



'''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	,				)      ->			Any:
							UpperCAmelCase     :		List[str]							     =		{}

							if train_file is not None:
														UpperCAmelCase     :		Tuple							     =		[train_file]
							if eval_file is not None:
														UpperCAmelCase     :		Any							     =		[eval_file]
							if test_file is not None:
														UpperCAmelCase     :		Any							     =		[test_file]

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

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

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

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

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

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

							UpperCAmelCase     :		List[Any]							     =		(
							    tf.data.Dataset.from_generator(
							        _lowercase	,				({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     :		str							     =		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

							UpperCAmelCase     :		List[str]							     =		(
							    tf.data.Dataset.from_generator(
							        _lowercase	,				({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     :		Dict							     =		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]		)		)		)

							return train_ds, val_ds, test_ds, labelaid


a   :			List[str]					  =    logging.getLogger(__name__)

@dataclass
class 			UpperCamelCase_ :
	lowercase          = field(metadata={'help': 'Which column contains the label'}			)
	lowercase          = field(default=__magic_name__      ,     metadata={'help': 'The path of the training file'}			)
	lowercase          = field(default=__magic_name__      ,     metadata={'help': 'The path of the development file'}			)
	lowercase          = field(default=__magic_name__      ,     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=__magic_name__      ,     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=__magic_name__      ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
	lowercase          = field(
	    default=__magic_name__      ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
	lowercase          = field(default=__magic_name__      ,     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=__magic_name__      ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}      ,     )




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

							UpperCAmelCase     :		List[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	,				)

							UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		int							     =		get_tfds(
							    train_file=data_args.train_file	,				eval_file=data_args.dev_file	,				test_file=data_args.test_file	,				tokenizer=_lowercase	,				label_column_id=data_args.label_column_id	,				max_seq_length=data_args.max_seq_length	,				)

							UpperCAmelCase     :		Dict							     =		AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=len(_lowercase		)	,				labelaid=_lowercase	,				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     :		int							     =		TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path	,				from_pt=bool(""".bin""" in model_args.model_name_or_path		)	,				config=_lowercase	,				cache_dir=model_args.cache_dir	,				)

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

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

							# Initialize our Trainer
							UpperCAmelCase     :		Optional[Any]							     =		TFTrainer(
							    model=_lowercase	,				args=_lowercase	,				train_dataset=_lowercase	,				eval_dataset=_lowercase	,				compute_metrics=_lowercase	,				)

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

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

														with open(_lowercase	,				"""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(_lowercase		)

							return results


if __name__ == "__main__":
							main()






'''simple docstring'''




from PIL import Image



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Image:

							def brightness(_lowercase		) -> float:
														return 1_2_8 + level + (c - 1_2_8)

							if not -255.0 <= level <= 255.0:
														raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""		)
							return img.point(_lowercase		)


if __name__ == "__main__":
							# Load image
							with Image.open("""image_data/lena.jpg""") as img:
														# Change brightness to 100
														a   :			Optional[Any]					  =    change_brightness(img, 1_0_0)
														brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

