


"""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


UpperCAmelCase       :	Dict  		=						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCAmelCase       :	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
UpperCAmelCase       :	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
}
UpperCAmelCase       :	List[Any]  		=						sorted(arg_to_scheduler.keys())
UpperCAmelCase       :	Tuple  		=						'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class 	lowerCamelCase__  (	pl.LightningModule							):







					"""simple docstring"""


					def __init__(			self       :					Optional[int]					,			UpperCamelCase       :					Any					,			UpperCamelCase       :					Optional[Any]=None					,			UpperCamelCase       :					Dict="base"					,			UpperCamelCase       :					Union[str, Any]=None					,			UpperCamelCase       :					Tuple=None					,			UpperCamelCase       :					str=None					,			**UpperCamelCase       :					Optional[Any]					,			):
												'''simple docstring'''






												super().__init__()
												# TODO: move to self.save_hyperparameters()
												# self.save_hyperparameters()
												# can also expand arguments into trainer signature for easier reading

												self.save_hyperparameters(UpperCamelCase_    )
												__UpperCAmelCase  :		List[Any] 				=   0
												__UpperCAmelCase  :		Union[str, Any] 				=   Path(self.hparams.output_dir    )
												__UpperCAmelCase  :		Union[str, Any] 				=   self.hparams.cache_dir if self.hparams.cache_dir else None
												if config is None:
																			__UpperCAmelCase  :		str 				=   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=UpperCamelCase_					,			**UpperCamelCase_					,			)
												else:
																			__UpperCAmelCase  :		Tuple 				=   config

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

												if tokenizer is None:
																			__UpperCAmelCase  :		Optional[Any] 				=   AutoTokenizer.from_pretrained(
																			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,			cache_dir=UpperCamelCase_					,			)
												else:
																			__UpperCAmelCase  :		Tuple 				=   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=UpperCamelCase_					,			)
												else:
																			__UpperCAmelCase  :		List[Any] 				=   model






					def lowerCamelCase__				(			self       :					Optional[Any]					,			*UpperCamelCase       :					Union[str, Any]					,			**UpperCamelCase       :					str    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Optional[int] 				=   self.model_type.from_pretrained(*UpperCamelCase_					,			**UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					Union[str, Any]    ):
												'''simple docstring'''






												__UpperCAmelCase  :		List[Any] 				=   arg_to_scheduler[self.hparams.lr_scheduler]
												__UpperCAmelCase  :		Optional[int] 				=   get_schedule_func(
												    self.opt					,			num_warmup_steps=self.hparams.warmup_steps					,			num_training_steps=self.total_steps()    )
												__UpperCAmelCase  :		Any 				=   {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
												return scheduler






					def lowerCamelCase__				(			self       :					Dict    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Dict 				=   self.model
												__UpperCAmelCase  :		Optional[Any] 				=   ["""bias""", """LayerNorm.weight"""]
												__UpperCAmelCase  :		Optional[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  :		Optional[int] 				=   Adafactor(
																			    UpperCamelCase_					,			lr=self.hparams.learning_rate					,			scale_parameter=UpperCamelCase_					,			relative_step=UpperCamelCase_    )

												else:
																			__UpperCAmelCase  :		int 				=   AdamW(
																			    UpperCamelCase_					,			lr=self.hparams.learning_rate					,			eps=self.hparams.adam_epsilon    )
												__UpperCAmelCase  :		List[Any] 				=   optimizer

												__UpperCAmelCase  :		Optional[Any] 				=   self.get_lr_scheduler()

												return [optimizer], [scheduler]






					def lowerCamelCase__				(			self       :					List[str]					,			UpperCamelCase       :					Any					,			UpperCamelCase       :					int    ):
												'''simple docstring'''






												return self.validation_step(UpperCamelCase_					,			UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					Optional[int]					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												return self.validation_end(UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					Any    ):
												'''simple docstring'''






												__UpperCAmelCase  :		int 				=   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 lowerCamelCase__				(			self       :					List[str]					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												if stage == "test":
																			__UpperCAmelCase  :		List[Any] 				=   len(self.test_dataloader().dataset    )
												else:
																			__UpperCAmelCase  :		List[str] 				=   self.get_dataloader("""train"""					,			self.hparams.train_batch_size					,			shuffle=UpperCamelCase_    )
																			__UpperCAmelCase  :		Dict 				=   len(self.train_dataloader().dataset    )






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					List[str]					,			UpperCamelCase       :					List[Any]					,			UpperCamelCase       :					Dict = False    ):
												'''simple docstring'''






												raise NotImplementedError("""You must implement this for your task"""    )






					def lowerCamelCase__				(			self       :					Optional[Any]    ):
												'''simple docstring'''






												return self.train_loader






					def lowerCamelCase__				(			self       :					Tuple    ):
												'''simple docstring'''






												return self.get_dataloader("""dev"""					,			self.hparams.eval_batch_size					,			shuffle=UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					Union[str, Any]    ):
												'''simple docstring'''






												return self.get_dataloader("""test"""					,			self.hparams.eval_batch_size					,			shuffle=UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												return os.path.join(
												    self.hparams.data_dir					,			"""cached_{}_{}_{}""".format(
												        UpperCamelCase_					,			list(filter(UpperCamelCase_					,			self.hparams.model_name_or_path.split("""/"""    )    )    ).pop()					,			str(self.hparams.max_seq_length    )					,			)					,			)






					@pl.utilities.rank_zero_only
					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					List[Any]    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Tuple 				=   self.output_dir.joinpath("""best_tfmr"""    )
												__UpperCAmelCase  :		Any 				=   self.step_count
												self.model.save_pretrained(UpperCamelCase_    )
												self.tokenizer.save_pretrained(UpperCamelCase_    )






					@staticmethod
					def lowerCamelCase__				(			UpperCamelCase       :					int					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






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






class 	lowerCamelCase__  (	pl.Callback							):







					"""simple docstring"""


					def lowerCamelCase__				(			self       :					Tuple					,			UpperCamelCase       :					Any					,			UpperCamelCase       :					int    ):
												'''simple docstring'''






												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 	lowerCamelCase__  (	pl.Callback							):







					"""simple docstring"""


					def lowerCamelCase__				(			self       :					Dict					,			UpperCamelCase       :					Optional[int]					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												for name, param in pl_module.model.rag.named_parameters():
																			if param.grad is None:
																										print(UpperCamelCase_    )






class 	lowerCamelCase__  (	pl.Callback							):







					"""simple docstring"""


					def lowerCamelCase__				(			self       :					str					,			UpperCamelCase       :					Union[str, Any]					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Optional[int] 				=   trainer.lr_schedulers[0]["""scheduler"""]
												__UpperCAmelCase  :		Any 				=   {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
												pl_module.logger.log_metrics(UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					Dict					,			UpperCamelCase       :					Dict    ):
												'''simple docstring'''






												rank_zero_info("""***** Validation results *****"""    )
												__UpperCAmelCase  :		int 				=   trainer.callback_metrics
												# Log results
												for key in sorted(UpperCamelCase_    ):
																			if key not in ["log", "progress_bar"]:
																										rank_zero_info("""{} = {}\n""".format(UpperCamelCase_					,			str(metrics[key]    )    )    )






					def lowerCamelCase__				(			self       :					Any					,			UpperCamelCase       :					Union[str, Any]					,			UpperCamelCase       :					Optional[Any]    ):
												'''simple docstring'''






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






def 	lowerCamelCase			(	_UpperCamelCase						: Optional[Any]			,     _UpperCamelCase						: Optional[Any]				)						->			None:

							'''simple docstring'''



							parser.add_argument(
							    """--output_dir"""			,     default=str(Path(UpperCAmelCase_				).parent / """test_run""" / """model_checkpoints"""				)			,     type=UpperCAmelCase_			,     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=UpperCAmelCase_			,     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=UpperCAmelCase_				)
							parser.add_argument("""--max_grad_norm"""			,     dest="""gradient_clip_val"""			,     default=1.0			,     type=UpperCAmelCase_			,     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=UpperCAmelCase_			,     default=1			,     help="""Number of updates steps to accumulate before performing a backward/update pass."""			,     )
							parser.add_argument("""--seed"""			,     type=UpperCAmelCase_			,     default=4_2			,     help="""random seed for initialization"""				)
							parser.add_argument(
							    """--data_dir"""			,     default=str(Path(UpperCAmelCase_				).parent / """test_run""" / """dummy-train-data"""				)			,     type=UpperCAmelCase_			,     help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""			,     )
def 	lowerCamelCase			(	_UpperCamelCase						: Any			,     _UpperCamelCase						: Tuple			,     _UpperCamelCase						: Tuple=None			,     _UpperCamelCase						: Any=True			,     _UpperCamelCase						: Any=[]			,     _UpperCamelCase						: List[str]=None			,     _UpperCamelCase						: Union[str, Any]=None			,     **_UpperCamelCase						: List[str]			,     )						->			List[Any]:

							'''simple docstring'''



							pl.seed_everything(args.seed				)

							# init model
							__UpperCAmelCase  :		Tuple 				=   Path(model.hparams.output_dir				)
							odir.mkdir(exist_ok=UpperCAmelCase_				)

							# add custom checkpoints
							if checkpoint_callback is None:
														__UpperCAmelCase  :		Dict 				=   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(UpperCAmelCase_				)
							if logging_callback is None:
														__UpperCAmelCase  :		str 				=   LoggingCallback()

							__UpperCAmelCase  :		Optional[Any] 				=   {}

							if args.fpaa:
														__UpperCAmelCase  :		Union[str, Any] 				=   1_6

							if args.gpus > 1:
														__UpperCAmelCase  :		Tuple 				=   """auto"""
														__UpperCAmelCase  :		Optional[Any] 				=   """ddp"""

							__UpperCAmelCase  :		str 				=   args.accumulate_grad_batches
							__UpperCAmelCase  :		int 				=   None
							__UpperCAmelCase  :		Optional[Any] 				=   """auto"""

							__UpperCAmelCase  :		List[str] 				=   pl.Trainer.from_argparse_args(
							    UpperCAmelCase_			,     weights_summary=UpperCAmelCase_			,     callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]			,     logger=UpperCAmelCase_			,     val_check_interval=1			,     num_sanity_val_steps=2			,     **UpperCAmelCase_			,     )

							if args.do_train:
														trainer.fit(UpperCAmelCase_				)

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







'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

		snake_case__	       =   (image / 2 + 0.5).clamp(0      ,       1		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()
		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



