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					(   a_				: str						,			a_				: str						,			a_				: str						,			a_				: PreTrainedTokenizer						,			a_				: int						,			a_				: Optional[int] = None						,			):
			lowerCamelCase						:List[str]						  =     {}

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

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

			if len(a_) == 1:
						for k in files.keys():
									lowerCamelCase						:int						  =     ds[k].map(
									    lambda a_: tokenizer.batch_encode_plus(
									        example[features_name[0]]						,			truncation=a_						,			max_length=a_						,			padding='''max_length''')						,			batched=a_						,			)
			elif len(a_) == 2:
						for k in files.keys():
									lowerCamelCase						:int						  =     ds[k].map(
									    lambda a_: tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]])						,			truncation=a_						,			max_length=a_						,			padding='''max_length'''						,			)						,			batched=a_						,			)

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

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

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

			lowerCamelCase						:Dict						  =     (
			    tf.data.Dataset.from_generator(
			        a_						,			({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						:List[Any]						  =     train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

			lowerCamelCase						:str						  =     (
			    tf.data.Dataset.from_generator(
			        a_						,			({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(
			        a_						,			({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						:List[str]						  =     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		:
							_UpperCAmelCase						    =     field(metadata={'help': 'Which column contains the label'}				)
							_UpperCAmelCase						    =     field(default=__SCREAMING_SNAKE_CASE      ,   metadata={'help': 'The path of the training file'}				)
							_UpperCAmelCase						    =     field(default=__SCREAMING_SNAKE_CASE      ,   metadata={'help': 'The path of the development file'}				)
							_UpperCAmelCase						    =     field(default=__SCREAMING_SNAKE_CASE      ,   metadata={'help': 'The path of the test file'}				)
							_UpperCAmelCase						    =     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.'
							        )
							    }      ,   )
							_UpperCAmelCase						    =     field(
							    default=__SCREAMING_SNAKE_CASE      ,   metadata={'help': 'Overwrite the cached training and evaluation sets'}				)


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





def  _lowerCamelCase					(   ):
			# 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						:Optional[int]						  =     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
			lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						: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.

			lowerCamelCase						: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						,			)

			lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase  ,    lowerCamelCase						:str						  =     get_tfds(
			    train_file=data_args.train_file						,			eval_file=data_args.dev_file						,			test_file=data_args.test_file						,			tokenizer=a_						,			label_column_id=data_args.label_column_id						,			max_seq_length=data_args.max_seq_length						,			)

			lowerCamelCase						:Tuple						  =     AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,			num_labels=len(a_)						,			labelaid=a_						,			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						:List[str]						  =     TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path						,			from_pt=bool('''.bin''' in model_args.model_name_or_path)						,			config=a_						,			cache_dir=model_args.cache_dir						,			)

			def compute_metrics(a_				: EvalPrediction) -> Dict:
						lowerCamelCase						:List[Any]						  =     np.argmax(p.predictions						,			axis=1)

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

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

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

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

						with open(a_						,			'''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(a_)

			return results


if __name__ == "__main__":
			main()




from maths.prime_factors import prime_factors





def  _lowerCamelCase					(   a_				: int):
			if not isinstance(a_						,			a_):
						lowerCamelCase						:Tuple						  =     F"Input value of [number={number}] must be an integer"
						raise TypeError(a_)
			if number < 1:
						raise ValueError('''Input must be a positive integer''')
			return -1 if len(prime_factors(a_)) % 2 else 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



