


'''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


lowerCamelCase__     						=  logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCamelCase__     						=  {
    '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
lowerCamelCase__     						=  {
    '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
}
lowerCamelCase__     						=  sorted(arg_to_scheduler.keys())
lowerCamelCase__     						=  '{' + ', '.join(arg_to_scheduler_choices) + '}'






class 						lowerCAmelCase__      (      pl.LightningModule						):




			def __init__(		self	:  List[Any]						,	lowerCamelCase__	:  argparse.Namespace						,	lowerCamelCase__	:  Tuple=None						,	lowerCamelCase__	:  Union[str, Any]="base"						,	lowerCamelCase__	:  List[Any]=None						,	lowerCamelCase__	:  Union[str, Any]=None						,	lowerCamelCase__	:  Union[str, Any]=None						,	**lowerCamelCase__	:  Any						,	) ->Tuple:



					'''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(lowerCamelCase__			)
					_UpperCAmelCase		:							str             =   0
					_UpperCAmelCase		:							Tuple             =   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=lowerCamelCase__						,	**lowerCamelCase__						,	)
					else:
							_UpperCAmelCase		:							PretrainedConfig             =   config

					_UpperCAmelCase		:							Tuple             =   ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
					for p in extra_model_params:
							if getattr(self.hparams						,	lowerCamelCase__						,	lowerCamelCase__			):
									assert hasattr(self.config						,	lowerCamelCase__			), F"""model config doesn't have a `{p}` attribute"""
									setattr(self.config						,	lowerCamelCase__						,	getattr(self.hparams						,	lowerCamelCase__			)			)

					if tokenizer is None:
							_UpperCAmelCase		:							List[str]             =   AutoTokenizer.from_pretrained(
							    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						,	cache_dir=lowerCamelCase__						,	)
					else:
							_UpperCAmelCase		:							PreTrainedTokenizer             =   tokenizer
					_UpperCAmelCase		:							Optional[int]             =   MODEL_MODES[mode]
					if model is None:
							_UpperCAmelCase		:							int             =   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=lowerCamelCase__						,	)
					else:
							_UpperCAmelCase		:							Union[str, Any]             =   model




			def 				lowerCAmelCase__     (		self	:  int						,	*lowerCamelCase__	:  Tuple						,	**lowerCamelCase__	:  Any			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   self.model_type.from_pretrained(*lowerCamelCase__						,	**lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   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		:							int             =   {"scheduler": scheduler, "interval": "step", "frequency": 1}
					return scheduler




			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   self.model
					_UpperCAmelCase		:							Optional[Any]             =   ["bias", "LayerNorm.weight"]
					_UpperCAmelCase		:							List[str]             =   [
					    {
					        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
					        "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[Any]             =   Adafactor(
							    lowerCamelCase__						,	lr=self.hparams.learning_rate						,	scale_parameter=lowerCamelCase__						,	relative_step=lowerCamelCase__			)

					else:
							_UpperCAmelCase		:							Optional[Any]             =   AdamW(
							    lowerCamelCase__						,	lr=self.hparams.learning_rate						,	eps=self.hparams.adam_epsilon			)
					_UpperCAmelCase		:							List[str]             =   optimizer

					_UpperCAmelCase		:							Any             =   self.get_lr_scheduler()

					return [optimizer], [scheduler]




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  Optional[int]			) ->Union[str, Any]:



					'''simple docstring'''







					return self.validation_step(lowerCamelCase__						,	lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  Dict			) ->str:



					'''simple docstring'''







					return self.validation_end(lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   max(1						,	self.hparams.gpus			)  # TODO: consider num_tpu_cores
					_UpperCAmelCase		:							List[Any]             =   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	:  Optional[Any]						,	lowerCamelCase__	:  Dict			) ->List[str]:



					'''simple docstring'''







					if stage == "test":
							_UpperCAmelCase		:							Any             =   len(self.test_dataloader().dataset			)
					else:
							_UpperCAmelCase		:							Dict             =   self.get_dataloader("train"						,	self.hparams.train_batch_size						,	shuffle=lowerCamelCase__			)
							_UpperCAmelCase		:							Optional[int]             =   len(self.train_dataloader().dataset			)




			def 				lowerCAmelCase__     (		self	:  Optional[int]						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  int						,	lowerCamelCase__	:  bool = False			) ->int:



					'''simple docstring'''







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




			def 				lowerCAmelCase__     (		self	:  str			) ->Optional[Any]:



					'''simple docstring'''







					return self.train_loader




			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->List[Any]:



					'''simple docstring'''







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




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Dict:



					'''simple docstring'''







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




			def 				lowerCAmelCase__     (		self	:  int						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







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




			@pl.utilities.rank_zero_only
			def 				lowerCAmelCase__     (		self	:  str						,	lowerCamelCase__	:  Dict[str, Any]			) ->None:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   self.output_dir.joinpath("best_tfmr"			)
					_UpperCAmelCase		:							int             =   self.step_count
					self.model.save_pretrained(lowerCamelCase__			)
					self.tokenizer.save_pretrained(lowerCamelCase__			)






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Optional[int]			) ->Any:



					'''simple docstring'''







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






class 						lowerCAmelCase__      (      pl.Callback						):




			def 				lowerCAmelCase__     (		self	:  Dict						,	lowerCamelCase__	:  Tuple						,	lowerCamelCase__	:  Optional[Any]			) ->Any:



					'''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						):




			def 				lowerCAmelCase__     (		self	:  Any						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  int			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   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(lowerCamelCase__			)




			def 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule			) ->Union[str, Any]:



					'''simple docstring'''







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






			def 				lowerCAmelCase__     (		self	:  Union[str, Any]						,	lowerCamelCase__	:  pl.Trainer						,	lowerCamelCase__	:  pl.LightningModule			) ->Union[str, Any]:



					'''simple docstring'''







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






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    "--output_dir"   ,			default=__lowerCAmelCase   ,			type=__lowerCAmelCase   ,			required=__lowerCAmelCase   ,			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=__lowerCAmelCase   ,			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=__lowerCAmelCase )
		parser.add_argument("--max_grad_norm"   ,			dest="gradient_clip_val"   ,			default=1.0   ,			type=__lowerCAmelCase   ,			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=__lowerCAmelCase   ,			default=1   ,			help="Number of updates steps to accumulate before performing a backward/update pass."   ,			)
		parser.add_argument("--seed"   ,			type=__lowerCAmelCase   ,			default=42   ,			help="random seed for initialization" )
		parser.add_argument(
		    "--data_dir"   ,			default=__lowerCAmelCase   ,			type=__lowerCAmelCase   ,			required=__lowerCAmelCase   ,			help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."   ,			)






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=True   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=[]   ,			__lowerCAmelCase=None   ,			__lowerCAmelCase=None   ,			**__lowerCAmelCase   ,			):
		pl.seed_everything(args.seed )

		# init model
		_UpperCAmelCase		:							Optional[int]             =   Path(model.hparams.output_dir )
		odir.mkdir(exist_ok=__lowerCAmelCase )

		# add custom checkpoints
		if checkpoint_callback is None:
				_UpperCAmelCase		:							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(__lowerCAmelCase )
		if logging_callback is None:
				_UpperCAmelCase		:							Optional[int]             =   LoggingCallback()

		_UpperCAmelCase		:							Dict             =   {}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				_UpperCAmelCase		:							Optional[int]             =   16
				# train_params["amp_level"] = args.fp16_opt_level

		if args.gpus > 1:
				_UpperCAmelCase		:							Dict             =   "auto"  # "ddp"
				_UpperCAmelCase		:							int             =   "ddp"

		_UpperCAmelCase		:							Dict             =   args.accumulate_grad_batches
		_UpperCAmelCase		:							int             =   None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
		_UpperCAmelCase		:							Optional[int]             =   "auto"

		_UpperCAmelCase		:							Optional[Any]             =   pl.Trainer.from_argparse_args(
		    __lowerCAmelCase   ,			weights_summary=__lowerCAmelCase   ,			callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]   ,			logger=__lowerCAmelCase   ,			**__lowerCAmelCase   ,			)

		if args.do_train:
				trainer.fit(__lowerCAmelCase )

		return trainer







'''simple docstring'''


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    'cross_attention',
    '0.20.0',
    'Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.',
    standard_warn=False,
)


lowerCamelCase__     						=  AttentionProcessor






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Union[str, Any]						,	*lowerCamelCase__	:  int						,	**lowerCamelCase__	:  List[str]			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Optional[Any]						,	*lowerCamelCase__	:  Any						,	**lowerCamelCase__	:  Tuple			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  List[Any]						,	*lowerCamelCase__	:  List[Any]						,	**lowerCamelCase__	:  List[str]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  int						,	*lowerCamelCase__	:  Tuple						,	**lowerCamelCase__	:  Union[str, Any]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Dict             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  int						,	*lowerCamelCase__	:  List[str]						,	**lowerCamelCase__	:  Tuple			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  List[Any]						,	*lowerCamelCase__	:  int						,	**lowerCamelCase__	:  Optional[int]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Any						,	*lowerCamelCase__	:  Optional[Any]						,	**lowerCamelCase__	:  Union[str, Any]			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			def __init__(		self	:  Optional[Any]						,	*lowerCamelCase__	:  List[Any]						,	**lowerCamelCase__	:  Dict			) ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {''.join(self.__class__.__name__.split('Cross'			)			)} instead."""
					deprecate("cross_attention"						,	"0.20.0"						,	lowerCamelCase__						,	standard_warn=lowerCamelCase__			)
					super().__init__(*lowerCamelCase__						,	**lowerCamelCase__			)



