
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 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__				,	A__ = None				,	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      {}

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

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

    if len(A__	) == 1:
        for k in files.keys():
            snake_case				  =      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():
            snake_case				  =      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]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

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

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

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

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

    snake_case				  =      (
        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:
        snake_case				  =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


_A						      =				logging.getLogger(__name__)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							int     =	field(metadata={"help": "Which column contains the label"}						)
     UpperCAmelCase__       :							str     =	field(default=A_							,				metadata={"help": "The path of the training file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the development file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the test file"}						)
     UpperCAmelCase__       :							int     =	field(
         default=1_28							,				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=A_							,				metadata={"help": "Overwrite the cached training and evaluation sets"}						)







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



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    snake_case  ,							snake_case  ,							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.

    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				,	)

    snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      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				,	)

    snake_case				  =      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():
        snake_case				  =      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:
        snake_case				  =      np.argmax(p.predictions				,	axis=1	)

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

    # Initialize our Trainer
    snake_case				  =      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
    snake_case				  =      {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"	)
        snake_case				  =      trainer.evaluate()
        snake_case				  =      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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_A						      =				"▁"

_A						      =				{"vocab_file": "spiece.model"}

_A						      =				{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_A						      =				{
    "google/pegasus-xsum": 5_12,
}


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	VOCAB_FILES_NAMES

     UpperCAmelCase__       :							Tuple     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							List[str]     =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							Optional[int]     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							Optional[Any]     =	["input_ids", "attention_mask"]





     def __init__(self   :					Optional[Any]       ,					_A   :					Any       ,					_A   :					List[Any]="<pad>"       ,					_A   :					int="</s>"       ,					_A   :					Dict="<unk>"       ,					_A   :					str="<mask_2>"       ,					_A   :					Optional[int]="<mask_1>"       ,					_A   :					Optional[Any]=None       ,					_A   :					Tuple=1_0_3       ,					_A   :					Optional[Dict[str, Any]] = None       ,					**_A   :					List[str]       ,					)		->		None:
         snake_case				  =      offset
         if additional_special_tokens is not None:
             if not isinstance(_A       ,					_A     ):
                 raise TypeError(
                     f'additional_special_tokens should be of type {type(_A     )}, but is'
                     f' {type(_A     )}'     )

             snake_case				  =      (
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 f'<unk_{i}>' for i in range(len(_A     )       ,					self.offset - 1     )
             ]

             if len(set(_A     )     ) != len(_A     ):
                 raise ValueError(
                     "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                     f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'     )
             snake_case				  =      additional_special_tokens_extended
         else:
             snake_case				  =      [mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [f'<unk_{i}>' for i in range(2       ,					self.offset     )]

         snake_case				  =      {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=_A       ,					unk_token=_A       ,					mask_token=_A       ,					pad_token=_A       ,					mask_token_sent=_A       ,					offset=_A       ,					additional_special_tokens=_A       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**_A       ,					)
         snake_case				  =      mask_token_sent
         snake_case				  =      vocab_file
         snake_case				  =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(_A     )

         # add special tokens to encoder dict
         snake_case				  =      {
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }     )

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,					self.offset - 1     )}     )

         snake_case				  =      {v: k for k, v in self.encoder.items()}





     @property
     def 	UpperCAmelCase(self   :					str     )		->		int:
         return len(self.sp_model     ) + self.offset





     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict[str, int]:
         snake_case				  =      {self.convert_ids_to_tokens(_A     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab





     def __getstate__(self   :					Any     )		->		List[Any]:
         snake_case				  =      self.__dict__.copy()
         snake_case				  =      None
         return state





     def __setstate__(self   :					str       ,					_A   :					Union[str, Any]     )		->		Tuple:
         snake_case				  =      d

         # for backward compatibility
         if not hasattr(self       ,					"sp_model_kwargs"     ):
             snake_case				  =      {}

         snake_case				  =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(self.vocab_file     )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					str     )		->		List[str]:
         return self.sp_model.encode(_A       ,					out_type=_A     )





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					str     )		->		int:
         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         snake_case				  =      self.sp_model.piece_to_id(_A     )
         return sp_id + self.offset





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					int     )		->		str:
         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             snake_case				  =      self.sp_model.IdToPiece(index - self.offset     )
         return token





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Tuple     )		->		Tuple:
         snake_case				  =      []
         snake_case				  =      ""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(_A     ) + token
                 snake_case				  =      []
             else:
                 current_sub_tokens.append(_A     )
         out_string += self.sp_model.decode(_A     )
         return out_string.strip()





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Tuple=False     )		->		Tuple:
         return 1





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[int]     )		->		Tuple:
         snake_case				  =      set(self.all_special_ids     )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]





     def 	UpperCAmelCase(self   :					str       ,					_A   :					List       ,					_A   :					Optional[List] = None       ,					_A   :					bool = False     )		->		List[int]:
         if already_has_special_tokens:
             return self._special_token_mask(_A     )
         elif token_ids_a is None:
             return self._special_token_mask(_A     ) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]





     def 	UpperCAmelCase(self   :					int       ,					_A   :					Dict       ,					_A   :					List[Any]=None     )		->		List[int]:
         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					str       ,					_A   :					Optional[str] = None     )		->		Tuple[str]:
         if not os.path.isdir(_A     ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
             return
         snake_case				  =      os.path.join(
             _A       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

         if os.path.abspath(self.vocab_file     ) != os.path.abspath(_A     ) and os.path.isfile(self.vocab_file     ):
             copyfile(self.vocab_file       ,					_A     )
         elif not os.path.isfile(self.vocab_file     ):
             with open(_A       ,					"wb"     ) as fi:
                 snake_case				  =      self.sp_model.serialized_model_proto()
                 fi.write(_A     )

         return (out_vocab_file,)

