code: 
  import os
  from dataclasses import replace
  
  import jax
  import wandb
  from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
  from datasets import load_dataset
  from flax import jax_utils
  
  from transformers import BigBirdTokenizerFast
  
  
  if __name__ == "__main__":
  				print("#################### AVAILABLE DEVICES ####################")
  				print(jax.devices())
  				print("###########################################################")
  
  				# setup for wandb sweep
  				snake_case				:	Dict							   =						Args()
  				snake_case				:	str							   =						wandb.init(project="bigbird-natural-questions", config=args.__dict__)
  				snake_case				:	Dict							   =						dict(logger.config)
  				del wandb_args["batch_size"]
  				snake_case				:	Optional[Any]							   =						replace(args, **wandb_args)
  				snake_case				:	List[Any]							   =						args.base_dir + "-" + wandb.run.id
  				snake_case				:	Union[str, Any]							   =						replace(args, base_dir=base_dir)
  				print(args)
  
  				snake_case				:	int							   =						load_dataset("json", data_files=args.tr_data_path)["train"]
  				snake_case				:	Optional[Any]							   =						load_dataset("json", data_files=args.val_data_path)["train"]
  
  				# drop extra batch for now
  				snake_case				:	int							   =						range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
  				snake_case				:	Union[str, Any]							   =						tr_dataset.shuffle().select(indices)
  				snake_case				:	Dict							   =						range(len(val_dataset) - len(val_dataset) % args.batch_size)
  				snake_case				:	Any							   =						val_dataset.shuffle().select(indices)
  
  				if os.environ.get("TRAIN_ON_SMALL", "false") == "true":
  								snake_case				:	Tuple							   =						tr_dataset.shuffle().select(range(80_000))
  								snake_case				:	List[str]							   =						val_dataset.shuffle().select(range(8_000))
  
  				print(tr_dataset)
  				print(val_dataset)
  
  				snake_case				:	Tuple							   =						FlaxBigBirdForNaturalQuestions.from_pretrained(
  				    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
  				)
  				snake_case				:	Dict							   =						BigBirdTokenizerFast.from_pretrained(args.model_id)
  				snake_case				:	Any							   =						DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)
  
  				snake_case				:	int							   =						{
  				    "lr": args.lr,
  				    "init_lr": args.init_lr,
  				    "warmup_steps": args.warmup_steps,
  				    "num_train_steps": args.max_epochs * (len(tr_dataset) // args.batch_size),
  				    "weight_decay": args.weight_decay,
  				}
  				snake_case				:	Dict							   =						build_tx(**tx_args)
  
  				snake_case				:	Union[str, Any]							   =						Trainer(
  				    args=args,
  				    data_collator=data_collator,
  				    model_save_fn=model.save_pretrained,
  				    train_step_fn=train_step,
  				    val_step_fn=val_step,
  				    logger=logger,
  				    scheduler_fn=lr,
  				)
  
  				snake_case				:	List[str]							   =						None
  				snake_case				:	Tuple							   =						trainer.create_state(model, tx, num_train_steps=tx_args["num_train_steps"], ckpt_dir=ckpt_dir)
  				try:
  								trainer.train(state, tr_dataset, val_dataset)
  				except KeyboardInterrupt:
  								print("Oooops; TRAINING STOPPED UNFORTUNATELY")
  
  				print("SAVING WEIGHTS IN `final-weights`")
  				snake_case				:	Union[str, Any]							   =						jax_utils.unreplicate(state.params)
  				model.save_pretrained(os.path.join(args.base_dir, "final-weights"), params=params)
  
  
  
  
style_context: 
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      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()
  
  snake_case				:	int							   =						logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						bool = True     )       ->	Tuple:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[Any]			     =  {}
  	for name in model.attn_processors.keys():
  		__magic_name__	:	List[Any]			     =  name.endswith("attn2.processor"     ) and not (
  		    name.startswith("transformer_in"     ) or "temp_attentions" in name.split("."     )
  		)
  		__magic_name__	:	List[str]			     =  model.config.cross_attention_dim if has_cross_attention else None
  		if name.startswith("mid_block"     ):
  			__magic_name__	:	Tuple			     =  model.config.block_out_channels[-1]
  		elif name.startswith("up_blocks"     ):
  			__magic_name__	:	Tuple			     =  int(name[len("up_blocks."     )]     )
  			__magic_name__	:	Optional[Any]			     =  list(reversed(model.config.block_out_channels     )     )[block_id]
  		elif name.startswith("down_blocks"     ):
  			__magic_name__	:	Optional[int]			     =  int(name[len("down_blocks."     )]     )
  			__magic_name__	:	str			     =  model.config.block_out_channels[block_id]
  		elif name.startswith("transformer_in"     ):
  			# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
  			__magic_name__	:	List[Any]			     =  8 * model.config.attention_head_dim
  
  		__magic_name__	:	Union[str, Any]			     =  LoRAAttnProcessor(hidden_size=_snake_case   ,     cross_attention_dim=_snake_case     )
  		__magic_name__	:	str			     =  lora_attn_procs[name].to(model.device     )
  
  		if mock_weights:
  			# add 1 to weights to mock trained weights
  			with torch.no_grad():
  				lora_attn_procs[name].to_q_lora.up.weight += 1
  				lora_attn_procs[name].to_k_lora.up.weight += 1
  				lora_attn_procs[name].to_v_lora.up.weight += 1
  				lora_attn_procs[name].to_out_lora.up.weight += 1
  
  	return lora_attn_procs
  
  
  
  
  
  @skip_mps
  class    _snake_case      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					UNetaDConditionModel
  			UpperCamelCase__					   =					'sample'
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  4
  				__magic_name__	:	str			     =  4
  				__magic_name__	:	List[str]			     =  4
  				__magic_name__	:	Optional[int]			     =  (32, 32)
  
  				__magic_name__	:	Optional[Any]			     =  floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(_a	)
  				__magic_name__	:	Union[str, Any]			     =  torch.tensor([10]	).to(_a	)
  				__magic_name__	:	List[str]			     =  floats_tensor((batch_size, 4, 32)	).to(_a	)
  
  				return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return (4, 4, 32, 32)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  {
  				    "block_out_channels": (32, 64),
  				    "down_block_types": (
  				        "CrossAttnDownBlock3D",
  				        "DownBlock3D",
  				    ),
  				    "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
  				    "cross_attention_dim": 32,
  				    "attention_head_dim": 8,
  				    "out_channels": 4,
  				    "in_channels": 4,
  				    "layers_per_block": 1,
  				    "sample_size": 32,
  				}
  				__magic_name__	:	List[str]			     =  self.dummy_input
  				return init_dict, inputs_dict
  
  
  
  
  
  
  
  			@unittest.skipIf(
  			    torch_device != "cuda" or not is_xformers_available()		,     reason="XFormers attention is only available with CUDA and `xformers` installed"		,     )
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Any			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  
  				model.enable_xformers_memory_efficient_attention()
  
  				assert (
  				    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
  				    == "XFormersAttnProcessor"
  				), "xformers is not enabled"
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Tuple			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  32
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	)
  
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	Any			     =  output.sample
  
  				self.assertIsNotNone(_a	)
  				__magic_name__	:	Union[str, Any]			     =  inputs_dict["sample"].shape
  				self.assertEqual(output.shape		,     _a		,     "Input and output shapes do not match"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					# Warmup pass when using mps (see #372)
  					if torch_device == "mps" and isinstance(_a		,     _a	):
  						model(**self.dummy_input	)
  
  					__magic_name__	:	str			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  first.sample
  
  					__magic_name__	:	Tuple			     =  model(**_a	)
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	str			     =  second.sample
  
  				__magic_name__	:	Optional[int]			     =  first.cpu().numpy()
  				__magic_name__	:	Any			     =  second.cpu().numpy()
  				__magic_name__	:	List[Any]			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Any			     =  out_a[~np.isnan(_a	)]
  				__magic_name__	:	Tuple			     =  np.amax(np.abs(out_a - out_a	)	)
  				self.assertLessEqual(_a		,     1e-5	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Union[str, Any]			     =  8
  
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				model.set_attention_slice("auto"	)
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice("max"	)
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	)
  				assert output is not None
  
  				model.set_attention_slice(2	)
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	)
  				assert output is not None
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[Any]			     =  8
  
  				__magic_name__	:	Dict			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Union[str, Any]			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  
  				# make sure we can set a list of attention processors
  				model.set_attn_processor(_a	)
  				model.to(_a	)
  
  				# test that attn processors can be set to itself
  				model.set_attn_processor(model.attn_processors	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  					__magic_name__	:	Union[str, Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				assert (samplea - samplea).abs().max() < 3e-3
  				assert (samplea - samplea).abs().max() < 3e-3
  
  				# sample 2 and sample 3 should be different
  				assert (samplea - samplea).abs().max() > 3e-3
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Tuple			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	int			     =  new_model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				assert (sample - new_sample).abs().max() < 1e-3
  
  				# LoRA and no LoRA should NOT be the same
  				assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Optional[int]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a		,     safe_serialization=_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.safetensors"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	List[Any]			     =  new_model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
  
  				assert (sample - new_sample).abs().max() < 3e-3
  
  				# LoRA and no LoRA should NOT be the same
  				assert (sample - old_sample).abs().max() > 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Tuple			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	Any			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					new_model.load_attn_procs(_a		,     weight_name="pytorch_lora_weights.bin"	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	List[str]			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				__magic_name__	:	str			     =  create_lora_layers(_a		,     mock_weights=_a	)
  				model.set_attn_processor(_a	)
  				# Saving as torch, properly reloads with directly filename
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					model.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					torch.manual_seed(0	)
  					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
  					new_model.to(_a	)
  					with self.assertRaises(_a	) as e:
  						new_model.load_attn_procs(_a		,     use_safetensors=_a	)
  					self.assertIn("Error no file named pytorch_lora_weights.safetensors"		,     str(e.exception	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Any			     =  8
  
  				torch.manual_seed(0	)
  				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
  				model.to(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Any			     =  model(**_a	).sample
  
  				__magic_name__	:	int			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				with torch.no_grad():
  					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
  
  				model.set_attn_processor(AttnProcessor()	)
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample
  
  				assert (sample - new_sample).abs().max() < 1e-4
  				assert (sample - old_sample).abs().max() < 3e-3
  
  
  
  
  
  
  
  			@unittest.skipIf(
  			    torch_device != "cuda" or not is_xformers_available()		,     reason="XFormers attention is only available with CUDA and `xformers` installed"		,     )
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# enable deterministic behavior for gradient checkpointing
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  
  				__magic_name__	:	Optional[int]			     =  4
  
  				torch.manual_seed(0	)
  				__magic_name__	:	str			     =  self.model_class(**_a	)
  				model.to(_a	)
  				__magic_name__	:	Any			     =  create_lora_layers(_a	)
  				model.set_attn_processor(_a	)
  
  				# default
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	).sample
  
  					model.enable_xformers_memory_efficient_attention()
  					__magic_name__	:	str			     =  model(**_a	).sample
  
  					model.disable_xformers_memory_efficient_attention()
  					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample
  
  				assert (sample - on_sample).abs().max() < 1e-4
  				assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
  				__magic_name__	:	str			     =  32
  
  				__magic_name__	:	int			     =  self.model_class(**_a	)
  				model.to(_a	)
  				model.eval()
  
  				with torch.no_grad():
  					__magic_name__	:	Optional[Any]			     =  model(**_a	)[0]
  
  				model.enable_forward_chunking()
  				with torch.no_grad():
  					__magic_name__	:	int			     =  model(**_a	)[0]
  
  				self.assertEqual(output.shape		,     output_a.shape		,     "Shape doesn't match"	)
  				assert np.abs(output.cpu() - output_a.cpu()	).max() < 1e-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  