


'''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 							__magic_name__(						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


_UpperCAmelCase				: List[Any]							    =				logging.getLogger(__name__)


@dataclass
class       a__				:







				"""simple docstring"""



				__UpperCamelCase    :		int	=	field(metadata={'help': 'Which column contains the label'}      )
				__UpperCamelCase    :		str	=	field(default=__A							,						metadata={'help': 'The path of the training file'}      )
				__UpperCamelCase    :		Optional[str]	=	field(default=__A							,						metadata={'help': 'The path of the development file'}      )
				__UpperCamelCase    :		Optional[str]	=	field(default=__A							,						metadata={'help': 'The path of the test file'}      )
				__UpperCamelCase    :		int	=	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.'
				        )
				    }							,						)
				__UpperCamelCase    :		bool	=	field(
				    default=__A							,						metadata={'help': 'Overwrite the cached training and evaluation sets'}      )


@dataclass
class       a__				:







				"""simple docstring"""



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

def 							__magic_name__(						):
						# 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()





'''simple docstring'''

from __future__ import annotations

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				sorted(numsa + numsa)
						__lowerCAmelCase       ,					__lowerCAmelCase							       =				divmod(len(lowerCamelCase),				2)
						if mod == 1:
												return all_numbers[div]
						else:
												return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_UpperCAmelCase				: Union[str, Any]							    =				[float(x) for x in input("""Enter the elements of first array: """).split()]
	_UpperCAmelCase				: int							    =				[float(x) for x in input("""Enter the elements of second array: """).split()]
	print(f"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")

