code: 
  
  
  """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()
  
  __SCREAMING_SNAKE_CASE     :   Tuple					=						logging.get_logger(__name__)
  
  
  
  def 						__a      (  a,		a = True					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				{}
         for name in model.attn_processors.keys():
                _a          =				name.endswith("attn2.processor"					) and not (
                    name.startswith("transformer_in"					) or "temp_attentions" in name.split("."					)
                )
                _a          =				model.config.cross_attention_dim if has_cross_attention else None
                if name.startswith("mid_block"					):
                       _a          =				model.config.block_out_channels[-1]
                elif name.startswith("up_blocks"					):
                       _a          =				int(name[len("up_blocks."					)]					)
                       _a          =				list(reversed(model.config.block_out_channels					)					)[block_id]
                elif name.startswith("down_blocks"					):
                       _a          =				int(name[len("down_blocks."					)]					)
                       _a          =				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
                       _a          =				8 * model.config.attention_head_dim
  
                _a          =				LoRAAttnProcessor(hidden_size=_A,		cross_attention_dim=_A					)
                _a          =				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		(					_UpperCAmelCase				,       _UpperCAmelCase				,       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Optional[int]     	=			UNetaDConditionModel
     lowerCAmelCase_				:   Optional[int]     	=			'sample'
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            _a          =				4
            _a          =				4
            _a          =				4
            _a          =				(32, 32)
  
            _a          =				floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(UpperCamelCase__       )
            _a          =				torch.tensor([10]       ).to(UpperCamelCase__       )
            _a          =				floats_tensor((batch_size, 4, 32)       ).to(UpperCamelCase__       )
  
            return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            return (4, 4, 32, 32)
  
  
  
  
     @property
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            return (4, 4, 32, 32)
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            _a          =				{
                "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,
            }
            _a          =				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							:Tuple       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
            _a          =				self.model_class(**UpperCamelCase__       )
  
            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							:List[str]       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				32
  
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
            model.eval()
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       )
  
                   if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                          _a          =				output.sample
  
            self.assertIsNotNone(UpperCamelCase__       )
            _a          =				inputs_dict["sample"].shape
            self.assertEqual(output.shape				,  UpperCamelCase__				,  "Input and output shapes do not match"       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
            model.eval()
  
            with torch.no_grad():
                   # Warmup pass when using mps (see #372)
                   if torch_device == "mps" and isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                          model(**self.dummy_input       )
  
                   _a          =				model(**UpperCamelCase__       )
                   if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                          _a          =				first.sample
  
                   _a          =				model(**UpperCamelCase__       )
                   if isinstance(UpperCamelCase__				,  UpperCamelCase__       ):
                          _a          =				second.sample
  
            _a          =				first.cpu().numpy()
            _a          =				second.cpu().numpy()
            _a          =				out_a[~np.isnan(UpperCamelCase__       )]
            _a          =				out_a[~np.isnan(UpperCamelCase__       )]
            _a          =				np.amax(np.abs(out_a - out_a       )       )
            self.assertLessEqual(UpperCamelCase__				,  1E-5       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				8
  
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
            model.eval()
  
            model.set_attention_slice("auto"       )
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       )
            assert output is not None
  
            model.set_attention_slice("max"       )
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       )
            assert output is not None
  
            model.set_attention_slice(2       )
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       )
            assert output is not None
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				8
  
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       ).sample
  
            _a          =				create_lora_layers(UpperCamelCase__       )
  
            # make sure we can set a list of attention processors
            model.set_attn_processor(UpperCamelCase__       )
            model.to(UpperCamelCase__       )
  
            # test that attn processors can be set to itself
            model.set_attn_processor(model.attn_processors       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__				,  cross_attention_kwargs={"scale": 0.0}       ).sample
                   _a          =				model(**UpperCamelCase__				,  cross_attention_kwargs={"scale": 0.5}       ).sample
                   _a          =				model(**UpperCamelCase__				,  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							:List[Any]       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				8
  
            torch.manual_seed(0       )
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       ).sample
  
            _a          =				create_lora_layers(UpperCamelCase__       )
            model.set_attn_processor(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__				,  cross_attention_kwargs={"scale": 0.5}       ).sample
  
            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(UpperCamelCase__       )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
                   torch.manual_seed(0       )
                   _a          =				self.model_class(**UpperCamelCase__       )
                   new_model.to(UpperCamelCase__       )
                   new_model.load_attn_procs(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				new_model(**UpperCamelCase__				,  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							:Union[str, Any]       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				8
  
            torch.manual_seed(0       )
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       ).sample
  
            _a          =				create_lora_layers(UpperCamelCase__       )
            model.set_attn_processor(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__				,  cross_attention_kwargs={"scale": 0.5}       ).sample
  
            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(UpperCamelCase__				,  safe_serialization=UpperCamelCase__       )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.safetensors"       )       )       )
                   torch.manual_seed(0       )
                   _a          =				self.model_class(**UpperCamelCase__       )
                   new_model.to(UpperCamelCase__       )
                   new_model.load_attn_procs(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				new_model(**UpperCamelCase__				,  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							:str       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				8
  
            torch.manual_seed(0       )
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
  
            _a          =				create_lora_layers(UpperCamelCase__				,  mock_weights=UpperCamelCase__       )
            model.set_attn_processor(UpperCamelCase__       )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(UpperCamelCase__       )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
                   torch.manual_seed(0       )
                   _a          =				self.model_class(**UpperCamelCase__       )
                   new_model.to(UpperCamelCase__       )
                   new_model.load_attn_procs(UpperCamelCase__				,  weight_name="pytorch_lora_weights.bin"       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				8
  
            torch.manual_seed(0       )
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
  
            _a          =				create_lora_layers(UpperCamelCase__				,  mock_weights=UpperCamelCase__       )
            model.set_attn_processor(UpperCamelCase__       )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_attn_procs(UpperCamelCase__       )
                   self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase__				,  "pytorch_lora_weights.bin"       )       )       )
                   torch.manual_seed(0       )
                   _a          =				self.model_class(**UpperCamelCase__       )
                   new_model.to(UpperCamelCase__       )
                   with self.assertRaises(UpperCamelCase__       ) as e:
                          new_model.load_attn_procs(UpperCamelCase__				,  use_safetensors=UpperCamelCase__       )
                   self.assertIn("Error no file named pytorch_lora_weights.safetensors"				,  str(e.exception       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				8
  
            torch.manual_seed(0       )
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       ).sample
  
            _a          =				create_lora_layers(UpperCamelCase__       )
            model.set_attn_processor(UpperCamelCase__       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__				,  cross_attention_kwargs={"scale": 0.0}       ).sample
  
            model.set_attn_processor(AttnProcessor()       )
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       ).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							:Optional[Any]       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
  
            _a          =				4
  
            torch.manual_seed(0       )
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
            _a          =				create_lora_layers(UpperCamelCase__       )
            model.set_attn_processor(UpperCamelCase__       )
  
            # default
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       ).sample
  
                   model.enable_xformers_memory_efficient_attention()
                   _a          =				model(**UpperCamelCase__       ).sample
  
                   model.disable_xformers_memory_efficient_attention()
                   _a          =				model(**UpperCamelCase__       ).sample
  
            assert (sample - on_sample).abs().max() < 1E-4
            assert (sample - off_sample).abs().max() < 1E-4
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            _a   , _a          =				self.prepare_init_args_and_inputs_for_common()
            _a          =				32
  
            _a          =				self.model_class(**UpperCamelCase__       )
            model.to(UpperCamelCase__       )
            model.eval()
  
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       )[0]
  
            model.enable_forward_chunking()
            with torch.no_grad():
                   _a          =				model(**UpperCamelCase__       )[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.
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						{
      """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
      # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
  }
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Tuple     	=			'qdqbert'
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:List[Any]=30_522				,  UpperCamelCase__							:Optional[int]=768				,  UpperCamelCase__							:str=12				,  UpperCamelCase__							:Tuple=12				,  UpperCamelCase__							:Any=3_072				,  UpperCamelCase__							:Union[str, Any]="gelu"				,  UpperCamelCase__							:Dict=0.1				,  UpperCamelCase__							:Any=0.1				,  UpperCamelCase__							:Dict=512				,  UpperCamelCase__							:Optional[int]=2				,  UpperCamelCase__							:Any=0.02				,  UpperCamelCase__							:Optional[int]=1E-12				,  UpperCamelCase__							:str=True				,  UpperCamelCase__							:str=1				,  UpperCamelCase__							:List[Any]=0				,  UpperCamelCase__							:List[str]=2				,  **UpperCamelCase__							:Optional[Any]				,  ):
            super().__init__(pad_token_id=UpperCamelCase__				,  bos_token_id=UpperCamelCase__				,  eos_token_id=UpperCamelCase__				,  **UpperCamelCase__       )
  
            _a          =				vocab_size
            _a          =				max_position_embeddings
            _a          =				hidden_size
            _a          =				num_hidden_layers
            _a          =				num_attention_heads
            _a          =				intermediate_size
            _a          =				hidden_act
            _a          =				hidden_dropout_prob
            _a          =				attention_probs_dropout_prob
            _a          =				initializer_range
            _a          =				type_vocab_size
            _a          =				layer_norm_eps
            _a          =				use_cache
  