





'''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						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = None    ,	) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      str							      =     {}

			if train_file is not None:
						__snake_case					:      Any							      =     [train_file]
			if eval_file is not None:
						__snake_case					:      Optional[Any]							      =     [eval_file]
			if test_file is not None:
						__snake_case					:      Optional[int]							      =     [test_file]

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

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

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

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

			__snake_case					:      int							      =     (
			    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:
						__snake_case					:      List[str]							      =     train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

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

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

			return train_ds, val_ds, test_ds, labelaid


__UpperCamelCase					    =	logging.getLogger(__name__)




@dataclass
class  _A :
		lowercase__:   int				     =   field(metadata={'''help''': '''Which column contains the label'''}		)
		lowercase__:   str				     =   field(default=__lowercase						,					metadata={'''help''': '''The path of the training file'''}		)
		lowercase__:   Optional[str]				     =   field(default=__lowercase						,					metadata={'''help''': '''The path of the development file'''}		)
		lowercase__:   Optional[str]				     =   field(default=__lowercase						,					metadata={'''help''': '''The path of the test file'''}		)
		lowercase__:   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.'''
		        )
		    }						,					)
		lowercase__:   bool				     =   field(
		    default=__lowercase						,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





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







def 					_a						(       ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      Any							      =     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
			__snake_case      ,     __snake_case      ,     __snake_case					:      Tuple							      =     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					:      Union[str, Any]							      =     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					:      List[str]							      =     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    ,	)

			__snake_case					:      str							      =     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():
						__snake_case					:      int							      =     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:
						__snake_case					:      List[str]							      =     np.argmax(p.predictions    ,	axis=1 )

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

			# Initialize our Trainer
			__snake_case					:      Tuple							      =     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
			__snake_case					:      Dict							      =     {}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***""" )
						__snake_case					:      List[str]							      =     trainer.evaluate()
						__snake_case					:      Tuple							      =     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 					_a						(       _lowerCamelCase ) ->				int:



			"""simple docstring"""


			if not isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
						raise TypeError("""only integers accepted as input""" )
			else:
						__snake_case					:      List[Any]							      =     str(abs(_lowerCamelCase ) )
						__snake_case					:      Union[str, Any]							      =     [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
						for index in range(len(_lowerCamelCase ) ):
									num_transpositions[index].pop(_lowerCamelCase )
						return max(
						    int("""""".join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
	__import__("doctest").testmod()


