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


lowercase_  : Union[str, Any]								=					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowercase_  : Tuple								=					{
    '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
lowercase_  : Optional[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
}
lowercase_  : List[str]								=					sorted(arg_to_scheduler.keys())
lowercase_  : int								=					'{' + ', '.join(arg_to_scheduler_choices) + '}'






class 		_lowerCamelCase		( pl.LightningModule     ):
    def __init__(	self			,			lowerCAmelCase			,			lowerCAmelCase=None			,			lowerCAmelCase="base"			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			**lowerCAmelCase			,			)							->       Union[str, 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(lowerCamelCase_ )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   0
     SCREAMING_SNAKE_CASE__: Optional[Any]=   Path(self.hparams.output_dir )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
      SCREAMING_SNAKE_CASE__: List[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:
      SCREAMING_SNAKE_CASE__: PretrainedConfig=   config

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   ('''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:
      SCREAMING_SNAKE_CASE__: Tuple=   AutoTokenizer.from_pretrained(
          self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,			cache_dir=lowerCamelCase_			,			)
     else:
      SCREAMING_SNAKE_CASE__: PreTrainedTokenizer=   tokenizer
     SCREAMING_SNAKE_CASE__: Tuple=   MODEL_MODES[mode]
     if model is None:
      SCREAMING_SNAKE_CASE__: List[str]=   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:
      SCREAMING_SNAKE_CASE__: Optional[int]=   model
    def     UpperCamelCase_					(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       Dict:
     SCREAMING_SNAKE_CASE__: List[str]=   self.model_type.from_pretrained(*lowerCamelCase_			,			**lowerCamelCase_ )
    def     UpperCamelCase_					(	self )							->       Any:
     SCREAMING_SNAKE_CASE__: str=   arg_to_scheduler[self.hparams.lr_scheduler]
     SCREAMING_SNAKE_CASE__: List[Any]=   get_schedule_func(
         self.opt			,			num_warmup_steps=self.hparams.warmup_steps			,			num_training_steps=self.total_steps() )
     SCREAMING_SNAKE_CASE__: Optional[int]=   {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
     return scheduler
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: int=   self.model
     SCREAMING_SNAKE_CASE__: Optional[Any]=   ['''bias''', '''LayerNorm.weight''']
     SCREAMING_SNAKE_CASE__: List[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:
      SCREAMING_SNAKE_CASE__: Tuple=   Adafactor(
          lowerCamelCase_			,			lr=self.hparams.learning_rate			,			scale_parameter=lowerCamelCase_			,			relative_step=lowerCamelCase_ )

     else:
      SCREAMING_SNAKE_CASE__: Optional[int]=   AdamW(
          lowerCamelCase_			,			lr=self.hparams.learning_rate			,			eps=self.hparams.adam_epsilon )
     SCREAMING_SNAKE_CASE__: Optional[int]=   optimizer

     SCREAMING_SNAKE_CASE__: int=   self.get_lr_scheduler()

     return [optimizer], [scheduler]
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Dict:
     return self.validation_step(lowerCamelCase_			,			lowerCamelCase_ )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       str:
     return self.validation_end(lowerCamelCase_ )
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: List[str]=   max(1			,			self.hparams.gpus )  # TODO: consider num_tpu_cores
     SCREAMING_SNAKE_CASE__: 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     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       Dict:



     if stage == "test":
      SCREAMING_SNAKE_CASE__: List[str]=   len(self.test_dataloader().dataset )
     else:
      SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_dataloader('''train'''			,			self.hparams.train_batch_size			,			shuffle=lowerCamelCase_ )
      SCREAMING_SNAKE_CASE__: Optional[Any]=   len(self.train_dataloader().dataset )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase = False )							->       str:
     raise NotImplementedError('''You must implement this for your task''' )
    def     UpperCamelCase_					(	self )							->       Any:
     return self.train_loader
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     return self.get_dataloader('''dev'''			,			self.hparams.eval_batch_size			,			shuffle=lowerCamelCase_ )
    def     UpperCamelCase_					(	self )							->       Tuple:
     return self.get_dataloader('''test'''			,			self.hparams.eval_batch_size			,			shuffle=lowerCamelCase_ )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       List[str]:
     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     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       None:
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.output_dir.joinpath('''best_tfmr''' )
     SCREAMING_SNAKE_CASE__: str=   self.step_count
     self.model.save_pretrained(lowerCamelCase_ )
     self.tokenizer.save_pretrained(lowerCamelCase_ )



    @staticmethod
    def     UpperCamelCase_					(	lowerCAmelCase			,			lowerCAmelCase )							->       List[str]:
     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=str(Path(lowerCamelCase_ ).parent / '''test_run''' / '''cache''' )			,			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     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[int]:



     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     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[Any]:



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






class 		_lowerCamelCase		( pl.Callback     ):
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: List[str]=   trainer.lr_schedulers[0]['''scheduler''']
     SCREAMING_SNAKE_CASE__: Tuple=   {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr() )}
     pl_module.logger.log_metrics(lowerCamelCase_ )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       Dict:
     rank_zero_info('''***** Validation results *****''' )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   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     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     rank_zero_info('''***** Test results *****''' )
     SCREAMING_SNAKE_CASE__: List[Any]=   trainer.callback_metrics
     # Log and save results to file
     SCREAMING_SNAKE_CASE__: Dict=   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       A__   (						snake_case_			:		Tuple							,					snake_case_			:		str   ):
 parser.add_argument(
     '''--output_dir'''							,					default=str(Path(_A   ).parent / '''test_run''' / '''model_checkpoints'''   )							,					type=_A							,					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=_A							,					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=_A   )
 parser.add_argument('''--max_grad_norm'''							,					dest='''gradient_clip_val'''							,					default=1.0							,					type=_A							,					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=_A							,					default=1							,					help='''Number of updates steps to accumulate before performing a backward/update pass.'''							,					)
 parser.add_argument('''--seed'''							,					type=_A							,					default=42							,					help='''random seed for initialization'''   )
 parser.add_argument(
     '''--data_dir'''							,					default=str(Path(_A   ).parent / '''test_run''' / '''dummy-train-data'''   )							,					type=_A							,					help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''							,					)







def       A__   (						snake_case_			:		BaseTransformer							,					snake_case_			:		argparse.Namespace							,					snake_case_			:		Optional[Any]=None							,					snake_case_			:		int=True							,					snake_case_			:		Any=[]							,					snake_case_			:		Optional[Any]=None							,					snake_case_			:		Tuple=None							,					**snake_case_			:		Dict							,					):
 pl.seed_everything(args.seed   )

 # init model
 SCREAMING_SNAKE_CASE__: Tuple=   Path(model.hparams.output_dir   )
 odir.mkdir(exist_ok=_A   )

 # add custom checkpoints
 if checkpoint_callback is None:
  SCREAMING_SNAKE_CASE__: 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(_A   )
 if logging_callback is None:
  SCREAMING_SNAKE_CASE__: Dict=   LoggingCallback()

 SCREAMING_SNAKE_CASE__: Any=   {}

 if args.fpaa:
  SCREAMING_SNAKE_CASE__: List[Any]=   16

 if args.gpus > 1:
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   '''auto'''
  SCREAMING_SNAKE_CASE__: List[Any]=   '''ddp'''

 SCREAMING_SNAKE_CASE__: Any=   args.accumulate_grad_batches
 SCREAMING_SNAKE_CASE__: Dict=   None
 SCREAMING_SNAKE_CASE__: List[str]=   '''auto'''

 SCREAMING_SNAKE_CASE__: Dict=   pl.Trainer.from_argparse_args(
     _A							,					weights_summary=_A							,					callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]							,					logger=_A							,					val_check_interval=1							,					num_sanity_val_steps=2							,					**_A							,					)

 if args.do_train:
  trainer.fit(_A   )

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


'''simple docstring'''

import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class 					_lowercase						(   lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   int   				= AutoencoderKL
      UpperCAmelCase_   :   Any   				= '''sample'''
      UpperCAmelCase_   :   List[str]   				= 1E-2




      @property
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	4
         UpperCAmelCase__				:					Optional[int] 					=	3
         UpperCAmelCase__				:					Tuple 					=	(32, 32)

         UpperCAmelCase__				:					Optional[int] 					=	floats_tensor((batch_size, num_channels) + sizes				).to(lowerCamelCase_				)

         return {"sample": image}




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



         '''simple docstring'''



         return (3, 32, 32)




      @property
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         return (3, 32, 32)




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	{
             '''block_out_channels''': [32, 64],
             '''in_channels''': 3,
             '''out_channels''': 3,
             '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
             '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
             '''latent_channels''': 4,
         }
         UpperCAmelCase__				:					Optional[Any] 					=	self.dummy_input
         return init_dict, inputs_dict




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



         '''simple docstring'''



         pass




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



         '''simple docstring'''



         pass




      @unittest.skipIf(torch_device == '''mps'''       ,'''Gradient checkpointing skipped on MPS'''				)
      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[Any] 					=	self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase__				:					Union[str, Any] 					=	self.model_class(**lowerCamelCase_				)
         model.to(lowerCamelCase_				)

         assert not model.is_gradient_checkpointing and model.training

         UpperCAmelCase__				:					Any 					=	model(**lowerCamelCase_				).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model.zero_grad()

         UpperCAmelCase__				:					Optional[int] 					=	torch.randn_like(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	(out - labels).mean()
         loss.backward()

         # re-instantiate the model now enabling gradient checkpointing
         UpperCAmelCase__				:					Optional[int] 					=	self.model_class(**lowerCamelCase_				)
         # clone model
         model_a.load_state_dict(model.state_dict()				)
         model_a.to(lowerCamelCase_				)
         model_a.enable_gradient_checkpointing()

         assert model_a.is_gradient_checkpointing and model_a.training

         UpperCAmelCase__				:					int 					=	model_a(**lowerCamelCase_				).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model_a.zero_grad()
         UpperCAmelCase__				:					Union[str, Any] 					=	(out_a - labels).mean()
         loss_a.backward()

         # compare the output and parameters gradients
         self.assertTrue((loss - loss_a).abs() < 1e-5				)
         UpperCAmelCase__				:					Union[str, Any] 					=	dict(model.named_parameters()				)
         UpperCAmelCase__				:					int 					=	dict(model_a.named_parameters()				)
         for name, param in named_params.items():
            self.assertTrue(torch_all_close(param.grad.data       ,named_params_a[name].grad.data       ,atol=5e-5				)				)




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''       ,output_loading_info=lowerCamelCase_				)
         self.assertIsNotNone(lowerCamelCase_				)
         self.assertEqual(len(loading_info['''missing_keys''']				)       ,0				)

         model.to(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	model(**self.dummy_input				)

         assert image is not None, "Make sure output is not None"




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''				)
         UpperCAmelCase__				:					Optional[Any] 					=	model.to(lowerCamelCase_				)
         model.eval()

         if torch_device == "mps":
            UpperCAmelCase__				:					Optional[Any] 					=	torch.manual_seed(0				)
         else:
            UpperCAmelCase__				:					Tuple 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(0				)

         UpperCAmelCase__				:					str 					=	torch.randn(
             1       ,model.config.in_channels       ,model.config.sample_size       ,model.config.sample_size       ,generator=torch.manual_seed(0				)       ,)
         UpperCAmelCase__				:					int 					=	image.to(lowerCamelCase_				)
         with torch.no_grad():
            UpperCAmelCase__				:					str 					=	model(lowerCamelCase_       ,sample_posterior=lowerCamelCase_       ,generator=lowerCamelCase_				).sample

         UpperCAmelCase__				:					int 					=	output[0, -1, -3:, -3:].flatten().cpu()

         # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
         # the expected output slices are not the same for CPU and GPU.
         if torch_device == "mps":
            UpperCAmelCase__				:					int 					=	torch.tensor(
                [
                    -4.0078e-01,
                    -3.8323e-04,
                    -1.2681e-01,
                    -1.1462e-01,
                    2.0095e-01,
                    1.0893e-01,
                    -8.8247e-02,
                    -3.0361e-01,
                    -9.8644e-03,
                ]				)
         elif torch_device == "cpu":
            UpperCAmelCase__				:					List[Any] 					=	torch.tensor(
                [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]				)
         else:
            UpperCAmelCase__				:					Optional[Any] 					=	torch.tensor(
                [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]				)

         self.assertTrue(torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,rtol=1e-2				)				)




@slow
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         return f'''gaussian_noise_s={seed}_shape={'_'.join([str(lowerCamelCase_				) for s in shape]				)}.npy'''




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



         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=0       ,lowerCamelCase_=(4, 3, 512, 512)       ,lowerCamelCase_=False				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	torch.floataa if fpaa else torch.floataa
         UpperCAmelCase__				:					Dict 					=	torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_       ,lowerCamelCase_				)				)				).to(lowerCamelCase_				).to(lowerCamelCase_				)
         return image




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_="CompVis/stable-diffusion-v1-4"       ,lowerCamelCase_=False				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	'''fp16''' if fpaa else None
         UpperCAmelCase__				:					str 					=	torch.floataa if fpaa else torch.floataa

         UpperCAmelCase__				:					List[Any] 					=	AutoencoderKL.from_pretrained(
             lowerCamelCase_       ,subfolder='''vae'''       ,torch_dtype=lowerCamelCase_       ,revision=lowerCamelCase_       ,)
         model.to(lowerCamelCase_				).eval()

         return model




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_=0				)      ->      Optional[Any]:



         '''simple docstring'''



         if torch_device == "mps":
            return torch.manual_seed(lowerCamelCase_				)
         return torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)




      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
              [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
              # fmt: on
          ]				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					str 					=	self.get_sd_image(lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	self.get_generator(lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					Tuple 					=	model(lowerCamelCase_       ,generator=lowerCamelCase_       ,sample_posterior=lowerCamelCase_				).sample

         assert sample.shape == image.shape

         UpperCAmelCase__				:					List[Any] 					=	sample[-1, -2:, -2:, :2].flatten().float().cpu()
         UpperCAmelCase__				:					Tuple 					=	torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=3e-3				)




      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
              [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
              # fmt: on
          ]				)
      @require_torch_gpu
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	self.get_sd_vae_model(fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	self.get_sd_image(lowerCamelCase_       ,fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	self.get_generator(lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					List[str] 					=	model(lowerCamelCase_       ,generator=lowerCamelCase_       ,sample_posterior=lowerCamelCase_				).sample

         assert sample.shape == image.shape

         UpperCAmelCase__				:					Optional[Any] 					=	sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCAmelCase__				:					List[str] 					=	torch.tensor(lowerCamelCase_				)

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-2				)




      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
              [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
              # fmt: on
          ]				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					Optional[int] 					=	self.get_sd_image(lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					List[str] 					=	model(lowerCamelCase_				).sample

         assert sample.shape == image.shape

         UpperCAmelCase__				:					str 					=	sample[-1, -2:, -2:, :2].flatten().float().cpu()
         UpperCAmelCase__				:					Optional[Any] 					=	torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice				)

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=3e-3				)




      @parameterized.expand(
          [
              # fmt: off
              [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
              [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
              # fmt: on
          ]				)
      @require_torch_gpu
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Dict:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					Dict 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)				)

         with torch.no_grad():
            UpperCAmelCase__				:					Optional[Any] 					=	model.decode(lowerCamelCase_				).sample

         assert list(sample.shape				) == [3, 3, 512, 512]

         UpperCAmelCase__				:					Optional[int] 					=	sample[-1, -2:, :2, -2:].flatten().cpu()
         UpperCAmelCase__				:					str 					=	torch.tensor(lowerCamelCase_				)

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-3				)




      @parameterized.expand(
          [
              # fmt: off
              [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
              [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
              # fmt: on
          ]				)
      @require_torch_gpu
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.get_sd_vae_model(fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)       ,fpaa=lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					Dict 					=	model.decode(lowerCamelCase_				).sample

         assert list(sample.shape				) == [3, 3, 512, 512]

         UpperCAmelCase__				:					List[str] 					=	sample[-1, -2:, :2, -2:].flatten().float().cpu()
         UpperCAmelCase__				:					Any 					=	torch.tensor(lowerCamelCase_				)

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=5e-3				)




      @parameterized.expand([(13,), (16,), (27,)]				)
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()       ,reason='''xformers is not required when using PyTorch 2.0.'''				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.get_sd_vae_model(fpaa=lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)       ,fpaa=lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					Any 					=	model.decode(lowerCamelCase_				).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            UpperCAmelCase__				:					int 					=	model.decode(lowerCamelCase_				).sample

         assert list(sample.shape				) == [3, 3, 512, 512]

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-1				)




      @parameterized.expand([(13,), (16,), (37,)]				)
      @require_torch_gpu
      @unittest.skipIf(not is_xformers_available()       ,reason='''xformers is not required when using PyTorch 2.0.'''				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					List[Any] 					=	self.get_sd_image(lowerCamelCase_       ,shape=(3, 4, 64, 64)				)

         with torch.no_grad():
            UpperCAmelCase__				:					Tuple 					=	model.decode(lowerCamelCase_				).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
            UpperCAmelCase__				:					List[str] 					=	model.decode(lowerCamelCase_				).sample

         assert list(sample.shape				) == [3, 3, 512, 512]

         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=1e-2				)




      @parameterized.expand(
          [
              # fmt: off
              [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
              [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
              # fmt: on
          ]				)
      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.get_sd_vae_model()
         UpperCAmelCase__				:					Optional[Any] 					=	self.get_sd_image(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	self.get_generator(lowerCamelCase_				)

         with torch.no_grad():
            UpperCAmelCase__				:					Optional[int] 					=	model.encode(lowerCamelCase_				).latent_dist
            UpperCAmelCase__				:					Tuple 					=	dist.sample(generator=lowerCamelCase_				)

         assert list(sample.shape				) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

         UpperCAmelCase__				:					List[str] 					=	sample[0, -1, -3:, -3:].flatten().cpu()
         UpperCAmelCase__				:					Optional[Any] 					=	torch.tensor(lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	3e-3 if torch_device != '''mps''' else 1e-2
         assert torch_all_close(lowerCamelCase_       ,lowerCamelCase_       ,atol=lowerCamelCase_				)




