code: 
  
  
  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 : Any          =    logging.get_logger(__name__)
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: bool = True      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Dict			=	{}
  							for name in model.attn_processors.keys():
  														a_       :   Optional[Any]			=	name.endswith("""attn2.processor"""      ) and not (
  														    name.startswith("""transformer_in"""      ) or """temp_attentions""" in name.split("""."""      )
  														)
  														a_       :   str			=	model.config.cross_attention_dim if has_cross_attention else None
  														if name.startswith("""mid_block"""      ):
  																					a_       :   Union[str, Any]			=	model.config.block_out_channels[-1]
  														elif name.startswith("""up_blocks"""      ):
  																					a_       :   List[Any]			=	int(name[len("""up_blocks."""      )]      )
  																					a_       :   str			=	list(reversed(model.config.block_out_channels      )      )[block_id]
  														elif name.startswith("""down_blocks"""      ):
  																					a_       :   List[Any]			=	int(name[len("""down_blocks."""      )]      )
  																					a_       :   Any			=	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_       :   Union[str, Any]			=	8 * model.config.attention_head_dim
  
  														a_       :   List[str]			=	LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE_	,	cross_attention_dim=SCREAMING_SNAKE_CASE_      )
  														a_       :   Tuple			=	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__  ( __A      ,    __A      ,    unittest.TestCase       ):
  	UpperCAmelCase   :							Dict         = UNetaDConditionModel
  	UpperCAmelCase   :							Optional[Any]         = """sample"""
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	4
  								a_       :   List[str]			=	4
  								a_       :   int			=	4
  								a_       :   List[str]			=	(32, 32)
  
  								a_       :   List[Any]			=	floats_tensor((batch_size, num_channels, num_frames) + sizes							).to(UpperCamelCase_							)
  								a_       :   Optional[Any]			=	torch.tensor([10]							).to(UpperCamelCase_							)
  								a_       :   Tuple			=	floats_tensor((batch_size, 4, 32)							).to(UpperCamelCase_							)
  
  								return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return (4, 4, 32, 32)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return (4, 4, 32, 32)
  
  
  
  
  
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   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,
  								}
  								a_       :   Union[str, Any]			=	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							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Dict			=	self.prepare_init_args_and_inputs_for_common()
  								a_       :   List[str]			=	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     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Union[str, Any]			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   int			=	32
  
  								a_       :   Any			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  								model.eval()
  
  								with torch.no_grad():
  															a_       :   int			=	model(**UpperCamelCase_							)
  
  															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  																						a_       :   Dict			=	output.sample
  
  								self.assertIsNotNone(UpperCamelCase_							)
  								a_       :   Optional[Any]			=	inputs_dict["""sample"""].shape
  								self.assertEqual(output.shape				,    UpperCamelCase_				,    """Input and output shapes do not match"""							)
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Any			=	self.prepare_init_args_and_inputs_for_common()
  								a_       :   str			=	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_       :   Tuple			=	model(**UpperCamelCase_							)
  															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  																						a_       :   int			=	first.sample
  
  															a_       :   Optional[int]			=	model(**UpperCamelCase_							)
  															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  																						a_       :   Optional[Any]			=	second.sample
  
  								a_       :   Union[str, Any]			=	first.cpu().numpy()
  								a_       :   List[Any]			=	second.cpu().numpy()
  								a_       :   Dict			=	out_a[~np.isnan(UpperCamelCase_							)]
  								a_       :   List[str]			=	out_a[~np.isnan(UpperCamelCase_							)]
  								a_       :   Dict			=	np.amax(np.abs(out_a - out_a							)							)
  								self.assertLessEqual(UpperCamelCase_				,    1e-5							)
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Dict			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   List[Any]			=	8
  
  								a_       :   str			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  								model.eval()
  
  								model.set_attention_slice("""auto"""							)
  								with torch.no_grad():
  															a_       :   str			=	model(**UpperCamelCase_							)
  								assert output is not None
  
  								model.set_attention_slice("""max"""							)
  								with torch.no_grad():
  															a_       :   Any			=	model(**UpperCamelCase_							)
  								assert output is not None
  
  								model.set_attention_slice(2							)
  								with torch.no_grad():
  															a_       :   int			=	model(**UpperCamelCase_							)
  								assert output is not None
  
  
  
  
  
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Union[str, Any]			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   Optional[int]			=	8
  
  								a_       :   str			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   List[str]			=	model(**UpperCamelCase_							).sample
  
  								a_       :   List[Any]			=	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_       :   int			=	model(**UpperCamelCase_				,    cross_attention_kwargs={"""scale""": 0.0}							).sample
  															a_       :   List[str]			=	model(**UpperCamelCase_				,    cross_attention_kwargs={"""scale""": 0.5}							).sample
  															a_       :   Any			=	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     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   int			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   int			=	8
  
  								torch.manual_seed(0							)
  								a_       :   Tuple			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   Any			=	model(**UpperCamelCase_							).sample
  
  								a_       :   List[str]			=	create_lora_layers(UpperCamelCase_							)
  								model.set_attn_processor(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   str			=	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_       :   Dict			=	self.model_class(**UpperCamelCase_							)
  															new_model.to(UpperCamelCase_							)
  															new_model.load_attn_procs(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   Optional[int]			=	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     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Tuple			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   Optional[int]			=	8
  
  								torch.manual_seed(0							)
  								a_       :   Dict			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   Optional[Any]			=	model(**UpperCamelCase_							).sample
  
  								a_       :   Tuple			=	create_lora_layers(UpperCamelCase_							)
  								model.set_attn_processor(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   Optional[int]			=	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_       :   Dict			=	self.model_class(**UpperCamelCase_							)
  															new_model.to(UpperCamelCase_							)
  															new_model.load_attn_procs(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   str			=	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     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Dict			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   List[str]			=	8
  
  								torch.manual_seed(0							)
  								a_       :   Union[str, Any]			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  
  								a_       :   Dict			=	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_       :   Dict			=	self.model_class(**UpperCamelCase_							)
  															new_model.to(UpperCamelCase_							)
  															new_model.load_attn_procs(UpperCamelCase_				,    weight_name="""pytorch_lora_weights.bin"""							)
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Union[str, Any]			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   Dict			=	8
  
  								torch.manual_seed(0							)
  								a_       :   Dict			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  
  								a_       :   List[Any]			=	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_       :   Tuple			=	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     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Tuple			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   Optional[int]			=	8
  
  								torch.manual_seed(0							)
  								a_       :   Any			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   Dict			=	model(**UpperCamelCase_							).sample
  
  								a_       :   Dict			=	create_lora_layers(UpperCamelCase_							)
  								model.set_attn_processor(UpperCamelCase_							)
  
  								with torch.no_grad():
  															a_       :   int			=	model(**UpperCamelCase_				,    cross_attention_kwargs={"""scale""": 0.0}							).sample
  
  								model.set_attn_processor(AttnProcessor()							)
  
  								with torch.no_grad():
  															a_       :   Any			=	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     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   str			=	self.prepare_init_args_and_inputs_for_common()
  
  								a_       :   List[Any]			=	4
  
  								torch.manual_seed(0							)
  								a_       :   Optional[Any]			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  								a_       :   Tuple			=	create_lora_layers(UpperCamelCase_							)
  								model.set_attn_processor(UpperCamelCase_							)
  
  								# default
  								with torch.no_grad():
  															a_       :   Tuple			=	model(**UpperCamelCase_							).sample
  
  															model.enable_xformers_memory_efficient_attention()
  															a_       :   Tuple			=	model(**UpperCamelCase_							).sample
  
  															model.disable_xformers_memory_efficient_attention()
  															a_       :   Dict			=	model(**UpperCamelCase_							).sample
  
  								assert (sample - on_sample).abs().max() < 1e-4
  								assert (sample - off_sample).abs().max() < 1e-4
  
  
  
  
  
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   int			=	self.prepare_init_args_and_inputs_for_common()
  								a_       :   List[Any]			=	32
  
  								a_       :   str			=	self.model_class(**UpperCamelCase_							)
  								model.to(UpperCamelCase_							)
  								model.eval()
  
  								with torch.no_grad():
  															a_       :   List[Any]			=	model(**UpperCamelCase_							)[0]
  
  								model.enable_forward_chunking()
  								with torch.no_grad():
  															a_       :   Tuple			=	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: 
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : List[Any]          =    {
      "shi-labs/oneformer_ade20k_swin_tiny": (
          "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
      ),
      # See all OneFormer models at https://huggingface.co/models?filter=oneformer
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Tuple         = """oneformer"""
  	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if backbone_config is None:
  															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
  															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
  															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
  								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
  															a_       :   str			=	backbone_config.get("""model_type"""							)
  															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
  															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)
  
  								a_       :   str			=	backbone_config
  
  								a_       :   List[str]			=	ignore_value
  								a_       :   Optional[int]			=	num_queries
  								a_       :   Optional[Any]			=	no_object_weight
  								a_       :   List[Any]			=	class_weight
  								a_       :   Union[str, Any]			=	mask_weight
  								a_       :   Union[str, Any]			=	dice_weight
  								a_       :   Optional[Any]			=	contrastive_weight
  								a_       :   Any			=	contrastive_temperature
  								a_       :   Optional[int]			=	train_num_points
  								a_       :   Tuple			=	oversample_ratio
  								a_       :   str			=	importance_sample_ratio
  								a_       :   List[Any]			=	init_std
  								a_       :   int			=	init_xavier_std
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   Optional[int]			=	is_training
  								a_       :   List[Any]			=	use_auxiliary_loss
  								a_       :   str			=	output_auxiliary_logits
  								a_       :   Dict			=	strides
  								a_       :   List[Any]			=	task_seq_len
  								a_       :   int			=	text_encoder_width
  								a_       :   List[Any]			=	text_encoder_context_length
  								a_       :   List[str]			=	text_encoder_num_layers
  								a_       :   Union[str, Any]			=	text_encoder_vocab_size
  								a_       :   Optional[Any]			=	text_encoder_proj_layers
  								a_       :   Optional[int]			=	text_encoder_n_ctx
  								a_       :   Optional[Any]			=	conv_dim
  								a_       :   str			=	mask_dim
  								a_       :   Optional[Any]			=	hidden_dim
  								a_       :   Any			=	encoder_feedforward_dim
  								a_       :   Dict			=	norm
  								a_       :   List[Any]			=	encoder_layers
  								a_       :   int			=	decoder_layers
  								a_       :   Any			=	use_task_norm
  								a_       :   Union[str, Any]			=	num_attention_heads
  								a_       :   List[str]			=	dropout
  								a_       :   List[Any]			=	dim_feedforward
  								a_       :   Union[str, Any]			=	pre_norm
  								a_       :   Dict			=	enforce_input_proj
  								a_       :   Tuple			=	query_dec_layers
  								a_       :   Any			=	common_stride
  								a_       :   List[str]			=	decoder_layers
  
  								super().__init__(**UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict[str, any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
  								a_       :   Any			=	self.backbone_config.to_dict()
  								a_       :   Union[str, Any]			=	self.__class__.model_type
  								return output
  
  
  