"""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 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__ = None      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = {}

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

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

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

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

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

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

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

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

		return train_ds, val_ds, test_ds, labelaid


__lowerCAmelCase     :				Any    				=    logging.getLogger(__name__)

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

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







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__   = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = 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__   = 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__   = 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      ,	)

		lowerCAmelCase__   = 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():
				lowerCAmelCase__   = 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__  ) -> Dict:
				lowerCAmelCase__   = np.argmax(p.predictions      ,	axis=1  )

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

		# Initialize our Trainer
		lowerCAmelCase__   = 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
		lowerCAmelCase__   = {}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""  )
				lowerCAmelCase__   = trainer.evaluate()
				lowerCAmelCase__   = 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()





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




