





"""simple docstring"""





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__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = None  ,	):




		"""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=__UpperCAmelCase    )
		UpperCamelCase   =	list(ds[list(files.keys()    )[0]].features.keys()    )
		UpperCamelCase   =	features_name.pop(__UpperCAmelCase    )
		UpperCamelCase   =	list(set(ds[list(files.keys()    )[0]][label_name]    )    )
		UpperCamelCase   =	{label: i for i, label in enumerate(__UpperCAmelCase    )}
		UpperCamelCase   =	tokenizer.model_input_names
		UpperCamelCase   =	{}

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

		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(
		        __UpperCAmelCase  ,	({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(
		        __UpperCAmelCase  ,	({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(
		        __UpperCAmelCase  ,	({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


lowerCAmelCase__     			=			logging.getLogger(__name__)





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





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



def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
		UpperCamelCase   ,				UpperCamelCase   ,				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   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	get_tfds(
		    train_file=data_args.train_file  ,	eval_file=data_args.dev_file  ,	test_file=data_args.test_file  ,	tokenizer=__UpperCAmelCase  ,	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(__UpperCAmelCase    )  ,	labelaid=__UpperCAmelCase  ,	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=__UpperCAmelCase  ,	cache_dir=model_args.cache_dir  ,	)

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

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

		# Initialize our Trainer
		UpperCamelCase   =	TFTrainer(
		    model=__UpperCAmelCase  ,	args=__UpperCAmelCase  ,	train_dataset=__UpperCAmelCase  ,	eval_dataset=__UpperCAmelCase  ,	compute_metrics=__UpperCAmelCase  ,	)

		# 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(__UpperCAmelCase  ,	"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(__UpperCAmelCase    )

		return results


if __name__ == "__main__":
						main()













"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime





@require_onnxruntime
class        _lowerCamelCase      :
						pass






