



'''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						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase = None			,       )   ->    List[str]:
							UpperCAmelCase  :	Tuple							  =	{}

							if train_file is not None:
														UpperCAmelCase  :	List[Any]							  =	[train_file]
							if eval_file is not None:
														UpperCAmelCase  :	Union[str, Any]							  =	[eval_file]
							if test_file is not None:
														UpperCAmelCase  :	Optional[Any]							  =	[test_file]

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

							if len(UpperCAmelCase__	) == 1:
														for k in files.keys():
																					UpperCAmelCase  :	Tuple							  =	ds[k].map(
																					    lambda _lowercase	: 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  :	Optional[int]							  =	ds[k].map(
																					    lambda _lowercase	: 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  :	List[str]							  =	{k: v for k, v in ex.items() if k in input_names}
																					UpperCAmelCase  :	Tuple							  =	labelaid[ex[label_name]]
																					yield (d, label)

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

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

							UpperCAmelCase  :	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.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														UpperCAmelCase  :	Dict							  =	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

							UpperCAmelCase  :	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.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														UpperCAmelCase  :	Optional[Any]							  =	val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

							UpperCAmelCase  :	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.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														UpperCAmelCase  :	Optional[Any]							  =	test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

							return train_ds, val_ds, test_ds, labelaid


a     :    Tuple      						=					logging.getLogger(__name__)

@dataclass
class     UpperCamelCase_				:
	lowercase    =				field(metadata={'help': 'Which column contains the label'}    )
	lowercase    =				field(default=_a   ,  metadata={'help': 'The path of the training file'}    )
	lowercase    =				field(default=_a   ,  metadata={'help': 'The path of the development file'}    )
	lowercase    =				field(default=_a   ,  metadata={'help': 'The path of the test file'}    )
	lowercase    =				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    =				field(
	    default=_a   ,  metadata={'help': 'Overwrite the cached training and evaluation sets'}    )




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




def    __lowerCamelCase						(      )   ->    List[Any]:
							UpperCAmelCase  :	List[str]							  =	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	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.

							UpperCAmelCase  :	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			,       )

							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  :	List[Any]							  =	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  :	List[Any]							  =	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  :	List[Any]							  =	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(_lowercase	) -> Dict:
														UpperCAmelCase  :	Any							  =	np.argmax(p.predictions			,       axis=1	)

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

							# Initialize our Trainer
							UpperCAmelCase  :	Optional[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
							UpperCAmelCase  :	int							  =	{}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""	)
														UpperCAmelCase  :	Any							  =	trainer.evaluate()
														UpperCAmelCase  :	Tuple							  =	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'''




a     :    Tuple      						=					[sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]



def    __lowerCamelCase						(      _lowercase	)   ->    int:
							UpperCAmelCase  :	str							  =	0
							while number:
														# Increased Speed Slightly by checking every 5 digits together.
														sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
														number //= 1_0_0_0_0_0

							return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
a     :    list[bool | None]      						=					[None] * 1_0_0_0_0_0_0_0
a     :    Optional[Any]      						=					True
a     :    List[Any]      						=					False



def    __lowerCamelCase						(      _lowercase	)   ->    bool:

							if CHAINS[number - 1] is not None:
														return CHAINS[number - 1]  # type: ignore

							UpperCAmelCase  :	List[str]							  =	chain(next_number(_lowercase	)	)
							UpperCAmelCase  :	Tuple							  =	number_chain

							while number < 1_0_0_0_0_0_0_0:
														UpperCAmelCase  :	List[str]							  =	number_chain
														number *= 1_0

							return number_chain



def    __lowerCamelCase						(      _lowercase = 1_0_0_0_0_0_0_0	)   ->    int:
							for i in range(1			,       _lowercase	):
														if CHAINS[i] is None:
																					chain(i + 1	)

							return CHAINS[:number].count(_lowercase	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'''{solution() = }''')

