



'''simple docstring'''




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a   :			Dict					  =    logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

a   :			Union[str, Any]					  =    {
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a   :			Union[str, Any]					  =    {
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a   :			int					  =    sorted(arg_to_scheduler.keys())
a   :			str					  =    """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class 			UpperCamelCase_ (							pl.LightningModule			):





	def __init__(  self		,		A		,		A=None		,		A="base"		,		A=None		,		A=None		,		A=None		,		**A		,		)			->			Any:
								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(A    )
								UpperCAmelCase     :		Tuple							     =		0
								UpperCAmelCase     :		Optional[Any]							     =		Path(self.hparams.output_dir    )
								UpperCAmelCase     :		int							     =		self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
															UpperCAmelCase     :		Optional[int]							     =		AutoConfig.from_pretrained(
															    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,		**({"""num_labels""": num_labels} if num_labels is not None else {})		,		cache_dir=A		,		**A		,		)
								else:
															UpperCAmelCase     :		PretrainedConfig							     =		config

								UpperCAmelCase     :		Any							     =		("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
								for p in extra_model_params:
															if getattr(self.hparams		,		A		,		A    ):
																						assert hasattr(self.config		,		A    ), f'''model config doesn\'t have a `{p}` attribute'''
																						setattr(self.config		,		A		,		getattr(self.hparams		,		A    )    )

								if tokenizer is None:
															UpperCAmelCase     :		int							     =		AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,		cache_dir=A		,		)
								else:
															UpperCAmelCase     :		PreTrainedTokenizer							     =		tokenizer
								UpperCAmelCase     :		Dict							     =		MODEL_MODES[mode]
								if model is None:
															UpperCAmelCase     :		Tuple							     =		self.model_type.from_pretrained(
															    self.hparams.model_name_or_path		,		from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path    )		,		config=self.config		,		cache_dir=A		,		)
								else:
															UpperCAmelCase     :		Dict							     =		model





	def  _lowercase(  self		,		*A		,		**A    )			->			Optional[Any]:
								UpperCAmelCase     :		int							     =		self.model_type.from_pretrained(*A		,		**A    )





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Optional[Any]							     =		arg_to_scheduler[self.hparams.lr_scheduler]
								UpperCAmelCase     :		List[Any]							     =		get_schedule_func(
								    self.opt		,		num_warmup_steps=self.hparams.warmup_steps		,		num_training_steps=self.total_steps()    )
								UpperCAmelCase     :		List[Any]							     =		{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
								return scheduler





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		List[str]							     =		self.model
								UpperCAmelCase     :		int							     =		["""bias""", """LayerNorm.weight"""]
								UpperCAmelCase     :		Any							     =		[
								    {
								        """params""": [
								            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )
								        ],  # check this named paramters
								        """weight_decay""": self.hparams.weight_decay,
								    },
								    {
								        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
								        """weight_decay""": 0.0,
								    },
								]
								if self.hparams.adafactor:
															UpperCAmelCase     :		str							     =		Adafactor(
															    A		,		lr=self.hparams.learning_rate		,		scale_parameter=A		,		relative_step=A    )

								else:
															UpperCAmelCase     :		int							     =		AdamW(
															    A		,		lr=self.hparams.learning_rate		,		eps=self.hparams.adam_epsilon    )
								UpperCAmelCase     :		Any							     =		optimizer

								UpperCAmelCase     :		Optional[int]							     =		self.get_lr_scheduler()

								return [optimizer], [scheduler]





	def  _lowercase(  self		,		A		,		A    )			->			Union[str, Any]:
								return self.validation_step(A		,		A    )





	def  _lowercase(  self		,		A    )			->			Optional[Any]:
								return self.validation_end(A    )





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Union[str, Any]							     =		max(1		,		self.hparams.gpus    )  # TODO: consider num_tpu_cores
								UpperCAmelCase     :		Optional[int]							     =		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





	def  _lowercase(  self		,		A    )			->			Any:
								if stage == "test":
															UpperCAmelCase     :		List[str]							     =		len(self.test_dataloader().dataset    )
								else:
															UpperCAmelCase     :		Tuple							     =		self.get_dataloader("""train"""		,		self.hparams.train_batch_size		,		shuffle=A    )
															UpperCAmelCase     :		str							     =		len(self.train_dataloader().dataset    )





	def  _lowercase(  self		,		A		,		A		,		A = False    )			->			str:
								raise NotImplementedError("""You must implement this for your task"""    )





	def  _lowercase(  self    )			->			Optional[Any]:
								return self.train_loader





	def  _lowercase(  self    )			->			Optional[int]:
								return self.get_dataloader("""dev"""		,		self.hparams.eval_batch_size		,		shuffle=A    )





	def  _lowercase(  self    )			->			int:
								return self.get_dataloader("""test"""		,		self.hparams.eval_batch_size		,		shuffle=A    )





	def  _lowercase(  self		,		A    )			->			Tuple:
								return os.path.join(
								    self.hparams.data_dir		,		"""cached_{}_{}_{}""".format(
								        A		,		list(filter(A		,		self.hparams.model_name_or_path.split("""/"""    )    )    ).pop()		,		str(self.hparams.max_seq_length    )		,		)		,		)





	@pl.utilities.rank_zero_only
	def  _lowercase(  self		,		A    )			->			None:
								UpperCAmelCase     :		int							     =		self.output_dir.joinpath("""best_tfmr"""    )
								UpperCAmelCase     :		List[Any]							     =		self.step_count
								self.model.save_pretrained(A    )
								self.tokenizer.save_pretrained(A    )






	@staticmethod
	def  _lowercase(  A		,		A    )			->			str:
								parser.add_argument(
								    """--model_name_or_path"""		,		default=A		,		type=A		,		required=A		,		help="""Path to pretrained model or model identifier from huggingface.co/models"""		,		)
								parser.add_argument(
								    """--config_name"""		,		default=""""""		,		type=A		,		help="""Pretrained config name or path if not the same as model_name"""    )
								parser.add_argument(
								    """--tokenizer_name"""		,		default=A		,		type=A		,		help="""Pretrained tokenizer name or path if not the same as model_name"""		,		)
								parser.add_argument(
								    """--cache_dir"""		,		default=str(Path(A    ).parent / """test_run""" / """cache"""    )		,		type=A		,		help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""		,		)
								parser.add_argument(
								    """--encoder_layerdrop"""		,		type=A		,		help="""Encoder layer dropout probability (Optional). Goes into model.config"""		,		)
								parser.add_argument(
								    """--decoder_layerdrop"""		,		type=A		,		help="""Decoder layer dropout probability (Optional). Goes into model.config"""		,		)
								parser.add_argument(
								    """--dropout"""		,		type=A		,		help="""Dropout probability (Optional). Goes into model.config"""		,		)
								parser.add_argument(
								    """--attention_dropout"""		,		type=A		,		help="""Attention dropout probability (Optional). Goes into model.config"""		,		)
								parser.add_argument("""--learning_rate"""		,		default=5e-5		,		type=A		,		help="""The initial learning rate for Adam."""    )
								parser.add_argument(
								    """--lr_scheduler"""		,		default="""linear"""		,		choices=A		,		metavar=A		,		type=A		,		help="""Learning rate scheduler"""		,		)
								parser.add_argument("""--weight_decay"""		,		default=0.0		,		type=A		,		help="""Weight decay if we apply some."""    )
								parser.add_argument("""--adam_epsilon"""		,		default=1e-8		,		type=A		,		help="""Epsilon for Adam optimizer."""    )
								parser.add_argument("""--warmup_steps"""		,		default=0		,		type=A		,		help="""Linear warmup over warmup_steps."""    )
								parser.add_argument("""--num_workers"""		,		default=4		,		type=A		,		help="""kwarg passed to DataLoader"""    )
								parser.add_argument("""--num_train_epochs"""		,		dest="""max_epochs"""		,		default=3		,		type=A    )
								parser.add_argument("""--train_batch_size"""		,		default=32		,		type=A    )
								parser.add_argument("""--eval_batch_size"""		,		default=32		,		type=A    )
								parser.add_argument("""--adafactor"""		,		action="""store_true"""    )




class 			UpperCamelCase_ (							pl.Callback			):





	def  _lowercase(  self		,		A		,		A    )			->			Tuple:
								if (
								    trainer.is_global_zero and trainer.global_rank == 0
								):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
															pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class 			UpperCamelCase_ (							pl.Callback			):





	def  _lowercase(  self		,		A		,		A    )			->			List[Any]:
								# print(pl_module.model.rag)
								for name, param in pl_module.model.rag.named_parameters():
															if param.grad is None:
																						print(A    )




class 			UpperCamelCase_ (							pl.Callback			):





	def  _lowercase(  self		,		A		,		A    )			->			str:
								UpperCAmelCase     :		List[str]							     =		trainer.lr_schedulers[0]["""scheduler"""]
								UpperCAmelCase     :		str							     =		{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
								pl_module.logger.log_metrics(A    )





	def  _lowercase(  self		,		A		,		A    )			->			List[str]:
								rank_zero_info("""***** Validation results *****"""    )
								UpperCAmelCase     :		int							     =		trainer.callback_metrics
								# Log results
								for key in sorted(A    ):
															if key not in ["log", "progress_bar"]:
																						rank_zero_info("""{} = {}\n""".format(A		,		str(metrics[key]    )    )    )






	def  _lowercase(  self		,		A		,		A    )			->			Tuple:
								rank_zero_info("""***** Test results *****"""    )
								UpperCAmelCase     :		int							     =		trainer.callback_metrics
								# Log and save results to file
								UpperCAmelCase     :		Union[str, Any]							     =		os.path.join(pl_module.hparams.output_dir		,		"""test_results.txt"""    )
								with open(A		,		"""w"""    ) as writer:
															for key in sorted(A    ):
																						if key not in ["log", "progress_bar"]:
																													rank_zero_info("""{} = {}\n""".format(A		,		str(metrics[key]    )    )    )
																													writer.write("""{} = {}\n""".format(A		,		str(metrics[key]    )    )    )




def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			None:
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    """--output_dir"""	,				default=str(Path(_lowercase		).parent / """test_run""" / """model_checkpoints"""		)	,				type=_lowercase	,				help="""The output directory where the model predictions and checkpoints will be written."""	,				)
							parser.add_argument(
							    """--fp16"""	,				action="""store_true"""	,				help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""	,				)

							parser.add_argument(
							    """--fp16_opt_level"""	,				type=_lowercase	,				default="""O2"""	,				help=(
							        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
							        """See details at https://nvidia.github.io/apex/amp.html"""
							    )	,				)
							parser.add_argument("""--n_tpu_cores"""	,				dest="""tpu_cores"""	,				type=_lowercase		)
							parser.add_argument("""--max_grad_norm"""	,				dest="""gradient_clip_val"""	,				default=1.0	,				type=_lowercase	,				help="""Max gradient norm"""		)
							parser.add_argument("""--do_train"""	,				action="""store_true"""	,				help="""Whether to run training."""		)
							parser.add_argument("""--do_predict"""	,				action="""store_true"""	,				help="""Whether to run predictions on the test set."""		)
							parser.add_argument(
							    """--gradient_accumulation_steps"""	,				dest="""accumulate_grad_batches"""	,				type=_lowercase	,				default=1	,				help="""Number of updates steps to accumulate before performing a backward/update pass."""	,				)
							parser.add_argument("""--seed"""	,				type=_lowercase	,				default=4_2	,				help="""random seed for initialization"""		)
							parser.add_argument(
							    """--data_dir"""	,				default=str(Path(_lowercase		).parent / """test_run""" / """dummy-train-data"""		)	,				type=_lowercase	,				help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""	,				)



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase=None	,				_lowercase=True	,				_lowercase=[]	,				_lowercase=None	,				_lowercase=None	,				**_lowercase	,				)      ->			Union[str, Any]:
							pl.seed_everything(args.seed		)

							# init model
							UpperCAmelCase     :		Optional[Any]							     =		Path(model.hparams.output_dir		)
							odir.mkdir(exist_ok=_lowercase		)

							# add custom checkpoints
							if checkpoint_callback is None:
														UpperCAmelCase     :		List[str]							     =		pl.callbacks.ModelCheckpoint(
														    filepath=args.output_dir	,				prefix="""checkpoint"""	,				monitor="""val_loss"""	,				mode="""min"""	,				save_top_k=1		)
							if early_stopping_callback:
														extra_callbacks.append(_lowercase		)
							if logging_callback is None:
														UpperCAmelCase     :		List[Any]							     =		LoggingCallback()

							UpperCAmelCase     :		Dict							     =		{}

							if args.fpaa:
														UpperCAmelCase     :		List[str]							     =		1_6

							if args.gpus > 1:
														UpperCAmelCase     :		str							     =		"""auto"""
														UpperCAmelCase     :		str							     =		"""ddp"""

							UpperCAmelCase     :		Any							     =		args.accumulate_grad_batches
							UpperCAmelCase     :		List[Any]							     =		None
							UpperCAmelCase     :		str							     =		"""auto"""

							UpperCAmelCase     :		Optional[Any]							     =		pl.Trainer.from_argparse_args(
							    _lowercase	,				weights_summary=_lowercase	,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]	,				logger=_lowercase	,				val_check_interval=1	,				num_sanity_val_steps=2	,				**_lowercase	,				)

							if args.do_train:
														trainer.fit(_lowercase		)

							else:
														print("""RAG modeling tests with new set functions successfuly executed!"""		)
							return trainer






'''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""")

