate(a      )}
   _A:  Any					=       tokenizer.model_input_names
   _A:  Tuple					=       {}

   if len(a      ) == 1:
      for k in files.keys():
         _A:  List[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():
         _A:  Any					=       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]:
         _A:  str					=       {k: v for k, v in ex.items() if k in input_names}
         _A:  int					=       labelaid[ex[label_name]]
         yield (d, label)

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

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

   _A:  Optional[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:
      _A:  Any					=       train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

   _A:  str					=       (
       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:
      _A:  List[Any]					=       val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

   _A:  Dict					=       (
       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:
      _A:  Optional[int]					=       test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

   return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase__		:     Dict     			=      logging.getLogger(__name__)






@dataclass
class UpperCAmelCase   :





   '''simple docstring'''
   __UpperCamelCase		:    int 				=    field(metadata={'''help''': '''Which column contains the label'''}   )
   __UpperCamelCase		:    str 				=    field(default=SCREAMING_SNAKE_CASE__							,							metadata={'''help''': '''The path of the training file'''}   )
   __UpperCamelCase		:    Optional[str] 				=    field(default=SCREAMING_SNAKE_CASE__							,							metadata={'''help''': '''The path of the development file'''}   )
   __UpperCamelCase		:    Optional[str] 				=    field(default=SCREAMING_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=SCREAMING_SNAKE_CASE__							,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )






@dataclass
class UpperCAmelCase   :





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






def   lowerCamelCase__							(					)      ->   Dict:
   # 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.
   _A:  Any					=       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
   _A			,  _A			,  _A:  Dict					=       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.

   _A:  Optional[int]					=       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		,		)

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

   _A:  List[Any]					=       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():
      _A:  Optional[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:
      _A:  int					=       np.argmax(p.predictions		,		axis=1      )

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

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

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


UpperCAmelCase__		:     Optional[Any]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Dict     			=      {'vocab_file': 'vocab.txt'}

UpperCAmelCase__		:     List[Any]     			=      {
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


UpperCAmelCase__		:     Dict     			=      {
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Any 				=    VOCAB_FILES_NAMES
   __UpperCamelCase		:    Tuple 				=    PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase		:    str 				=    PRETRAINED_INIT_CONFIGURATION
   __UpperCamelCase		:    int 				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase		:    int 				=    ConvBertTokenizer


   def __init__(					self     :							Tuple			,			lowerCAmelCase_     :							int=None			,			lowerCAmelCase_     :							List[Any]=None			,			lowerCAmelCase_     :							Optional[Any]=True			,			lowerCAmelCase_     :							List[Any]="[UNK]"			,			lowerCAmelCase_     :							Optional[Any]="[SEP]"			,			lowerCAmelCase_     :							Optional[Any]="[PAD]"			,			lowerCAmelCase_     :							List[Any]="[CLS]"			,			lowerCAmelCase_     :							Optional[int]="[MASK]"			,			lowerCAmelCase_     :							Any=True			,			lowerCAmelCase_     :							List[str]=None			,			**lowerCAmelCase_     :							List[Any]			,			):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_			,			tokenizer_file=lowerCAmelCase_			,			do_lower_case=lowerCAmelCase_			,			unk_token=lowerCAmelCase_			,			sep_token=lowerCAmelCase_			,			pad_token=lowerCAmelCase_			,			cls_token=lowerCAmelCase_			,			mask_token=lowerCAmelCase_			,			tokenize_chinese_chars=lowerCAmelCase_			,			strip_accents=lowerCAmelCase_			,			**lowerCAmelCase_			,			)

      _A:  List[Any]					=       json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
      if (
          normalizer_state.get('''lowercase'''			,			lowerCAmelCase_     ) != do_lower_case
          or normalizer_state.get('''strip_accents'''			,			lowerCAmelCase_     ) != strip_accents
          or normalizer_state.get('''handle_chinese_chars'''			,			lowerCAmelCase_     ) != tokenize_chinese_chars
      ):
         _A:  List[str]					=       getattr(lowerCAmelCase_			,			normalizer_state.pop('''type'''     )     )
         _A:  List[Any]					=       do_lower_case
         _A:  Optional[Any]					=       strip_accents
         _A:  Union[str, Any]					=       tokenize_chinese_chars
         _A:  Optional[int]					=       normalizer_class(**lowerCAmelCase_     )

      _A:  Optional[Any]					=       do_lower_case


   def  __magic_name__				(					self     :							str			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]=None     ):

      """simple docstring"""
      _A:  Dict					=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

      return output


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							List[int]			,			lowerCAmelCase_     :							Optional[List[int]] = None     ):

      """simple docstring"""
      _A:  Any					=       [self.sep_token_id]
      _A:  int					=       [self.cls_token_id]
      if token_ids_a is None:
         return len(cls + token_ids_a + sep     ) * [0]
      return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							str			,			lowerCAmelCase_     :							Optional[str] = None     ):

      """simple docstring"""
      _A:  str					=       self._tokenizer.model.save(lowerCAmelCase_			,			name=lowerCAmelCase_     )
      return tuple(lowerCAmelCase_     )



