code: 
  '''simple docstring'''
  
  
  
  from collections import OrderedDict
  from typing import Any, Mapping, Optional
  
  from ... import PreTrainedTokenizer
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
  from ...onnx.utils import compute_effective_axis_dimension
  from ...utils import TensorType, is_torch_available, logging
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
      # See all MBART models at https://huggingface.co/models?filter=mbart
  }
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							Tuple       		=  """mbart"""
  			lowerCamelCase							:							List[Any]       		=  ["""past_key_values"""]
  			lowerCamelCase							:							List[Any]       		=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  			def __init__(					self       ,     lowerCamelCase__=50_265       ,     lowerCamelCase__=1_024       ,     lowerCamelCase__=12       ,     lowerCamelCase__=4_096       ,     lowerCamelCase__=16       ,     lowerCamelCase__=12       ,     lowerCamelCase__=4_096       ,     lowerCamelCase__=16       ,     lowerCamelCase__=0.0       ,     lowerCamelCase__=0.0       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=1_024       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.0       ,     lowerCamelCase__=0.0       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=0.0       ,     lowerCamelCase__=False       ,     lowerCamelCase__=1       ,     lowerCamelCase__=0       ,     lowerCamelCase__=2       ,     lowerCamelCase__=2       ,     **lowerCamelCase__       ,     )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       vocab_size
  									lowercase__							 =       max_position_embeddings
  									lowercase__							 =       d_model
  									lowercase__							 =       encoder_ffn_dim
  									lowercase__							 =       encoder_layers
  									lowercase__							 =       encoder_attention_heads
  									lowercase__							 =       decoder_ffn_dim
  									lowercase__							 =       decoder_layers
  									lowercase__							 =       decoder_attention_heads
  									lowercase__							 =       dropout
  									lowercase__							 =       attention_dropout
  									lowercase__							 =       activation_dropout
  									lowercase__							 =       activation_function
  									lowercase__							 =       init_std
  									lowercase__							 =       encoder_layerdrop
  									lowercase__							 =       decoder_layerdrop
  									lowercase__							 =       classifier_dropout
  									lowercase__							 =       use_cache
  									lowercase__							 =       encoder_layers
  									lowercase__							 =       scale_embedding  # scale factor will be sqrt(d_model) if True
  									super().__init__(
  									    pad_token_id=lowerCamelCase__       ,     bos_token_id=lowerCamelCase__       ,     eos_token_id=lowerCamelCase__       ,     is_encoder_decoder=lowerCamelCase__       ,     forced_eos_token_id=lowerCamelCase__       ,     **lowerCamelCase__       ,     )
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			@property
  			def A__ (					self       )			->		Mapping[str, Mapping[int, str]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self.task in ["default", "seq2seq-lm"]:
  															lowercase__							 =       OrderedDict(
  															    [
  															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
  															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
  															    ]       )
  
  															if self.use_past:
  																					lowercase__							 =       {0: """batch"""}
  																					lowercase__							 =       {0: """batch""", 1: """past_decoder_sequence + sequence"""}
  															else:
  																					lowercase__							 =       {0: """batch""", 1: """decoder_sequence"""}
  																					lowercase__							 =       {0: """batch""", 1: """decoder_sequence"""}
  
  															if self.use_past:
  																					self.fill_with_past_key_values_(lowerCamelCase__       ,     direction="""inputs"""       )
  									elif self.task == "causal-lm":
  															# TODO: figure this case out.
  															lowercase__							 =       OrderedDict(
  															    [
  															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
  															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
  															    ]       )
  															if self.use_past:
  																					lowercase__ ,						lowercase__							 =       self.num_layers
  																					for i in range(lowerCamelCase__       ):
  																											lowercase__							 =       {0: """batch""", 2: """past_sequence + sequence"""}
  																											lowercase__							 =       {0: """batch""", 2: """past_sequence + sequence"""}
  									else:
  															lowercase__							 =       OrderedDict(
  															    [
  															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
  															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
  															        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
  															        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
  															    ]       )
  
  									return common_inputs
  
  
  
  			@property
  			def A__ (					self       )			->		Mapping[str, Mapping[int, str]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self.task in ["default", "seq2seq-lm"]:
  															lowercase__							 =       super().outputs
  									else:
  															lowercase__							 =       super(lowerCamelCase__       ,     self       ).outputs
  															if self.use_past:
  																					lowercase__ ,						lowercase__							 =       self.num_layers
  																					for i in range(lowerCamelCase__       ):
  																											lowercase__							 =       {0: """batch""", 2: """past_sequence + sequence"""}
  																											lowercase__							 =       {0: """batch""", 2: """past_sequence + sequence"""}
  									return common_outputs
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     )			->		Mapping[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									# Generate decoder inputs
  									lowercase__							 =       seq_length if not self.use_past else 1
  									lowercase__							 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
  									lowercase__							 =       dict(**lowerCamelCase__       ,     **lowerCamelCase__       )
  
  									if self.use_past:
  															if not is_torch_available():
  																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
  															else:
  																					import torch
  															lowercase__ ,						lowercase__							 =       common_inputs["""input_ids"""].shape
  															lowercase__							 =       common_inputs["""decoder_input_ids"""].shape[1]
  															lowercase__ ,						lowercase__							 =       self.num_attention_heads
  															lowercase__							 =       (
  															    batch,
  															    num_encoder_attention_heads,
  															    encoder_seq_length,
  															    self._config.hidden_size // num_encoder_attention_heads,
  															)
  															lowercase__							 =       decoder_seq_length + 3
  															lowercase__							 =       (
  															    batch,
  															    num_decoder_attention_heads,
  															    decoder_past_length,
  															    self._config.hidden_size // num_decoder_attention_heads,
  															)
  
  															lowercase__							 =       torch.cat(
  															    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase__       ,     lowerCamelCase__       )]       ,     dim=1       )
  
  															lowercase__							 =       []
  															# If the number of encoder and decoder layers are present in the model configuration, both are considered
  															lowercase__ ,						lowercase__							 =       self.num_layers
  															lowercase__							 =       min(lowerCamelCase__       ,     lowerCamelCase__       )
  															lowercase__							 =       max(lowerCamelCase__       ,     lowerCamelCase__       ) - min_num_layers
  															lowercase__							 =       """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
  
  															for _ in range(lowerCamelCase__       ):
  																					common_inputs["past_key_values"].append(
  																					    (
  																					        torch.zeros(lowerCamelCase__       ),
  																					        torch.zeros(lowerCamelCase__       ),
  																					        torch.zeros(lowerCamelCase__       ),
  																					        torch.zeros(lowerCamelCase__       ),
  																					    )       )
  															# TODO: test this.
  															lowercase__							 =       encoder_shape if remaining_side_name == """encoder""" else decoder_shape
  															for _ in range(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase__       ), torch.zeros(lowerCamelCase__       ))       )
  									return common_inputs
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     )			->		Mapping[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  									    lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									if self.use_past:
  															if not is_torch_available():
  																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
  															else:
  																					import torch
  															lowercase__ ,						lowercase__							 =       common_inputs["""input_ids"""].shape
  															# Not using the same length for past_key_values
  															lowercase__							 =       seqlen + 2
  															lowercase__ ,						lowercase__							 =       self.num_layers
  															lowercase__ ,						lowercase__							 =       self.num_attention_heads
  															lowercase__							 =       (
  															    batch,
  															    num_encoder_attention_heads,
  															    past_key_values_length,
  															    self._config.hidden_size // num_encoder_attention_heads,
  															)
  
  															lowercase__							 =       common_inputs["""attention_mask"""].dtype
  															lowercase__							 =       torch.cat(
  															    [common_inputs["""attention_mask"""], torch.ones(lowerCamelCase__       ,     lowerCamelCase__       ,     dtype=lowerCamelCase__       )]       ,     dim=1       )
  															lowercase__							 =       [
  															    (torch.zeros(lowerCamelCase__       ), torch.zeros(lowerCamelCase__       )) for _ in range(lowerCamelCase__       )
  															]
  									return common_inputs
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     )			->		Mapping[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       compute_effective_axis_dimension(
  									    lowerCamelCase__       ,     fixed_dimension=OnnxConfig.default_fixed_batch       ,     num_token_to_add=0       )
  
  									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  									lowercase__							 =       tokenizer.num_special_tokens_to_add(lowerCamelCase__       )
  									lowercase__							 =       compute_effective_axis_dimension(
  									    lowerCamelCase__       ,     fixed_dimension=OnnxConfig.default_fixed_sequence       ,     num_token_to_add=lowerCamelCase__       )
  
  									# Generate dummy inputs according to compute batch and sequence
  									lowercase__							 =       [""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
  									lowercase__							 =       dict(tokenizer(lowerCamelCase__       ,     return_tensors=lowerCamelCase__       )       )
  									return common_inputs
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     )			->		Mapping[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self.task in ["default", "seq2seq-lm"]:
  															lowercase__							 =       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  															    lowerCamelCase__       ,     batch_size=lowerCamelCase__       ,     seq_length=lowerCamelCase__       ,     is_pair=lowerCamelCase__       ,     framework=lowerCamelCase__       )
  
  									elif self.task == "causal-lm":
  															lowercase__							 =       self._generate_dummy_inputs_for_causal_lm(
  															    lowerCamelCase__       ,     batch_size=lowerCamelCase__       ,     seq_length=lowerCamelCase__       ,     is_pair=lowerCamelCase__       ,     framework=lowerCamelCase__       )
  									else:
  															lowercase__							 =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  															    lowerCamelCase__       ,     batch_size=lowerCamelCase__       ,     seq_length=lowerCamelCase__       ,     is_pair=lowerCamelCase__       ,     framework=lowerCamelCase__       )
  
  									return common_inputs
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self.task in ["default", "seq2seq-lm"]:
  															lowercase__							 =       super()._flatten_past_key_values_(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									else:
  															lowercase__							 =       super(lowerCamelCase__       ,     self       )._flatten_past_key_values_(
  															    lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  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()
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__ = True	):
  						lowercase__							 =       {}
  						for name in model.attn_processors.keys():
  												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
  												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
  												)
  												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
  												if name.startswith("""mid_block"""	):
  																		lowercase__							 =       model.config.block_out_channels[-1]
  												elif name.startswith("""up_blocks"""	):
  																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
  																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
  												elif name.startswith("""down_blocks"""	):
  																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
  																		lowercase__							 =       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
  																		lowercase__							 =       8 * model.config.attention_head_dim
  
  												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
  												lowercase__							 =       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 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
  			lowerCamelCase							:							int       		=  """sample"""
  
  
  
  			@property
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       4
  									lowercase__							 =       (32, 32)
  
  									lowercase__							 =       floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowerCamelCase__       )
  									lowercase__							 =       torch.tensor([10]       ).to(lowerCamelCase__       )
  									lowercase__							 =       floats_tensor((batch_size, 4, 32)       ).to(lowerCamelCase__       )
  
  									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  			@property
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (4, 4, 32, 32)
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       {
  									    """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,
  									}
  									lowercase__							 =       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 A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  
  									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 A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       output.sample
  
  									self.assertIsNotNone(lowerCamelCase__       )
  									lowercase__							 =       inputs_dict["""sample"""].shape
  									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															# Warmup pass when using mps (see #372)
  															if torch_device == "mps" and isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					model(**self.dummy_input       )
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       first.sample
  
  															lowercase__							 =       model(**lowerCamelCase__       )
  															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  																					lowercase__							 =       second.sample
  
  									lowercase__							 =       first.cpu().numpy()
  									lowercase__							 =       second.cpu().numpy()
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       out_a[~np.isnan(lowerCamelCase__       )]
  									lowercase__							 =       np.amax(np.abs(out_a - out_a       )       )
  									self.assertLessEqual(lowerCamelCase__       ,     1e-5       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									model.set_attention_slice("""auto"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice("""max"""       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  									model.set_attention_slice(2       )
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )
  									assert output is not None
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  
  									# make sure we can set a list of attention processors
  									model.set_attn_processor(lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									# test that attn processors can be set to itself
  									model.set_attn_processor(model.attn_processors       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  															lowercase__							 =       model(**lowerCamelCase__       ,     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 A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     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 A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
  
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       ,     safe_serialization=lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.safetensors"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       new_model(**lowerCamelCase__       ,     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 A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       ,     mock_weights=lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  									# Saving as torch, properly reloads with directly filename
  									with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(lowerCamelCase__       )
  															self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase__       ,     """pytorch_lora_weights.bin"""       )       )       )
  															torch.manual_seed(0       )
  															lowercase__							 =       self.model_class(**lowerCamelCase__       )
  															new_model.to(lowerCamelCase__       )
  															with self.assertRaises(lowerCamelCase__       ) as e:
  																					new_model.load_attn_procs(lowerCamelCase__       ,     use_safetensors=lowerCamelCase__       )
  															self.assertIn("""Error no file named pytorch_lora_weights.safetensors"""       ,     str(e.exception       )       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       8
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
  
  									model.set_attn_processor(AttnProcessor()       )
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).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 A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  
  									lowercase__							 =       4
  
  									torch.manual_seed(0       )
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
  									model.set_attn_processor(lowerCamelCase__       )
  
  									# default
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.enable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  															model.disable_xformers_memory_efficient_attention()
  															lowercase__							 =       model(**lowerCamelCase__       ).sample
  
  									assert (sample - on_sample).abs().max() < 1e-4
  									assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
  									lowercase__							 =       32
  
  									lowercase__							 =       self.model_class(**lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[0]
  
  									model.enable_forward_chunking()
  									with torch.no_grad():
  															lowercase__							 =       model(**lowerCamelCase__       )[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.
  
  
  
  
  
  
  