

"""simple docstring"""






import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed






class       _UpperCAmelCase     (						__a):
			def __init__(						self ,						_A ,						_A ,						_A=False ,						_A=10_24 ,						_A=10_24 ,						_A=3.6 ,						_A=False ,						)      ->      List[Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   tokenizer
				_UpperCAmelCase      :							List[str]       =   tokenizer.bos_token_id
				_UpperCAmelCase      :							List[Any]       =   dataset
				_UpperCAmelCase      :							Optional[Any]       =   seq_length
				_UpperCAmelCase      :							Optional[Any]       =   0
				_UpperCAmelCase      :							Union[str, Any]       =   infinite
				_UpperCAmelCase      :							List[Any]       =   0
				_UpperCAmelCase      :							Optional[int]       =   tokenized

				if self.tokenized:
					_UpperCAmelCase      :							Dict       =   seq_length * num_of_sequences
					_UpperCAmelCase      :							int       =   """input_ids"""
				else:
					_UpperCAmelCase      :							Optional[Any]       =   seq_length * chars_per_token * num_of_sequences
					_UpperCAmelCase      :							Tuple       =   """content"""
			def __iter__(						self       )      ->      int:






				'''simple docstring'''







				_UpperCAmelCase      :							Tuple       =   iter(self.dataset       )
				_UpperCAmelCase      :							List[Any]       =   True
				while more_examples:
					_UpperCAmelCase  ,				_UpperCAmelCase      :							Optional[int]       =   [], 0
					while True:
						if buffer_len >= self.max_buffer_size:
							break
						try:
							buffer.append(next(_A       )[self.content_field]       )
							buffer_len += len(buffer[-1]       )
						except StopIteration:
							if self.infinite:
								_UpperCAmelCase      :							int       =   iter(self.dataset       )
								self.epoch += 1
								logger.info(f'''Dataset epoch: {self.epoch}'''       )
							else:
								_UpperCAmelCase      :							Tuple       =   False
								break
					if self.tokenized:
						_UpperCAmelCase      :							Union[str, Any]       =   buffer
					else:
						_UpperCAmelCase      :							Any       =   self.tokenizer(_A ,						truncation=_A       )["""input_ids"""]
					_UpperCAmelCase      :							str       =   []
					for tokenized_input in tokenized_inputs:
						all_token_ids.extend(tokenized_input + [self.concat_token_id]       )
					for i in range(0 ,						len(_A       ) ,						self.seq_length       ):
						_UpperCAmelCase      :							int       =   all_token_ids[i : i + self.seq_length]
						if len(_A       ) == self.seq_length:
							self.current_size += 1
							yield torch.tensor(_A       )
			def 							__snake_case	(						self ,						_A=10_00       )      ->      Optional[int]:






				'''simple docstring'''







				return ShufflerIterDataPipe(self ,						buffer_size=_A       )
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int]	)			->     str:
	_UpperCAmelCase      :							Dict       =   args.model_ckpt.split("""/"""	)[-1]
	_UpperCAmelCase      :							str       =   logging.getLogger(__name__	)
	_UpperCAmelCase      :							Optional[Any]       =   Path(args.save_dir	) / """log/"""
	log_dir.mkdir(exist_ok=_lowerCAmelCase	)
	_UpperCAmelCase      :							Union[str, Any]       =   f'''debug_{accelerator.process_index}.log'''
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",					datefmt="""%m/%d/%Y %H:%M:%S""",					level=logging.INFO,					handlers=[logging.FileHandler(log_dir / filename	), logging.StreamHandler()],					)
	if accelerator.is_main_process:  # we only want to setup logging once
		accelerator.init_trackers(_lowerCAmelCase,					vars(_lowerCAmelCase	)	)
		_UpperCAmelCase      :							Tuple       =   accelerator.trackers[0].run.name
		logger.setLevel(logging.INFO	)
		datasets.utils.logging.set_verbosity_info()
		transformers.utils.logging.set_verbosity_info()
	else:
		_UpperCAmelCase      :							Optional[Any]       =   """"""
		logger.setLevel(logging.ERROR	)
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	return logger, run_name
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[Any]	)			->     int:
	_UpperCAmelCase      :							Union[str, Any]       =   {"""streaming""": True}
	_UpperCAmelCase      :							int       =   load_dataset(args.dataset_name_train,					split="""train""",					**_lowerCAmelCase	)
	_UpperCAmelCase      :							List[Any]       =   train_data.shuffle(buffer_size=args.shuffle_buffer,					seed=args.seed	)
	_UpperCAmelCase      :							Dict       =   load_dataset(args.dataset_name_valid,					split="""train""",					**_lowerCAmelCase	)
	_UpperCAmelCase      :							Dict       =   ConstantLengthDataset(
	    _lowerCAmelCase,					_lowerCAmelCase,					infinite=_lowerCAmelCase,					seq_length=args.seq_length,					tokenized=args.tokenized	)
	_UpperCAmelCase      :							Optional[Any]       =   ConstantLengthDataset(
	    _lowerCAmelCase,					_lowerCAmelCase,					infinite=_lowerCAmelCase,					seq_length=args.seq_length,					tokenized=args.tokenized	)
	_UpperCAmelCase      :							str       =   train_dataset.shuffle(buffer_size=args.shuffle_buffer	)
	_UpperCAmelCase      :							str       =   DataLoader(_lowerCAmelCase,					batch_size=args.train_batch_size,					shuffle=_lowerCAmelCase	)
	_UpperCAmelCase      :							Dict       =   DataLoader(_lowerCAmelCase,					batch_size=args.valid_batch_size	)
	return train_dataloader, eval_dataloader
def  UpperCamelCase   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Dict=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]	)			->     List[str]:
	_UpperCAmelCase  ,				_UpperCAmelCase      :							str       =   [], []
	for n, p in model.named_parameters():
		if any(nd in n for nd in no_decay	):
			params_without_wd.append(_lowerCAmelCase	)
		else:
			params_with_wd.append(_lowerCAmelCase	)
	return [
	    {"params": params_with_wd, "weight_decay": args.weight_decay},
	    {"params": params_without_wd, "weight_decay": 0.0},
	]
def  UpperCamelCase   (     _lowerCAmelCase       :  str,					_lowerCAmelCase       :  int	)			->     Union[str, Any]:
	logger.info(f'''Step {step}: {metrics}'''	)
	if accelerator.is_main_process:
		accelerator.log(_lowerCAmelCase,					_lowerCAmelCase	)
def  UpperCamelCase   (     _lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  List[str]	)			->     Union[str, Any]:
	# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
	_UpperCAmelCase      :							List[str]       =   accelerator.unwrap_model(_lowerCAmelCase	).config
	_UpperCAmelCase      :							int       =   4 if args.gradient_checkpointing else 3
	_UpperCAmelCase      :							List[Any]       =   args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
	_UpperCAmelCase      :							Any       =   24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
	_UpperCAmelCase      :							Optional[Any]       =   factor * (
	    1.0
	    + (args.seq_length / (6.0 * config_model.n_embd))
	    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
	)
	_UpperCAmelCase      :							Dict       =   flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
	return tflops
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int]	)			->     int:
	model.eval()
	_UpperCAmelCase      :							str       =   []
	for step, batch in enumerate(_lowerCAmelCase	):
		with torch.no_grad():
			_UpperCAmelCase      :							Union[str, Any]       =   model(_lowerCAmelCase,					labels=_lowerCAmelCase	)
		_UpperCAmelCase      :							Optional[Any]       =   outputs.loss.repeat(args.valid_batch_size	)
		losses.append(accelerator.gather(_lowerCAmelCase	)	)
		if args.max_eval_steps > 0 and step >= args.max_eval_steps:
			break
	_UpperCAmelCase      :							Any       =   torch.cat(_lowerCAmelCase	)
	_UpperCAmelCase      :							Optional[Any]       =   losses[: eval_dataloader.dataset.current_size].mean()
	try:
		_UpperCAmelCase      :							List[str]       =   torch.exp(_lowerCAmelCase	)
	except OverflowError:
		_UpperCAmelCase      :							List[str]       =   float("""inf"""	)
	return loss.item(), perplexity.item()


# Settings
lowerCamelCase__			:					Optional[Any]							  =					HfArgumentParser(TrainingArguments)
lowerCamelCase__			:					List[Any]							  =					parser.parse_args()

# Accelerator
lowerCamelCase__			:					Dict							  =					ProjectConfiguration(project_dir=args.save_dir, logging_dir='''log''')
lowerCamelCase__			:					List[str]							  =					Accelerator(log_with=['''wandb''', '''tensorboard'''], project_config=config)
lowerCamelCase__			:					str							  =					{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

lowerCamelCase__			:					Tuple							  =					Namespace(**vars(args), **acc_state)
lowerCamelCase__			:					Dict							  =					accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
						lowerCamelCase__			:					List[Any]							  =					Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowerCamelCase__  ,					lowerCamelCase__			:					Optional[int]							  =					setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
						hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
lowerCamelCase__			:					int							  =					AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
						model.gradient_checkpointing_enable()
lowerCamelCase__			:					str							  =					AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase__  ,					lowerCamelCase__			:					Optional[int]							  =					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase__			:					Dict							  =					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase__			:					int							  =					get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)
def  UpperCamelCase   (     )			->     Any:
	return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			:					str							  =					accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
												accelerator.load_state(args.resume_from_checkpoint)
												lowerCamelCase__			:					Tuple							  =					os.path.basename(args.resume_from_checkpoint)
						else:
												# Get the most recent checkpoint
												lowerCamelCase__			:					List[str]							  =					[f.name for f in os.scandir(args.save_dir) if f.is_dir() and '''step''' in str(f)]
												dirs.sort(key=os.path.getctime)
												lowerCamelCase__			:					Union[str, Any]							  =					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract the step of the checkpoint to continue from there
						lowerCamelCase__			:					Union[str, Any]							  =					os.path.splitext(path)[0]
						lowerCamelCase__			:					Any							  =					int(training_difference.replace('''step_''', ''''''))

# Train model
model.train()
lowerCamelCase__			:					List[Any]							  =					0
lowerCamelCase__			:					List[str]							  =					time.time()
lowerCamelCase__			:					str							  =					0
for step, batch in enumerate(train_dataloader, start=1):
						if args.resume_from_checkpoint and step < resume_step:
												continue  # we need to skip steps until we reach the resumed step
						lowerCamelCase__			:					Union[str, Any]							  =					model(batch, labels=batch, use_cache=False).loss
						lowerCamelCase__			:					List[str]							  =					accelerator.gather(loss.repeat(args.train_batch_size)).mean()
						loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
						log_metrics(step, {'''samples''': step * samples_per_step, '''loss_per_step/train''': loss.item()})
						lowerCamelCase__			:					Union[str, Any]							  =					loss / args.gradient_accumulation_steps
						if step % args.gradient_accumulation_steps != 0:
												# Prevent backward from doing gradient all_reduce in every step
												if accelerator.distributed_type == DistributedType.MULTI_GPU:
																		with model.no_sync():
																								accelerator.backward(loss)
												else:
																		accelerator.backward(loss)
						else:
												lowerCamelCase__			:					str							  =					get_lr()
												accelerator.backward(loss)
												accelerator.clip_grad_norm_(model.parameters(), 1.0)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
												lowerCamelCase__			:					Optional[Any]							  =					time.time() - t_start
												lowerCamelCase__			:					List[str]							  =					compute_tflops(elapsed_time, accelerator, args)
												log_metrics(
												    step,
												    {
												        '''steps''': completed_steps,
												        '''loss/train''': loss_tracking,
												        '''lr''': lr,
												        '''tflops''': tflops,
												        '''time_per_iteration''': elapsed_time,
												    },
												)
												lowerCamelCase__			:					Optional[int]							  =					time.time()
												lowerCamelCase__			:					Optional[int]							  =					0
												completed_steps += 1
						if step % args.save_checkpoint_steps == 0:
												logger.info('''Evaluating and saving model checkpoint''')
												lowerCamelCase__  ,					lowerCamelCase__			:					Union[str, Any]							  =					evaluate(args)
												log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
												accelerator.wait_for_everyone()
												lowerCamelCase__			:					List[Any]							  =					os.path.join(args.save_dir, F'''step_{step}''')
												accelerator.save_state(save_dir)
												if accelerator.is_main_process:
																		hf_repo.push_to_hub(commit_message=F'''step {step}''')
												model.train()
						if completed_steps >= args.max_train_steps:
												break

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
lowerCamelCase__  ,					lowerCamelCase__			:					List[str]							  =					evaluate(args)
log_metrics(step, {'''loss/eval''': eval_loss, '''perplexity''': perplexity})
accelerator.wait_for_everyone()
lowerCamelCase__			:					Tuple							  =					accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCamelCase__			:					Union[str, Any]							  =					os.path.join(args.save_dir, F'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message='''final model''')








"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class       _UpperCAmelCase     (						__a      ,       unittest.TestCase):
			__a	:    List[Any]			   =							KandinskyPriorPipeline
			__a	:    List[Any]			   =							["""prompt"""]
			__a	:    List[str]			   =							["""prompt""", """negative_prompt"""]
			__a	:    Tuple			   =							[
			    """num_images_per_prompt""",
			    """generator""",
			    """num_inference_steps""",
			    """latents""",
			    """negative_prompt""",
			    """guidance_scale""",
			    """output_type""",
			    """return_dict""",
			]
			__a	:    str			   =							False
			@property
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				return 32
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				return self.time_input_dim
			@property
			def 							__snake_case	(						self       )      ->      Optional[Any]:






				'''simple docstring'''







				return self.time_input_dim * 4
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				return 1_00
			@property
			def 							__snake_case	(						self       )      ->      Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
				return tokenizer
			@property
			def 							__snake_case	(						self       )      ->      int:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							List[Any]       =   CLIPTextConfig(
				    bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=self.text_embedder_hidden_size ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						layer_norm_eps=1e-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=10_00 ,						)
				return CLIPTextModelWithProjection(_A       )
			@property
			def 							__snake_case	(						self       )      ->      List[Any]:






				'''simple docstring'''







				torch.manual_seed(0       )

				_UpperCAmelCase      :							Optional[int]       =   {
				    """num_attention_heads""": 2,
				    """attention_head_dim""": 12,
				    """embedding_dim""": self.text_embedder_hidden_size,
				    """num_layers""": 1,
				}

				_UpperCAmelCase      :							List[Any]       =   PriorTransformer(**_A       )
				# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
				_UpperCAmelCase      :							List[Any]       =   nn.Parameter(torch.ones(model.clip_std.shape       )       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				torch.manual_seed(0       )
				_UpperCAmelCase      :							Optional[Any]       =   CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size ,						image_size=2_24 ,						projection_dim=self.text_embedder_hidden_size ,						intermediate_size=37 ,						num_attention_heads=4 ,						num_channels=3 ,						num_hidden_layers=5 ,						patch_size=14 ,						)

				_UpperCAmelCase      :							Tuple       =   CLIPVisionModelWithProjection(_A       )
				return model
			@property
			def 							__snake_case	(						self       )      ->      List[str]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   CLIPImageProcessor(
				    crop_size=2_24 ,						do_center_crop=_A ,						do_normalize=_A ,						do_resize=_A ,						image_mean=[0.48145466, 0.4578275, 0.40821073] ,						image_std=[0.26862954, 0.26130258, 0.27577711] ,						resample=3 ,						size=2_24 ,						)

				return image_processor
			def 							__snake_case	(						self       )      ->      Dict:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[int]       =   self.dummy_prior
				_UpperCAmelCase      :							int       =   self.dummy_image_encoder
				_UpperCAmelCase      :							int       =   self.dummy_text_encoder
				_UpperCAmelCase      :							str       =   self.dummy_tokenizer
				_UpperCAmelCase      :							Any       =   self.dummy_image_processor

				_UpperCAmelCase      :							Any       =   UnCLIPScheduler(
				    variance_type="""fixed_small_log""" ,						prediction_type="""sample""" ,						num_train_timesteps=10_00 ,						clip_sample=_A ,						clip_sample_range=10.0 ,						)

				_UpperCAmelCase      :							Any       =   {
				    """prior""": prior,
				    """image_encoder""": image_encoder,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """scheduler""": scheduler,
				    """image_processor""": image_processor,
				}

				return components
			def 							__snake_case	(						self ,						_A ,						_A=0       )      ->      Optional[Any]:






				'''simple docstring'''







				if str(_A       ).startswith("""mps"""       ):
					_UpperCAmelCase      :							Any       =   torch.manual_seed(_A       )
				else:
					_UpperCAmelCase      :							Optional[int]       =   torch.Generator(device=_A       ).manual_seed(_A       )
				_UpperCAmelCase      :							Union[str, Any]       =   {
				    """prompt""": """horse""",
				    """generator""": generator,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs
			def 							__snake_case	(						self       )      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[str]       =   """cpu"""

				_UpperCAmelCase      :							int       =   self.get_dummy_components()

				_UpperCAmelCase      :							Optional[Any]       =   self.pipeline_class(**_A       )
				_UpperCAmelCase      :							List[Any]       =   pipe.to(_A       )

				pipe.set_progress_bar_config(disable=_A       )

				_UpperCAmelCase      :							str       =   pipe(**self.get_dummy_inputs(_A       )       )
				_UpperCAmelCase      :							Any       =   output.image_embeds

				_UpperCAmelCase      :							Optional[int]       =   pipe(
				    **self.get_dummy_inputs(_A       ) ,						return_dict=_A ,						)[0]

				_UpperCAmelCase      :							int       =   image[0, -10:]
				_UpperCAmelCase      :							Optional[Any]       =   image_from_tuple[0, -10:]

				assert image.shape == (1, 32)

				_UpperCAmelCase      :							Optional[Any]       =   np.array(
				    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
			@skip_mps
			def 							__snake_case	(						self       )      ->      Tuple:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   torch_device == """cpu"""
				_UpperCAmelCase      :							Optional[Any]       =   True
				_UpperCAmelCase      :							List[Any]       =   False

				self._test_inference_batch_single_identical(
				    test_max_difference=_A ,						relax_max_difference=_A ,						test_mean_pixel_difference=_A ,						)
			@skip_mps
			def 							__snake_case	(						self       )      ->      Any:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   torch_device == """cpu"""
				_UpperCAmelCase      :							List[str]       =   False

				self._test_attention_slicing_forward_pass(
				    test_max_difference=_A ,						test_mean_pixel_difference=_A ,						)





