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

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

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




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
	lowerCAmelCase__   =					field(
	    default=snake_case_     ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
	lowerCAmelCase__   =					field(
	    default=snake_case_     ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
	lowerCAmelCase__   =					field(default=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.
	lowerCAmelCase__   =					field(
	    default=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_         =     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=lowerCamelCase__      ,					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(lowerCamelCase__       )      ,					labelaid=lowerCamelCase__      ,					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=lowerCamelCase__      ,					cache_dir=model_args.cache_dir      ,					)

				def compute_metrics(lowerCamelCase__       ) -> Dict:
								lowerCamelCase_         =     np.argmax(p.predictions      ,					axis=1       )

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

				# Initialize our Trainer
				lowerCamelCase_         =     TFTrainer(
				    model=lowerCamelCase__      ,					args=lowerCamelCase__      ,					train_dataset=lowerCamelCase__      ,					eval_dataset=lowerCamelCase__      ,					compute_metrics=lowerCamelCase__      ,					)

				# 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(lowerCamelCase__      ,					"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(lowerCamelCase__       )

				return results


if __name__ == "__main__":
			main()




from __future__ import annotations

import math





def      lowerCamelCase_					(							lowerCamelCase__       ):

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5      ,					int(math.sqrt(lowerCamelCase__       ) + 1       )      ,					6       ):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True


__A							=[num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]





def      lowerCamelCase_					(							lowerCamelCase__       ):
				if not isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
								raise ValueError("n must be an integer"       )
				if n <= 0:
								raise ValueError("n must be >= 0"       )

				lowerCamelCase_         =     []
				for num in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     0
								while 2 * i * i <= odd_composites[num]:
												lowerCamelCase_         =     odd_composites[num] - 2 * i * i
												if is_prime(lowerCamelCase__       ):
																break
												i += 1
								else:
												list_nums.append(odd_composites[num]       )
												if len(lowerCamelCase__       ) == n:
																return list_nums

				return []





def      lowerCamelCase_					(							):
				return compute_nums(1       )[0]


if __name__ == "__main__":
			print(F"""{solution() = }""")


