code: 
  import tempfile
  import unittest
  
  import numpy as np
  
  import transformers
  from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
  from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_flax_available():
  				import jax
  				import jax.numpy as jnp
  
  				from transformers.modeling_flax_pytorch_utils import (
  				    convert_pytorch_state_dict_to_flax,
  				    load_flax_weights_in_pytorch_model,
  				)
  				from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel
  
  if is_torch_available():
  				import torch
  
  
  
  
  
  class    _snake_case      :
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a=14		,     _a=7		,     _a=True		,     _a=True		,     _a=False		,     _a=True		,     _a=99		,     _a=32		,     _a=4		,     _a=4		,     _a=[[["global", "local"], 2]]		,     _a=37		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=7		,     _a=0.02		,     ):
  				__magic_name__	:	Dict			     =  parent
  				__magic_name__	:	Optional[Any]			     =  batch_size
  				__magic_name__	:	Optional[int]			     =  seq_length
  				__magic_name__	:	int			     =  is_training
  				__magic_name__	:	Any			     =  use_input_mask
  				__magic_name__	:	Any			     =  use_token_type_ids
  				__magic_name__	:	Optional[int]			     =  use_labels
  				__magic_name__	:	Dict			     =  vocab_size
  				__magic_name__	:	Dict			     =  hidden_size
  				__magic_name__	:	Union[str, Any]			     =  num_hidden_layers
  				__magic_name__	:	List[str]			     =  num_attention_heads
  				__magic_name__	:	str			     =  attention_types
  				__magic_name__	:	Optional[Any]			     =  intermediate_size
  				__magic_name__	:	List[str]			     =  hidden_act
  				__magic_name__	:	Optional[int]			     =  hidden_dropout_prob
  				__magic_name__	:	Optional[Any]			     =  attention_probs_dropout_prob
  				__magic_name__	:	Any			     =  max_position_embeddings
  				__magic_name__	:	List[str]			     =  window_size
  				__magic_name__	:	int			     =  initializer_range
  				__magic_name__	:	int			     =  None
  				__magic_name__	:	List[str]			     =  vocab_size - 1
  				__magic_name__	:	Union[str, Any]			     =  vocab_size - 1
  				__magic_name__	:	Union[str, Any]			     =  vocab_size - 1
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	int			     =  ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size	)
  
  				__magic_name__	:	List[str]			     =  None
  				if self.use_input_mask:
  					__magic_name__	:	Optional[Any]			     =  random_attention_mask([self.batch_size, self.seq_length]	)
  
  				__magic_name__	:	int			     =  GPTNeoConfig(
  				    vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_layers=self.num_hidden_layers		,     num_heads=self.num_attention_heads		,     max_position_embeddings=self.max_position_embeddings		,     use_cache=lowercase_		,     bos_token_id=self.bos_token_id		,     eos_token_id=self.eos_token_id		,     pad_token_id=self.pad_token_id		,     window_size=self.window_size		,     attention_types=self.attention_types		,     )
  
  				return (config, input_ids, input_mask)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  self.prepare_config_and_inputs()
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	List[str]			     =  config_and_inputs
  				__magic_name__	:	Optional[Any]			     =  {"input_ids": input_ids, "attention_mask": attention_mask}
  				return config, inputs_dict
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a	):
  				__magic_name__	:	List[str]			     =  20
  				__magic_name__	:	int			     =  model_class_name(lowercase_	)
  
  				__magic_name__	:	Union[str, Any]			     =  model.init_cache(input_ids.shape[0]		,     lowercase_	)
  				__magic_name__	:	List[Any]			     =  jnp.ones((input_ids.shape[0], max_decoder_length)		,     dtype="i4"	)
  
  				__magic_name__	:	List[str]			     =  jnp.broadcast_to(
  				    jnp.arange(input_ids.shape[-1] - 1	)[None, :]		,     (input_ids.shape[0], input_ids.shape[-1] - 1)	)
  				__magic_name__	:	Union[str, Any]			     =  model(
  				    input_ids[:, :-1]		,     attention_mask=lowercase_		,     past_key_values=lowercase_		,     position_ids=lowercase_		,     )
  
  				__magic_name__	:	List[Any]			     =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,     dtype="i4"	)
  				__magic_name__	:	Tuple			     =  model(
  				    input_ids[:, -1:]		,     attention_mask=lowercase_		,     past_key_values=outputs_cache.past_key_values		,     position_ids=lowercase_		,     )
  
  				__magic_name__	:	List[Any]			     =  model(lowercase_	)
  
  				__magic_name__	:	Optional[int]			     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
  				self.parent.assertTrue(diff < 1e-3		,     msg=f'''Max diff is {diff}'''	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a	):
  				__magic_name__	:	Union[str, Any]			     =  20
  				__magic_name__	:	List[str]			     =  model_class_name(lowercase_	)
  
  				__magic_name__	:	int			     =  jnp.concatenate(
  				    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]		,     axis=-1		,     )
  
  				__magic_name__	:	Optional[Any]			     =  model.init_cache(input_ids.shape[0]		,     lowercase_	)
  				__magic_name__	:	Tuple			     =  jnp.broadcast_to(
  				    jnp.arange(input_ids.shape[-1] - 1	)[None, :]		,     (input_ids.shape[0], input_ids.shape[-1] - 1)	)
  
  				__magic_name__	:	Optional[Any]			     =  model(
  				    input_ids[:, :-1]		,     attention_mask=lowercase_		,     past_key_values=lowercase_		,     position_ids=lowercase_		,     )
  				__magic_name__	:	Any			     =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,     dtype="i4"	)
  				__magic_name__	:	Dict			     =  model(
  				    input_ids[:, -1:]		,     past_key_values=outputs_cache.past_key_values		,     attention_mask=lowercase_		,     position_ids=lowercase_		,     )
  
  				__magic_name__	:	Tuple			     =  model(lowercase_		,     attention_mask=lowercase_	)
  
  				__magic_name__	:	Tuple			     =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
  				self.parent.assertTrue(diff < 1e-3		,     msg=f'''Max diff is {diff}'''	)
  
  
  
  
  
  
  
  @require_flax
  class    _snake_case      (  lowerCamelCase__       ,		lowerCamelCase__       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  			UpperCamelCase__					   =					(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  FlaxGPTNeoModelTester(self	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  
  
  
  
  
  
  				for model_class_name in self.all_model_classes:
  					__magic_name__     ,					__magic_name__     ,					__magic_name__	:	int			     =  self.model_tester.prepare_config_and_inputs()
  					self.model_tester.check_use_cache_forward(lowercase_		,     lowercase_		,     lowercase_		,     lowercase_	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  
  
  
  
  
  
  				for model_class_name in self.all_model_classes:
  					__magic_name__     ,					__magic_name__     ,					__magic_name__	:	int			     =  self.model_tester.prepare_config_and_inputs()
  					self.model_tester.check_use_cache_forward_with_attn_mask(
  					    lowercase_		,     lowercase_		,     lowercase_		,     lowercase_	)
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  GPTaTokenizer.from_pretrained("gpt2"		,     pad_token="<|endoftext|>"		,     padding_side="left"	)
  				__magic_name__	:	Optional[int]			     =  tokenizer(["Hello this is a long string", "Hey"]		,     return_tensors="np"		,     padding=lowercase_		,     truncation=lowercase_	)
  
  				__magic_name__	:	Optional[Any]			     =  FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"	)
  				__magic_name__	:	List[str]			     =  False
  				__magic_name__	:	Any			     =  model.config.eos_token_id
  
  				__magic_name__	:	Dict			     =  jax.jit(model.generate	)
  
  				__magic_name__	:	str			     =  jit_generate(
  				    inputs["input_ids"]		,     attention_mask=inputs["attention_mask"]		,     pad_token_id=tokenizer.pad_token_id	).sequences
  
  				__magic_name__	:	str			     =  tokenizer.batch_decode(lowercase_		,     skip_special_tokens=lowercase_	)
  
  				__magic_name__	:	List[Any]			     =  [
  				    "Hello this is a long string of text.\n\nI\'m trying to get the text of the",
  				    "Hey, I\'m a little late to the party. I\'m going to",
  				]
  
  				self.assertListEqual(lowercase_		,     lowercase_	)
  
  
  
  
  
  
  
  			@is_pt_flax_cross_test
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  					with self.subTest(model_class.__name__	):
  						# prepare inputs
  						__magic_name__	:	int			     =  self._prepare_for_class(lowercase_		,     lowercase_	)
  						__magic_name__	:	Dict			     =  {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}
  
  						# load corresponding PyTorch class
  						__magic_name__	:	Any			     =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
  						__magic_name__	:	str			     =  getattr(lowercase_		,     lowercase_	)
  
  						__magic_name__     ,					__magic_name__	:	Any			     =  pt_inputs["input_ids"].shape
  						__magic_name__	:	Tuple			     =  np.random.randint(0		,     seq_length - 1		,     size=(batch_size,)	)
  						for batch_idx, start_index in enumerate(lowercase_	):
  							__magic_name__	:	Optional[int]			     =  0
  							__magic_name__	:	List[str]			     =  1
  							__magic_name__	:	List[Any]			     =  0
  							__magic_name__	:	Optional[Any]			     =  1
  						__magic_name__	:	List[str]			     =  pt_model_class(lowercase_	).eval()
  						__magic_name__	:	Dict			     =  model_class(lowercase_		,     dtype=jnp.floataa	)
  
  						__magic_name__	:	List[Any]			     =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,     lowercase_	)
  						__magic_name__	:	Any			     =  fx_state
  
  						with torch.no_grad():
  							__magic_name__	:	Optional[Any]			     =  pt_model(**lowercase_	).to_tuple()
  
  						__magic_name__	:	Any			     =  fx_model(**lowercase_	).to_tuple()
  						self.assertEqual(len(lowercase_	)		,     len(lowercase_	)		,     "Output lengths differ between Flax and PyTorch"	)
  						for fx_output, pt_output in zip(lowercase_		,     lowercase_	):
  							self.assert_almost_equals(fx_output[:, -1]		,     pt_output[:, -1].numpy()		,     4e-2	)
  
  						with tempfile.TemporaryDirectory() as tmpdirname:
  							pt_model.save_pretrained(lowercase_	)
  							__magic_name__	:	Any			     =  model_class.from_pretrained(lowercase_		,     from_pt=lowercase_	)
  
  						__magic_name__	:	int			     =  fx_model_loaded(**lowercase_	).to_tuple()
  						self.assertEqual(
  						    len(lowercase_	)		,     len(lowercase_	)		,     "Output lengths differ between Flax and PyTorch"	)
  						for fx_output_loaded, pt_output in zip(lowercase_		,     lowercase_	):
  							self.assert_almost_equals(fx_output_loaded[:, -1]		,     pt_output[:, -1].numpy()		,     4e-2	)
  
  
  
  
  
  
  
  			@is_pt_flax_cross_test
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	str			     =  self.model_tester.prepare_config_and_inputs_for_common()
  				for model_class in self.all_model_classes:
  					with self.subTest(model_class.__name__	):
  						# prepare inputs
  						__magic_name__	:	List[str]			     =  self._prepare_for_class(lowercase_		,     lowercase_	)
  						__magic_name__	:	Dict			     =  {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}
  
  						# load corresponding PyTorch class
  						__magic_name__	:	Dict			     =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
  						__magic_name__	:	Any			     =  getattr(lowercase_		,     lowercase_	)
  
  						__magic_name__	:	List[Any]			     =  pt_model_class(lowercase_	).eval()
  						__magic_name__	:	List[str]			     =  model_class(lowercase_		,     dtype=jnp.floataa	)
  
  						__magic_name__	:	Any			     =  load_flax_weights_in_pytorch_model(lowercase_		,     fx_model.params	)
  						__magic_name__     ,					__magic_name__	:	List[str]			     =  pt_inputs["input_ids"].shape
  						__magic_name__	:	int			     =  np.random.randint(0		,     seq_length - 1		,     size=(batch_size,)	)
  						for batch_idx, start_index in enumerate(lowercase_	):
  							__magic_name__	:	Optional[Any]			     =  0
  							__magic_name__	:	Optional[int]			     =  1
  							__magic_name__	:	List[Any]			     =  0
  							__magic_name__	:	Union[str, Any]			     =  1
  
  						# make sure weights are tied in PyTorch
  						pt_model.tie_weights()
  
  						with torch.no_grad():
  							__magic_name__	:	Dict			     =  pt_model(**lowercase_	).to_tuple()
  
  						__magic_name__	:	Tuple			     =  fx_model(**lowercase_	).to_tuple()
  						self.assertEqual(len(lowercase_	)		,     len(lowercase_	)		,     "Output lengths differ between Flax and PyTorch"	)
  						for fx_output, pt_output in zip(lowercase_		,     lowercase_	):
  							self.assert_almost_equals(fx_output[:, -1]		,     pt_output[:, -1].numpy()		,     4e-2	)
  
  						with tempfile.TemporaryDirectory() as tmpdirname:
  							fx_model.save_pretrained(lowercase_	)
  							__magic_name__	:	Optional[Any]			     =  pt_model_class.from_pretrained(lowercase_		,     from_flax=lowercase_	)
  
  						with torch.no_grad():
  							__magic_name__	:	Any			     =  pt_model_loaded(**lowercase_	).to_tuple()
  
  						self.assertEqual(
  						    len(lowercase_	)		,     len(lowercase_	)		,     "Output lengths differ between Flax and PyTorch"	)
  						for fx_output, pt_output in zip(lowercase_		,     lowercase_	):
  							self.assert_almost_equals(fx_output[:, -1]		,     pt_output[:, -1].numpy()		,     4e-2	)
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  
  
  
  
  
  
  				for model_class_name in self.all_model_classes:
  					__magic_name__	:	Tuple			     =  model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"	)
  					__magic_name__	:	List[Any]			     =  model(np.ones((1, 1)	)	)
  					self.assertIsNotNone(lowercase_	)
  
  
  
  
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.
  