code: 
  import gc
  import os
  import tempfile
  import unittest
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
  from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
  from diffusers.models.attention_processor import (
      Attention,
      AttnProcessor,
      AttnProcessor2_0,
      LoRAAttnProcessor,
      LoRAAttnProcessor2_0,
      LoRAXFormersAttnProcessor,
      XFormersAttnProcessor,
  )
  from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						nn.Module     )       ->	Optional[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Optional[int]			     =  {}
  	for name in unet.attn_processors.keys():
  		__magic_name__	:	Any			     =  None if name.endswith("attn1.processor"     ) else unet.config.cross_attention_dim
  		if name.startswith("mid_block"     ):
  			__magic_name__	:	List[str]			     =  unet.config.block_out_channels[-1]
  		elif name.startswith("up_blocks"     ):
  			__magic_name__	:	List[Any]			     =  int(name[len("up_blocks."     )]     )
  			__magic_name__	:	Union[str, Any]			     =  list(reversed(unet.config.block_out_channels     )     )[block_id]
  		elif name.startswith("down_blocks"     ):
  			__magic_name__	:	List[Any]			     =  int(name[len("down_blocks."     )]     )
  			__magic_name__	:	str			     =  unet.config.block_out_channels[block_id]
  		__magic_name__	:	List[Any]			     =  (
  		    LoRAAttnProcessor2_0 if hasattr(_snake_case   ,     "scaled_dot_product_attention"     ) else LoRAAttnProcessor
  		)
  		__magic_name__	:	Union[str, Any]			     =  lora_attn_processor_class(
  		    hidden_size=_snake_case   ,     cross_attention_dim=_snake_case     )
  	__magic_name__	:	str			     =  AttnProcsLayers(_snake_case     )
  	return lora_attn_procs, unet_lora_layers
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						nn.Module     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Union[str, Any]			     =  {}
  	__magic_name__	:	Union[str, Any]			     =  (
  	    LoRAAttnProcessor2_0 if hasattr(_snake_case   ,     "scaled_dot_product_attention"     ) else LoRAAttnProcessor
  	)
  	for name, module in text_encoder.named_modules():
  		if name.endswith(_snake_case     ):
  			__magic_name__	:	List[str]			     =  lora_attn_processor_class(
  			    hidden_size=module.out_proj.out_features   ,     cross_attention_dim=_snake_case     )
  	return text_lora_attn_procs
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						nn.Module     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Dict			     =  create_text_encoder_lora_attn_procs(_snake_case     )
  	__magic_name__	:	Tuple			     =  AttnProcsLayers(_snake_case     )
  	return text_encoder_lora_layers
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Any   ,     _snake_case   :						List[str]=False     )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	for _, attn_proc in text_lora_attn_procs.items():
  		# set up.weights
  		for layer_name, layer_module in attn_proc.named_modules():
  			if layer_name.endswith("_lora"     ):
  				__magic_name__	:	List[Any]			     =  (
  				    torch.randn_like(layer_module.up.weight     )
  				    if randn_weight
  				    else torch.zeros_like(layer_module.up.weight     )
  				)
  				__magic_name__	:	Dict			     =  torch.nn.Parameter(_snake_case     )
  
  
  
  
  
  class    _snake_case      (  unittest.TestCase       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				torch.manual_seed(0	)
  				__magic_name__	:	List[Any]			     =  UNetaDConditionModel(
  				    block_out_channels=(32, 64)		,     layers_per_block=2		,     sample_size=32		,     in_channels=4		,     out_channels=4		,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")		,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")		,     cross_attention_dim=32		,     )
  				__magic_name__	:	Any			     =  DDIMScheduler(
  				    beta_start=0.0_00_85		,     beta_end=0.0_12		,     beta_schedule="scaled_linear"		,     clip_sample=_a		,     set_alpha_to_one=_a		,     steps_offset=1		,     )
  				torch.manual_seed(0	)
  				__magic_name__	:	Optional[int]			     =  AutoencoderKL(
  				    block_out_channels=[32, 64]		,     in_channels=3		,     out_channels=3		,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]		,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]		,     latent_channels=4		,     )
  				__magic_name__	:	Optional[Any]			     =  CLIPTextConfig(
  				    bos_token_id=0		,     eos_token_id=2		,     hidden_size=32		,     intermediate_size=37		,     layer_norm_eps=1e-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_000		,     )
  				__magic_name__	:	Optional[int]			     =  CLIPTextModel(_a	)
  				__magic_name__	:	List[Any]			     =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
  
  				__magic_name__     ,					__magic_name__	:	str			     =  create_unet_lora_layers(_a	)
  				__magic_name__	:	Tuple			     =  create_text_encoder_lora_layers(_a	)
  
  				__magic_name__	:	Optional[Any]			     =  {
  				    "unet": unet,
  				    "scheduler": scheduler,
  				    "vae": vae,
  				    "text_encoder": text_encoder,
  				    "tokenizer": tokenizer,
  				    "safety_checker": None,
  				    "feature_extractor": None,
  				}
  				__magic_name__	:	Optional[Any]			     =  {
  				    "unet_lora_layers": unet_lora_layers,
  				    "text_encoder_lora_layers": text_encoder_lora_layers,
  				    "unet_lora_attn_procs": unet_lora_attn_procs,
  				}
  				return pipeline_components, lora_components
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  1
  				__magic_name__	:	List[Any]			     =  10
  				__magic_name__	:	Dict			     =  4
  				__magic_name__	:	Dict			     =  (32, 32)
  
  				__magic_name__	:	int			     =  torch.manual_seed(0	)
  				__magic_name__	:	Union[str, Any]			     =  floats_tensor((batch_size, num_channels) + sizes	)
  				__magic_name__	:	str			     =  torch.randint(1		,     _a		,     size=(batch_size, sequence_length)		,     generator=_a	)
  
  				__magic_name__	:	int			     =  {
  				    "prompt": "A painting of a squirrel eating a burger",
  				    "generator": generator,
  				    "num_inference_steps": 2,
  				    "guidance_scale": 6.0,
  				    "output_type": "numpy",
  				}
  
  				return noise, input_ids, pipeline_inputs
  
  				# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	str			     =  77
  
  				__magic_name__	:	Tuple			     =  torch.randint(2		,     56		,     size=(1, max_seq_length)		,     generator=torch.manual_seed(0	)	)
  
  				__magic_name__	:	str			     =  {}
  				__magic_name__	:	List[Any]			     =  inputs
  				return prepared_inputs
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.get_dummy_components()
  				LoraLoaderMixin.save_lora_weights(
  				    save_directory=_a		,     unet_lora_layers=lora_components["unet_lora_layers"]		,     text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]		,     )
  				self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.get_dummy_components()
  				__magic_name__	:	int			     =  StableDiffusionPipeline(**_a	)
  				__magic_name__	:	Optional[Any]			     =  sd_pipe.to(_a	)
  				sd_pipe.set_progress_bar_config(disable=_a	)
  
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	str			     =  self.get_dummy_inputs()
  
  				__magic_name__	:	Tuple			     =  sd_pipe(**_a	).images
  				__magic_name__	:	Any			     =  original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					LoraLoaderMixin.save_lora_weights(
  					    save_directory=_a		,     unet_lora_layers=lora_components["unet_lora_layers"]		,     text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]		,     )
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					sd_pipe.load_lora_weights(_a	)
  
  				__magic_name__	:	Any			     =  sd_pipe(**_a	).images
  				__magic_name__	:	List[str]			     =  lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(_a	)		,     torch.from_numpy(_a	)	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.get_dummy_components()
  				__magic_name__	:	List[Any]			     =  StableDiffusionPipeline(**_a	)
  				__magic_name__	:	Optional[Any]			     =  sd_pipe.to(_a	)
  				sd_pipe.set_progress_bar_config(disable=_a	)
  
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	List[str]			     =  self.get_dummy_inputs()
  
  				__magic_name__	:	Optional[Any]			     =  sd_pipe(**_a	).images
  				__magic_name__	:	Dict			     =  original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					LoraLoaderMixin.save_lora_weights(
  					    save_directory=_a		,     unet_lora_layers=lora_components["unet_lora_layers"]		,     text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]		,     safe_serialization=_a		,     )
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.safetensors"	)	)	)
  					sd_pipe.load_lora_weights(_a	)
  
  				__magic_name__	:	Union[str, Any]			     =  sd_pipe(**_a	).images
  				__magic_name__	:	int			     =  lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(_a	)		,     torch.from_numpy(_a	)	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	List[Any]			     =  self.get_dummy_components()
  				__magic_name__	:	Union[str, Any]			     =  lora_components["unet_lora_attn_procs"]
  				__magic_name__	:	Optional[int]			     =  StableDiffusionPipeline(**_a	)
  				__magic_name__	:	Union[str, Any]			     =  sd_pipe.to(_a	)
  				sd_pipe.set_progress_bar_config(disable=_a	)
  
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Dict			     =  self.get_dummy_inputs()
  
  				__magic_name__	:	Tuple			     =  sd_pipe(**_a	).images
  				__magic_name__	:	List[Any]			     =  original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					__magic_name__	:	List[str]			     =  sd_pipe.unet
  					unet.set_attn_processor(_a	)
  					unet.save_attn_procs(_a	)
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					sd_pipe.load_lora_weights(_a	)
  
  				__magic_name__	:	Union[str, Any]			     =  sd_pipe(**_a	).images
  				__magic_name__	:	Tuple			     =  lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(_a	)		,     torch.from_numpy(_a	)	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	int			     =  self.get_dummy_components()
  				__magic_name__	:	Optional[int]			     =  StableDiffusionPipeline(**_a	)
  
  				__magic_name__	:	List[Any]			     =  self.get_dummy_tokens()
  
  				# inference without lora
  				__magic_name__	:	Optional[int]			     =  pipe.text_encoder(**_a	)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with zeroed out up.weights
  				__magic_name__	:	Tuple			     =  create_text_encoder_lora_attn_procs(pipe.text_encoder	)
  				set_lora_up_weights(_a		,     randn_weight=_a	)
  
  				# monkey patch
  				pipe._modify_text_encoder(_a	)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				__magic_name__	:	List[Any]			     =  pipe.text_encoder(**_a	)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    _a		,     _a	), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"
  
  				# create lora_attn_procs with randn up.weights
  				__magic_name__	:	Tuple			     =  create_text_encoder_lora_attn_procs(pipe.text_encoder	)
  				set_lora_up_weights(_a		,     randn_weight=_a	)
  
  				# monkey patch
  				pipe._modify_text_encoder(_a	)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				__magic_name__	:	List[Any]			     =  pipe.text_encoder(**_a	)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    _a		,     _a	), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.get_dummy_components()
  				__magic_name__	:	Any			     =  StableDiffusionPipeline(**_a	)
  
  				__magic_name__	:	str			     =  self.get_dummy_tokens()
  
  				# inference without lora
  				__magic_name__	:	Optional[int]			     =  pipe.text_encoder(**_a	)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with randn up.weights
  				__magic_name__	:	Optional[int]			     =  create_text_encoder_lora_attn_procs(pipe.text_encoder	)
  				set_lora_up_weights(_a		,     randn_weight=_a	)
  
  				# monkey patch
  				pipe._modify_text_encoder(_a	)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				__magic_name__	:	Any			     =  pipe.text_encoder(**_a	)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    _a		,     _a	), "lora outputs should be different to without lora outputs"
  
  				# remove monkey patch
  				pipe._remove_text_encoder_monkey_patch()
  
  				# inference with removed lora
  				__magic_name__	:	Tuple			     =  pipe.text_encoder(**_a	)[0]
  				assert outputs_without_lora_removed.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    _a		,     _a	), "remove lora monkey patch should restore the original outputs"
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	Any			     =  self.get_dummy_components()
  				__magic_name__	:	str			     =  StableDiffusionPipeline(**_a	)
  				__magic_name__	:	Optional[Any]			     =  sd_pipe.to(_a	)
  				sd_pipe.set_progress_bar_config(disable=_a	)
  
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	List[str]			     =  self.get_dummy_inputs()
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					LoraLoaderMixin.save_lora_weights(
  					    save_directory=_a		,     unet_lora_layers=lora_components["unet_lora_layers"]		,     text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]		,     )
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					sd_pipe.load_lora_weights(_a	)
  
  				__magic_name__	:	str			     =  sd_pipe(**_a	).images
  				__magic_name__	:	List[str]			     =  lora_images[0, -3:, -3:, -1]
  
  				__magic_name__	:	Tuple			     =  sd_pipe(**_a		,     cross_attention_kwargs={"scale": 0.5}	).images
  				__magic_name__	:	List[str]			     =  lora_images_with_scale[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(
  				    torch.allclose(torch.from_numpy(_a	)		,     torch.from_numpy(_a	)	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					self.create_lora_weight_file(_a	)
  
  					__magic_name__     ,					__magic_name__	:	int			     =  self.get_dummy_components()
  					__magic_name__	:	List[str]			     =  StableDiffusionPipeline(**_a	)
  					__magic_name__	:	Optional[int]			     =  sd_pipe.to(_a	)
  					sd_pipe.set_progress_bar_config(disable=_a	)
  
  					# check if vanilla attention processors are used
  					for _, module in sd_pipe.unet.named_modules():
  						if isinstance(_a		,     _a	):
  							self.assertIsInstance(module.processor		,     (AttnProcessor, AttnProcessor2_0)	)
  
              # load LoRA weight file
  					sd_pipe.load_lora_weights(_a	)
  
  					# check if lora attention processors are used
  					for _, module in sd_pipe.unet.named_modules():
  						if isinstance(_a		,     _a	):
  							__magic_name__	:	List[str]			     =  (
  							    LoRAAttnProcessor2_0 if hasattr(_a		,     "scaled_dot_product_attention"	) else LoRAAttnProcessor
  							)
  							self.assertIsInstance(module.processor		,     _a	)
  
  
  
  
  
  
  
  			@unittest.skipIf(torch_device != "cuda"		,     "This test is supposed to run on GPU"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					self.create_lora_weight_file(_a	)
  
  					__magic_name__     ,					__magic_name__	:	Dict			     =  self.get_dummy_components()
  					__magic_name__	:	Union[str, Any]			     =  StableDiffusionPipeline(**_a	)
  					__magic_name__	:	Dict			     =  sd_pipe.to(_a	)
  					sd_pipe.set_progress_bar_config(disable=_a	)
  
  					# enable XFormers
  					sd_pipe.enable_xformers_memory_efficient_attention()
  
  					# check if xFormers attention processors are used
  					for _, module in sd_pipe.unet.named_modules():
  						if isinstance(_a		,     _a	):
  							self.assertIsInstance(module.processor		,     _a	)
  
              # load LoRA weight file
  					sd_pipe.load_lora_weights(_a	)
  
  					# check if lora attention processors are used
  					for _, module in sd_pipe.unet.named_modules():
  						if isinstance(_a		,     _a	):
  							self.assertIsInstance(module.processor		,     _a	)
  
  
  
  
  
  
  
  			@unittest.skipIf(torch_device != "cuda"		,     "This test is supposed to run on GPU"	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__     ,					__magic_name__	:	int			     =  self.get_dummy_components()
  				__magic_name__	:	Tuple			     =  StableDiffusionPipeline(**_a	)
  				__magic_name__	:	Dict			     =  sd_pipe.to(_a	)
  				sd_pipe.set_progress_bar_config(disable=_a	)
  
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	str			     =  self.get_dummy_inputs()
  
  				# enable XFormers
  				sd_pipe.enable_xformers_memory_efficient_attention()
  
  				__magic_name__	:	Dict			     =  sd_pipe(**_a	).images
  				__magic_name__	:	List[Any]			     =  original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  					LoraLoaderMixin.save_lora_weights(
  					    save_directory=_a		,     unet_lora_layers=lora_components["unet_lora_layers"]		,     text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]		,     )
  					self.assertTrue(os.path.isfile(os.path.join(_a		,     "pytorch_lora_weights.bin"	)	)	)
  					sd_pipe.load_lora_weights(_a	)
  
  				__magic_name__	:	str			     =  sd_pipe(**_a	).images
  				__magic_name__	:	int			     =  lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(_a	)		,     torch.from_numpy(_a	)	)	)
  
style_context: 
  from __future__ import annotations
  
  snake_case				:	List[str]							   =						"Alexander Joslin"
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if number <= 0:
  		__magic_name__	:	int			     =  F'''n must be greater than 0. Got n = {number}'''
  		raise ValueError(_snake_case     )
  
  	__magic_name__	:	List[str]			     =  [number + 1] * (number + 1)
  
  	# starting position
  	__magic_name__	:	Any			     =  0
  	for i in range(1   ,     _snake_case     ):
  		__magic_name__	:	Optional[int]			     =  min(table[i + 1]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 2 <= number:
  			__magic_name__	:	str			     =  min(table[i * 2]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 3 <= number:
  			__magic_name__	:	Any			     =  min(table[i * 3]   ,     table[i] + 1     )
  	return table[number]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  