


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__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__ = None      ,	):
	__lowerCamelCase		:							int		 =						{}

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

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

	if len(__snake_case						) == 1:
		for k in files.keys():
			__lowerCamelCase		:							List[Any]		 =						ds[k].map(
			    lambda SCREAMING_SNAKE_CASE__						: tokenizer.batch_encode_plus(
			        example[features_name[0]]      ,	truncation=__snake_case      ,	max_length=__snake_case      ,	padding='max_length'						)      ,	batched=__snake_case      ,	)
	elif len(__snake_case						) == 2:
		for k in files.keys():
			__lowerCamelCase		:							Dict		 =						ds[k].map(
			    lambda SCREAMING_SNAKE_CASE__						: tokenizer.batch_encode_plus(
			        (example[features_name[0]], example[features_name[1]])      ,	truncation=__snake_case      ,	max_length=__snake_case      ,	padding='max_length'      ,	)      ,	batched=__snake_case      ,	)

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

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

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

	__lowerCamelCase		:							str		 =						(
	    tf.data.Dataset.from_generator(
	        __snake_case      ,	({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		:							Optional[Any]		 =						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

	__lowerCamelCase		:							int		 =						(
	    tf.data.Dataset.from_generator(
	        __snake_case      ,	({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		:							Optional[int]		 =						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

	__lowerCamelCase		:							Tuple		 =						(
	    tf.data.Dataset.from_generator(
	        __snake_case      ,	({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		:							Tuple		 =						test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]						)						)						)

	return train_ds, val_ds, test_ds, labelaid


lowercase_	   =     logging.getLogger(__name__)


@dataclass
class 	A_ :




						'''simple docstring'''


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



@dataclass
class 	A_ :




						'''simple docstring'''


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



def 				UpperCamelCase__					(  ):
	# 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.
	__lowerCamelCase		:							List[Any]		 =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
	__lowerCamelCase		:							Dict		 =						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		:							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      ,	)

	__lowerCamelCase		:							Union[str, Any]		 =						get_tfds(
	    train_file=data_args.train_file      ,	eval_file=data_args.dev_file      ,	test_file=data_args.test_file      ,	tokenizer=__snake_case      ,	label_column_id=data_args.label_column_id      ,	max_seq_length=data_args.max_seq_length      ,	)

	__lowerCamelCase		:							Optional[int]		 =						AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,	num_labels=len(__snake_case						)      ,	labelaid=__snake_case      ,	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		:							Optional[int]		 =						TFAutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path      ,	from_pt=bool('.bin' in model_args.model_name_or_path						)      ,	config=__snake_case      ,	cache_dir=model_args.cache_dir      ,	)

	def compute_metrics(SCREAMING_SNAKE_CASE__						) -> Dict:
		__lowerCamelCase		:							str		 =						np.argmax(p.predictions      ,	axis=1						)

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

	# Initialize our Trainer
	__lowerCamelCase		:							str		 =						TFTrainer(
	    model=__snake_case      ,	args=__snake_case      ,	train_dataset=__snake_case      ,	eval_dataset=__snake_case      ,	compute_metrics=__snake_case      ,	)

	# Training
	if training_args.do_train:
		trainer.train()
		trainer.save_model()
		tokenizer.save_pretrained(training_args.output_dir						)

	# Evaluation
	__lowerCamelCase		:							str		 =						{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'						)
		__lowerCamelCase		:							int		 =						trainer.evaluate()
		__lowerCamelCase		:							List[str]		 =						os.path.join(training_args.output_dir      ,	'eval_results.txt'						)

		with open(__snake_case      ,	'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(__snake_case						)

	return results


if __name__ == "__main__":
							main()





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						prime_factors(SCREAMING_SNAKE_CASE__						)
 if is_square_free(SCREAMING_SNAKE_CASE__						):
  return -1 if len(SCREAMING_SNAKE_CASE__						) % 2 else 1
 return 0


if __name__ == "__main__":
       import doctest

       doctest.testmod()

