code: 
  
  
  
  '''simple docstring'''
  
  
  
  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 _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  {}
   for name in unet.attn_processors.keys():
    __UpperCamelCase				      =  None if name.endswith("""attn1.processor"""       ) else unet.config.cross_attention_dim
    if name.startswith("""mid_block"""       ):
     __UpperCamelCase				      =  unet.config.block_out_channels[-1]
    elif name.startswith("""up_blocks"""       ):
     __UpperCamelCase				      =  int(name[len("""up_blocks."""       )]       )
     __UpperCamelCase				      =  list(reversed(unet.config.block_out_channels       )       )[block_id]
    elif name.startswith("""down_blocks"""       ):
     __UpperCamelCase				      =  int(name[len("""down_blocks."""       )]       )
     __UpperCamelCase				      =  unet.config.block_out_channels[block_id]
    __UpperCamelCase				      =  (
        LoRAAttnProcessor2_0 if hasattr(__A							,"""scaled_dot_product_attention"""       ) else LoRAAttnProcessor
    )
    __UpperCamelCase				      =  lora_attn_processor_class(
        hidden_size=__A							,cross_attention_dim=__A       )
   __UpperCamelCase				      =  AttnProcsLayers(__A       )
   return lora_attn_procs, unet_lora_layers
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  {}
   __UpperCamelCase				      =  (
       LoRAAttnProcessor2_0 if hasattr(__A							,"""scaled_dot_product_attention"""       ) else LoRAAttnProcessor
   )
   for name, module in text_encoder.named_modules():
    if name.endswith(__A       ):
     __UpperCamelCase				      =  lora_attn_processor_class(
         hidden_size=module.out_proj.out_features							,cross_attention_dim=__A       )
   return text_lora_attn_procs
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  create_text_encoder_lora_attn_procs(__A       )
   __UpperCamelCase				      =  AttnProcsLayers(__A       )
   return text_encoder_lora_layers
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A=False       ):
  
  
  
   '''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"""       ):
      __UpperCamelCase				      =  (
          torch.randn_like(layer_module.up.weight       )
          if randn_weight
          else torch.zeros_like(layer_module.up.weight       )
      )
      __UpperCamelCase				      =  torch.nn.Parameter(__A       )
  
  
  
  
  
  
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    torch.manual_seed(0			)
    __UpperCamelCase				      =  UNetaDConditionModel(
        block_out_channels=(3_2, 6_4)		,	layers_per_block=2		,	sample_size=3_2		,	in_channels=4		,	out_channels=4		,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,	cross_attention_dim=3_2		,	)
    __UpperCamelCase				      =  DDIMScheduler(
        beta_start=0.00_085		,	beta_end=0.012		,	beta_schedule="""scaled_linear"""		,	clip_sample=lowercase		,	set_alpha_to_one=lowercase		,	steps_offset=1		,	)
    torch.manual_seed(0			)
    __UpperCamelCase				      =  AutoencoderKL(
        block_out_channels=[3_2, 6_4]		,	in_channels=3		,	out_channels=3		,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,	latent_channels=4		,	)
    __UpperCamelCase				      =  CLIPTextConfig(
        bos_token_id=0		,	eos_token_id=2		,	hidden_size=3_2		,	intermediate_size=3_7		,	layer_norm_eps=1E-05		,	num_attention_heads=4		,	num_hidden_layers=5		,	pad_token_id=1		,	vocab_size=1_0_0_0		,	)
    __UpperCamelCase				      =  CLIPTextModel(lowercase			)
    __UpperCamelCase				      =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
  
    __UpperCamelCase      ,		__UpperCamelCase				      =  create_unet_lora_layers(lowercase			)
    __UpperCamelCase				      =  create_text_encoder_lora_layers(lowercase			)
  
    __UpperCamelCase				      =  {
        """unet""": unet,
        """scheduler""": scheduler,
        """vae""": vae,
        """text_encoder""": text_encoder,
        """tokenizer""": tokenizer,
        """safety_checker""": None,
        """feature_extractor""": None,
    }
    __UpperCamelCase				      =  {
        """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    __lowerCamelCase				(  self			)  ->							Any:
    __UpperCamelCase				      =  1
    __UpperCamelCase				      =  1_0
    __UpperCamelCase				      =  4
    __UpperCamelCase				      =  (3_2, 3_2)
  
    __UpperCamelCase				      =  torch.manual_seed(0			)
    __UpperCamelCase				      =  floats_tensor((batch_size, num_channels) + sizes			)
    __UpperCamelCase				      =  torch.randint(1		,	lowercase		,	size=(batch_size, sequence_length)		,	generator=lowercase			)
  
    __UpperCamelCase				      =  {
        """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    __lowerCamelCase				(  self			)  ->							Any:
    __UpperCamelCase				      =  7_7
  
    __UpperCamelCase				      =  torch.randint(2		,	5_6		,	size=(1, max_seq_length)		,	generator=torch.manual_seed(0			)			)
  
    __UpperCamelCase				      =  {}
    __UpperCamelCase				      =  inputs
    return prepared_inputs
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							Union[str, Any]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    LoraLoaderMixin.save_lora_weights(
        save_directory=lowercase		,	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(lowercase		,	"""pytorch_lora_weights.bin"""			)			)			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
    __UpperCamelCase				      =  sd_pipe.to(lowercase			)
    sd_pipe.set_progress_bar_config(disable=lowercase			)
  
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_inputs()
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  original_images[0, -3:, -3:, -1]
  
    with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase		,	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(lowercase		,	"""pytorch_lora_weights.bin"""			)			)			)
     sd_pipe.load_lora_weights(lowercase			)
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  lora_images[0, -3:, -3:, -1]
  
    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(lowercase			)		,	torch.from_numpy(lowercase			)			)			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
    __UpperCamelCase				      =  sd_pipe.to(lowercase			)
    sd_pipe.set_progress_bar_config(disable=lowercase			)
  
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_inputs()
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  original_images[0, -3:, -3:, -1]
  
    with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase		,	unet_lora_layers=lora_components["""unet_lora_layers"""]		,	text_encoder_lora_layers=lora_components["""text_encoder_lora_layers"""]		,	safe_serialization=lowercase		,	)
     self.assertTrue(os.path.isfile(os.path.join(lowercase		,	"""pytorch_lora_weights.safetensors"""			)			)			)
     sd_pipe.load_lora_weights(lowercase			)
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  lora_images[0, -3:, -3:, -1]
  
    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(lowercase			)		,	torch.from_numpy(lowercase			)			)			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  lora_components["""unet_lora_attn_procs"""]
    __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
    __UpperCamelCase				      =  sd_pipe.to(lowercase			)
    sd_pipe.set_progress_bar_config(disable=lowercase			)
  
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_inputs()
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  original_images[0, -3:, -3:, -1]
  
    with tempfile.TemporaryDirectory() as tmpdirname:
     __UpperCamelCase				      =  sd_pipe.unet
     unet.set_attn_processor(lowercase			)
     unet.save_attn_procs(lowercase			)
     self.assertTrue(os.path.isfile(os.path.join(lowercase		,	"""pytorch_lora_weights.bin"""			)			)			)
     sd_pipe.load_lora_weights(lowercase			)
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  lora_images[0, -3:, -3:, -1]
  
    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(lowercase			)		,	torch.from_numpy(lowercase			)			)			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
  
    __UpperCamelCase				      =  self.get_dummy_tokens()
  
    # inference without lora
    __UpperCamelCase				      =  pipe.text_encoder(**lowercase			)[0]
    assert outputs_without_lora.shape == (1, 7_7, 3_2)
  
    # create lora_attn_procs with zeroed out up.weights
    __UpperCamelCase				      =  create_text_encoder_lora_attn_procs(pipe.text_encoder			)
    set_lora_up_weights(lowercase		,	randn_weight=lowercase			)
  
    # monkey patch
    pipe._modify_text_encoder(lowercase			)
  
    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()
  
    # inference with lora
    __UpperCamelCase				      =  pipe.text_encoder(**lowercase			)[0]
    assert outputs_with_lora.shape == (1, 7_7, 3_2)
  
    assert torch.allclose(
        lowercase		,	lowercase			), "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
    __UpperCamelCase				      =  create_text_encoder_lora_attn_procs(pipe.text_encoder			)
    set_lora_up_weights(lowercase		,	randn_weight=lowercase			)
  
    # monkey patch
    pipe._modify_text_encoder(lowercase			)
  
    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()
  
    # inference with lora
    __UpperCamelCase				      =  pipe.text_encoder(**lowercase			)[0]
    assert outputs_with_lora.shape == (1, 7_7, 3_2)
  
    assert not torch.allclose(
        lowercase		,	lowercase			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
  
    __UpperCamelCase				      =  self.get_dummy_tokens()
  
    # inference without lora
    __UpperCamelCase				      =  pipe.text_encoder(**lowercase			)[0]
    assert outputs_without_lora.shape == (1, 7_7, 3_2)
  
    # create lora_attn_procs with randn up.weights
    __UpperCamelCase				      =  create_text_encoder_lora_attn_procs(pipe.text_encoder			)
    set_lora_up_weights(lowercase		,	randn_weight=lowercase			)
  
    # monkey patch
    pipe._modify_text_encoder(lowercase			)
  
    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()
  
    # inference with lora
    __UpperCamelCase				      =  pipe.text_encoder(**lowercase			)[0]
    assert outputs_with_lora.shape == (1, 7_7, 3_2)
  
    assert not torch.allclose(
        lowercase		,	lowercase			), "lora outputs should be different to without lora outputs"
  
    # remove monkey patch
    pipe._remove_text_encoder_monkey_patch()
  
    # inference with removed lora
    __UpperCamelCase				      =  pipe.text_encoder(**lowercase			)[0]
    assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)
  
    assert torch.allclose(
        lowercase		,	lowercase			), "remove lora monkey patch should restore the original outputs"
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
    __UpperCamelCase				      =  sd_pipe.to(lowercase			)
    sd_pipe.set_progress_bar_config(disable=lowercase			)
  
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_inputs()
  
    with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase		,	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(lowercase		,	"""pytorch_lora_weights.bin"""			)			)			)
     sd_pipe.load_lora_weights(lowercase			)
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  lora_images[0, -3:, -3:, -1]
  
    __UpperCamelCase				      =  sd_pipe(**lowercase		,	cross_attention_kwargs={"""scale""": 0.5}			).images
    __UpperCamelCase				      =  lora_images_with_scale[0, -3:, -3:, -1]
  
    # Outputs shouldn't match.
    self.assertFalse(
        torch.allclose(torch.from_numpy(lowercase			)		,	torch.from_numpy(lowercase			)			)			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  
  
  
    with tempfile.TemporaryDirectory() as tmpdirname:
     self.create_lora_weight_file(lowercase			)
  
     __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
     __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
     __UpperCamelCase				      =  sd_pipe.to(lowercase			)
     sd_pipe.set_progress_bar_config(disable=lowercase			)
  
     # check if vanilla attention processors are used
     for _, module in sd_pipe.unet.named_modules():
      if isinstance(lowercase		,	lowercase			):
       self.assertIsInstance(module.processor		,	(AttnProcessor, AttnProcessor2_0)			)
  
              # load LoRA weight file
     sd_pipe.load_lora_weights(lowercase			)
  
     # check if lora attention processors are used
     for _, module in sd_pipe.unet.named_modules():
      if isinstance(lowercase		,	lowercase			):
       __UpperCamelCase				      =  (
           LoRAAttnProcessor2_0 if hasattr(lowercase		,	"""scaled_dot_product_attention"""			) else LoRAAttnProcessor
       )
       self.assertIsInstance(module.processor		,	lowercase			)
  
  
  
   @unittest.skipIf(torch_device != """cuda"""		,	"""This test is supposed to run on GPU"""			)
   def    __lowerCamelCase				(  self			)  ->							int:
  
  
  
    with tempfile.TemporaryDirectory() as tmpdirname:
     self.create_lora_weight_file(lowercase			)
  
     __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
     __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
     __UpperCamelCase				      =  sd_pipe.to(lowercase			)
     sd_pipe.set_progress_bar_config(disable=lowercase			)
  
     # 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(lowercase		,	lowercase			):
       self.assertIsInstance(module.processor		,	lowercase			)
  
              # load LoRA weight file
     sd_pipe.load_lora_weights(lowercase			)
  
     # check if lora attention processors are used
     for _, module in sd_pipe.unet.named_modules():
      if isinstance(lowercase		,	lowercase			):
       self.assertIsInstance(module.processor		,	lowercase			)
  
  
  
  
   @unittest.skipIf(torch_device != """cuda"""		,	"""This test is supposed to run on GPU"""			)
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_components()
    __UpperCamelCase				      =  StableDiffusionPipeline(**lowercase			)
    __UpperCamelCase				      =  sd_pipe.to(lowercase			)
    sd_pipe.set_progress_bar_config(disable=lowercase			)
  
    __UpperCamelCase      ,		__UpperCamelCase      ,		__UpperCamelCase				      =  self.get_dummy_inputs()
  
    # enable XFormers
    sd_pipe.enable_xformers_memory_efficient_attention()
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  original_images[0, -3:, -3:, -1]
  
    with tempfile.TemporaryDirectory() as tmpdirname:
     LoraLoaderMixin.save_lora_weights(
         save_directory=lowercase		,	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(lowercase		,	"""pytorch_lora_weights.bin"""			)			)			)
     sd_pipe.load_lora_weights(lowercase			)
  
    __UpperCamelCase				      =  sd_pipe(**lowercase			).images
    __UpperCamelCase				      =  lora_images[0, -3:, -3:, -1]
  
    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(lowercase			)		,	torch.from_numpy(lowercase			)			)			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  a__						:			str										=			[chr(i) for i in range(3_2, 1_2_6)]
  a__						:			List[Any]										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(range(len(alphabets)))
  a__						:			Dict										=			list(range(len(alphabets)))
  a__						:			Union[str, Any]										=			list(reversed(range(len(alphabets))))
  a__						:			List[str]										=			[]
  a__						:			int										=			0
  
  
  
  
  
  
  
  def _lowercase   (  ):
  
  
  
   '''simple docstring'''
  
  
  
   global gear_one_pos
   global gear_two_pos
   global gear_three_pos
   __UpperCamelCase				      =  gear_one[0]
   gear_one.append(__A       )
   del gear_one[0]
   gear_one_pos += 1
   if gear_one_pos % int(len(__A       )       ) == 0:
    __UpperCamelCase				      =  gear_two[0]
    gear_two.append(__A       )
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__A       )       ) == 0:
     __UpperCamelCase				      =  gear_three[0]
     gear_three.append(__A       )
     del gear_three[0]
     gear_three_pos += 1
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  alphabets.index(__A       )
   __UpperCamelCase				      =  gear_one[target]
   __UpperCamelCase				      =  gear_two[target]
   __UpperCamelCase				      =  gear_three[target]
   __UpperCamelCase				      =  reflector[target]
   __UpperCamelCase				      =  gear_three.index(__A       )
   __UpperCamelCase				      =  gear_two.index(__A       )
   __UpperCamelCase				      =  gear_one.index(__A       )
   code.append(alphabets[target]       )
   rotator()
  
  
  if __name__ == "__main__":
   a__						:			Any										=			list(input('Type your message:\n'))
   while True:
    try:
     a__						:			List[Any]										=			int(input('Please set token:(must be only digits)\n'))
     break
    except Exception as error:
     print(error)
   for _ in range(token):
    rotator()
   for j in decode:
    engine(j)
   print('\n' + ''.join(code))
   print(
       f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
       'this message again you should input same digits as token!'
   )
  
  
  
  
  
  