



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					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str] = None      ,)				->			Optional[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=_lowerCAmelCase    )
						UpperCAmelCase_=       list(ds[list(files.keys()    )[0]].features.keys()    )
						UpperCAmelCase_=       features_name.pop(_lowerCAmelCase    )
						UpperCAmelCase_=       list(set(ds[list(files.keys()    )[0]][label_name]    )    )
						UpperCAmelCase_=       {label: i for i, label in enumerate(_lowerCAmelCase    )}
						UpperCAmelCase_=       tokenizer.model_input_names
						UpperCAmelCase_=       {}

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

						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(
						        _lowerCAmelCase      ,({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(
						        _lowerCAmelCase      ,({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(
						        _lowerCAmelCase      ,({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


__A       =			logging.getLogger(__name__)






@dataclass
class 		lowercase						:






	"""simple docstring"""


	a__				: int		       =							field(metadata={"help": "Which column contains the label"})
	a__				: Tuple		       =							field(default=_a			,   metadata={"help": "The path of the training file"})
	a__				: Any		       =							field(default=_a			,   metadata={"help": "The path of the development file"})
	a__				: List[str]		       =							field(default=_a			,   metadata={"help": "The path of the test file"})
	a__				: List[str]		       =							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."
	        )
	    }			,   )
	a__				: int		       =							field(
	    default=_a			,   metadata={"help": "Overwrite the cached training and evaluation sets"})






@dataclass
class 		lowercase						:






	"""simple docstring"""


	a__				: Dict		       =							field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
	a__				: Tuple		       =							field(
	    default=_a			,   metadata={"help": "Pretrained config name or path if not the same as model_name"})
	a__				: List[str]		       =							field(
	    default=_a			,   metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
	a__				: Any		       =							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.
	a__				: int		       =							field(
	    default=_a			,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,   )



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






						'''simple docstring'''



						UpperCAmelCase_=       HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
						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_=       get_tfds(
						    train_file=data_args.train_file      ,eval_file=data_args.dev_file      ,test_file=data_args.test_file      ,tokenizer=_lowerCAmelCase      ,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(_lowerCAmelCase    )      ,labelaid=_lowerCAmelCase      ,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=_lowerCAmelCase      ,cache_dir=model_args.cache_dir      ,)

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

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

						# Initialize our Trainer
						UpperCAmelCase_=       TFTrainer(
						    model=_lowerCAmelCase      ,args=_lowerCAmelCase      ,train_dataset=_lowerCAmelCase      ,eval_dataset=_lowerCAmelCase      ,compute_metrics=_lowerCAmelCase      ,)

						# 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(_lowerCAmelCase      ,"""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(_lowerCAmelCase    )

						return results


if __name__ == "__main__":
	main()









import warnings
from functools import wraps
from typing import Callable



def 	__a					(   lowerCAmelCase_       : Callable    )				->			Callable:






						'''simple docstring'''



						@wraps(lowerCAmelCase_    )
						def _inner_fn(*lowerCAmelCase_       : List[Any]      ,**lowerCAmelCase_       : Tuple    ):
												warnings.warn(
												    (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")      ,lowerCAmelCase_      ,)
												return fn(*lowerCAmelCase_      ,**lowerCAmelCase_    )

						return _inner_fn




