




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 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      PreTrainedTokenizer ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[int] = None ,     )     ->       Union[str, Any]:





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

				if len(UpperCamelCase__      ) == 1:
								for k in files.keys():
												__lowerCamelCase		       =    ds[k].map(
												    lambda UpperCamelCase__      : 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():
												__lowerCamelCase		       =    ds[k].map(
												    lambda UpperCamelCase__      : 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]:
												__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(
				        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:
								__lowerCamelCase		       =    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

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

				__lowerCamelCase		       =    (
				    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:
								__lowerCamelCase		       =    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 :

			"""simple docstring"""







			snake_case_   		=       field(metadata={'''help''': '''Which column contains the label'''} )
			snake_case_   		=       field(default=__magic_name__			,				metadata={'''help''': '''The path of the training file'''} )
			snake_case_   		=       field(default=__magic_name__			,				metadata={'''help''': '''The path of the development file'''} )
			snake_case_   		=       field(default=__magic_name__			,				metadata={'''help''': '''The path of the test file'''} )
			snake_case_   		=       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.'''
			        )
			    }			,				)
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







@dataclass
class     __lowerCAmelCase :

			"""simple docstring"""







			snake_case_   		=       field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
			snake_case_   		=       field(default=__magic_name__			,				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.
			snake_case_   		=       field(
			    default=__magic_name__			,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,				)







def 			lowerCamelCase_		(       )     ->       int:





				"""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=UpperCamelCase__ ,     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(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():
								__lowerCamelCase		       =    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(UpperCamelCase__				:      EvalPrediction      ) -> Dict:
								__lowerCamelCase		       =    np.argmax(p.predictions ,     axis=1      )

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

				# Initialize our Trainer
				__lowerCamelCase		       =    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
				__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(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()








def 			lowerCamelCase_		(       UpperCamelCase__				:      int      )     ->       bool:





				"""simple docstring"""


				return number & 1 == 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()


