



'''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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			)			->      List[Any]:
		_a     :      Dict        =							{}

		if train_file is not None:
				_a     :      str        =							[train_file]
		if eval_file is not None:
				_a     :      Union[str, Any]        =							[eval_file]
		if test_file is not None:
				_a     :      Tuple        =							[test_file]

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

		if len(__UpperCAmelCase  ) == 1:
				for k in files.keys():
						_a     :      Optional[int]        =							ds[k].map(
						    lambda lowerCAmelCase_  : 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():
						_a     :      Dict        =							ds[k].map(
						    lambda lowerCAmelCase_  : 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]:
						_a     :      Any        =							{k: v for k, v in ex.items() if k in input_names}
						_a     :      str        =							labelaid[ex[label_name]]
						yield (d, label)

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

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

		_a     :      Tuple        =							(
		    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:
				_a     :      Optional[Any]        =							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

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

		_a     :      Optional[Any]        =							(
		    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:
				_a     :      Union[str, Any]        =							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 	__magic_name__						:
							lowerCAmelCase       :    int   				=					field(metadata={'help': 'Which column contains the label'}   )
							lowerCAmelCase       :    str   				=					field(default=__lowerCamelCase			,						metadata={'help': 'The path of the training file'}   )
							lowerCAmelCase       :    Optional[str]   				=					field(default=__lowerCamelCase			,						metadata={'help': 'The path of the development file'}   )
							lowerCAmelCase       :    Optional[str]   				=					field(default=__lowerCamelCase			,						metadata={'help': 'The path of the test file'}   )
							lowerCAmelCase       :    int   				=					field(
							    default=1_2_8			,						metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }			,						)
							lowerCAmelCase       :    bool   				=					field(
							    default=__lowerCamelCase			,						metadata={'help': 'Overwrite the cached training and evaluation sets'}   )

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



def      __lowerCamelCase     (      )			->      int:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.
		_a     :      Tuple        =							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
		_a     :      Any        =							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.

		_a     :      List[str]        =							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						,			)

		_a     :      Optional[int]        =							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						,			)

		_a     :      int        =							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():
				_a     :      str        =							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(lowerCAmelCase_  ) -> Dict:
				_a     :      Optional[Any]        =							np.argmax(p.predictions						,			axis=1  )

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

		# Initialize our Trainer
		_a     :      Union[str, Any]        =							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
		_a     :      List[str]        =							{}
		if training_args.do_eval:
				logger.info('*** Evaluate ***'  )
				_a     :      Tuple        =							trainer.evaluate()
				_a     :      Optional[int]        =							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      )      ->	int:
    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''      )

    UpperCamelCase__							:				Optional[Any]          =      0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()

