code: 
  
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					logging.get_logger(__name__)
  
  
  SCREAMING_SNAKE_CASE__    :    str								=					{"vocab_file": "vocab.json", "merges_file": "merges.txt"}
  
  # See all MVP models at https://huggingface.co/models?filter=mvp
  SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					{
      "vocab_file": {
          "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
      },
      "added_tokens.json": {
          "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
      },
      "merges_file": {
          "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
      },
  }
  
  SCREAMING_SNAKE_CASE__    :    str								=					{
      "RUCAIBox/mvp": 10_24,
  }
  
  @lru_cache()
  def lowercase						(     ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	(
  			    list(range(ord('!'   )					,						ord('~'   ) + 1   )   ) + list(range(ord('¡'   )					,						ord('¬'   ) + 1   )   ) + list(range(ord('®'   )					,						ord('ÿ'   ) + 1   )   )
  			)
  			SCREAMING_SNAKE_CASE_			=	bs[:]
  			SCREAMING_SNAKE_CASE_			=	0
  			for b in range(2**8   ):
  						if b not in bs:
  									bs.append(a_   )
  									cs.append(2**8 + n   )
  									n += 1
  			SCREAMING_SNAKE_CASE_			=	[chr(a_   ) for n in cs]
  			return dict(zip(a_					,						a_   )   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	set()
  			SCREAMING_SNAKE_CASE_			=	word[0]
  			for char in word[1:]:
  						pairs.add((prev_char, char)   )
  						SCREAMING_SNAKE_CASE_			=	char
  			return pairs
  
  
  
  class 			a_					(    lowerCAmelCase_    ):
  	A						       =   VOCAB_FILES_NAMES
  	A						       =   PRETRAINED_VOCAB_FILES_MAP
  	A						       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	A						       =   ['input_ids', 'attention_mask']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE="replace"			,    SCREAMING_SNAKE_CASE="<s>"			,    SCREAMING_SNAKE_CASE="</s>"			,    SCREAMING_SNAKE_CASE="</s>"			,    SCREAMING_SNAKE_CASE="<s>"			,    SCREAMING_SNAKE_CASE="<unk>"			,    SCREAMING_SNAKE_CASE="<pad>"			,    SCREAMING_SNAKE_CASE="<mask>"			,    SCREAMING_SNAKE_CASE=False			,    **SCREAMING_SNAKE_CASE			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	AddedToken(lowerCamelCase_			,    lstrip=lowerCamelCase_			,    rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_			,    lowerCamelCase_			) else bos_token
  				SCREAMING_SNAKE_CASE_			=	AddedToken(lowerCamelCase_			,    lstrip=lowerCamelCase_			,    rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_			,    lowerCamelCase_			) else eos_token
  				SCREAMING_SNAKE_CASE_			=	AddedToken(lowerCamelCase_			,    lstrip=lowerCamelCase_			,    rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_			,    lowerCamelCase_			) else sep_token
  				SCREAMING_SNAKE_CASE_			=	AddedToken(lowerCamelCase_			,    lstrip=lowerCamelCase_			,    rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_			,    lowerCamelCase_			) else cls_token
  				SCREAMING_SNAKE_CASE_			=	AddedToken(lowerCamelCase_			,    lstrip=lowerCamelCase_			,    rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_			,    lowerCamelCase_			) else unk_token
  				SCREAMING_SNAKE_CASE_			=	AddedToken(lowerCamelCase_			,    lstrip=lowerCamelCase_			,    rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_			,    lowerCamelCase_			) else pad_token
  
  				# Mask token behave like a normal word, i.e. include the space before it
  				SCREAMING_SNAKE_CASE_			=	AddedToken(lowerCamelCase_			,    lstrip=lowerCamelCase_			,    rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_			,    lowerCamelCase_			) else mask_token
  
  				super().__init__(
  				    errors=lowerCamelCase_			,    bos_token=lowerCamelCase_			,    eos_token=lowerCamelCase_			,    unk_token=lowerCamelCase_			,    sep_token=lowerCamelCase_			,    cls_token=lowerCamelCase_			,    pad_token=lowerCamelCase_			,    mask_token=lowerCamelCase_			,    add_prefix_space=lowerCamelCase_			,    **lowerCamelCase_			,    )
  				with open(lowerCamelCase_			,    encoding='utf-8'			) as vocab_handle:
  							SCREAMING_SNAKE_CASE_			=	json.load(lowerCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	{v: k for k, v in self.encoder.items()}
  				SCREAMING_SNAKE_CASE_			=	errors  # how to handle errors in decoding
  				SCREAMING_SNAKE_CASE_			=	bytes_to_unicode()
  				SCREAMING_SNAKE_CASE_			=	{v: k for k, v in self.byte_encoder.items()}
  				with open(lowerCamelCase_			,    encoding='utf-8'			) as merges_handle:
  							SCREAMING_SNAKE_CASE_			=	merges_handle.read().split('\n'			)[1:-1]
  				SCREAMING_SNAKE_CASE_			=	[tuple(merge.split()			) for merge in bpe_merges]
  				SCREAMING_SNAKE_CASE_			=	dict(zip(lowerCamelCase_			,    range(len(lowerCamelCase_			)			)			)			)
  				SCREAMING_SNAKE_CASE_			=	{}
  				SCREAMING_SNAKE_CASE_			=	add_prefix_space
  
  				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  				SCREAMING_SNAKE_CASE_			=	re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'			)
  
  
  
  
  
  	@property
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return len(self.encoder			)
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return dict(self.encoder			,    **self.added_tokens_encoder			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if token in self.cache:
  							return self.cache[token]
  				SCREAMING_SNAKE_CASE_			=	tuple(lowerCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	get_pairs(lowerCamelCase_			)
  
  				if not pairs:
  							return token
  
  				while True:
  							SCREAMING_SNAKE_CASE_			=	min(lowerCamelCase_			,    key=lambda SCREAMING_SNAKE_CASE			: self.bpe_ranks.get(lowerCamelCase_			,    float('inf'			)			)			)
  							if bigram not in self.bpe_ranks:
  										break
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	bigram
  							SCREAMING_SNAKE_CASE_			=	[]
  							SCREAMING_SNAKE_CASE_			=	0
  							while i < len(lowerCamelCase_			):
  										try:
  													SCREAMING_SNAKE_CASE_			=	word.index(lowerCamelCase_			,    lowerCamelCase_			)
  										except ValueError:
  													new_word.extend(word[i:]			)
  													break
  										else:
  													new_word.extend(word[i:j]			)
  													SCREAMING_SNAKE_CASE_			=	j
  
  										if word[i] == first and i < len(lowerCamelCase_			) - 1 and word[i + 1] == second:
  													new_word.append(first + second			)
  													i += 2
  										else:
  													new_word.append(word[i]			)
  													i += 1
  							SCREAMING_SNAKE_CASE_			=	tuple(lowerCamelCase_			)
  							SCREAMING_SNAKE_CASE_			=	new_word
  							if len(lowerCamelCase_			) == 1:
  										break
  							else:
  										SCREAMING_SNAKE_CASE_			=	get_pairs(lowerCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	' '.join(lowerCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	word
  				return word
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	[]
  				for token in re.findall(self.pat			,    lowerCamelCase_			):
  							SCREAMING_SNAKE_CASE_			=	''.join(
  							    self.byte_encoder[b] for b in token.encode('utf-8'			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase_			).split(' '			)			)
  				return bpe_tokens
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.encoder.get(lowerCamelCase_			,    self.encoder.get(self.unk_token			)			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.decoder.get(lowerCamelCase_			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	''.join(lowerCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'			,    errors=self.errors			)
  				return text
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not os.path.isdir(lowerCamelCase_			):
  							logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
  							return
  				SCREAMING_SNAKE_CASE_			=	os.path.join(
  				    lowerCamelCase_			,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
  				SCREAMING_SNAKE_CASE_			=	os.path.join(
  				    lowerCamelCase_			,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)
  
  				with open(lowerCamelCase_			,    'w'			,    encoding='utf-8'			) as f:
  							f.write(json.dumps(self.encoder			,    indent=2			,    sort_keys=lowerCamelCase_			,    ensure_ascii=lowerCamelCase_			) + '\n'			)
  
  				SCREAMING_SNAKE_CASE_			=	0
  				with open(lowerCamelCase_			,    'w'			,    encoding='utf-8'			) as writer:
  							writer.write('#version: 0.2\n'			)
  							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,    key=lambda SCREAMING_SNAKE_CASE			: kv[1]			):
  										if index != token_index:
  													logger.warning(
  													    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
  													    ' Please check that the tokenizer is not corrupted!'			)
  													SCREAMING_SNAKE_CASE_			=	token_index
  										writer.write(' '.join(lowerCamelCase_			) + '\n'			)
  										index += 1
  
  				return vocab_file, merge_file
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if token_ids_a is None:
  							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				SCREAMING_SNAKE_CASE_			=	[self.cls_token_id]
  				SCREAMING_SNAKE_CASE_			=	[self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = False			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if already_has_special_tokens:
  							return super().get_special_tokens_mask(
  							    token_ids_a=lowerCamelCase_			,    token_ids_a=lowerCamelCase_			,    already_has_special_tokens=lowerCamelCase_			)
  
  				if token_ids_a is None:
  							return [1] + ([0] * len(lowerCamelCase_			)) + [1]
  				return [1] + ([0] * len(lowerCamelCase_			)) + [1, 1] + ([0] * len(lowerCamelCase_			)) + [1]
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	[self.sep_token_id]
  				SCREAMING_SNAKE_CASE_			=	[self.cls_token_id]
  
  				if token_ids_a is None:
  							return len(cls + token_ids_a + sep			) * [0]
  				return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=False			,    **SCREAMING_SNAKE_CASE			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	kwargs.pop('add_prefix_space'			,    self.add_prefix_space			)
  				if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_			) > 0 and not text[0].isspace()):
  							SCREAMING_SNAKE_CASE_			=	' ' + text
  				return (text, kwargs)
  
style_context: 
  
  
  
  
  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						(     SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			for name in unet.attn_processors.keys():
  						SCREAMING_SNAKE_CASE_			=	None if name.endswith('attn1.processor'   ) else unet.config.cross_attention_dim
  						if name.startswith('mid_block'   ):
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[-1]
  						elif name.startswith('up_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('up_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	list(reversed(unet.config.block_out_channels   )   )[block_id]
  						elif name.startswith('down_blocks'   ):
  									SCREAMING_SNAKE_CASE_			=	int(name[len('down_blocks.'   )]   )
  									SCREAMING_SNAKE_CASE_			=	unet.config.block_out_channels[block_id]
  						SCREAMING_SNAKE_CASE_			=	(
  						    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  						)
  						SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  						    hidden_size=SCREAMING_SNAKE_CASE					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return lora_attn_procs, unet_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	{}
  			SCREAMING_SNAKE_CASE_			=	(
  			    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE					,						'scaled_dot_product_attention'   ) else LoRAAttnProcessor
  			)
  			for name, module in text_encoder.named_modules():
  						if name.endswith(SCREAMING_SNAKE_CASE   ):
  									SCREAMING_SNAKE_CASE_			=	lora_attn_processor_class(
  									    hidden_size=module.out_proj.out_features					,						cross_attention_dim=SCREAMING_SNAKE_CASE   )
  			return text_lora_attn_procs
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Union[str, Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	AttnProcsLayers(SCREAMING_SNAKE_CASE   )
  			return text_encoder_lora_layers
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE=False   ) ->       Dict:
  
  
  			'''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'   ):
  												SCREAMING_SNAKE_CASE_			=	(
  												    torch.randn_like(layer_module.up.weight   )
  												    if randn_weight
  												    else torch.zeros_like(layer_module.up.weight   )
  												)
  												SCREAMING_SNAKE_CASE_			=	torch.nn.Parameter(SCREAMING_SNAKE_CASE   )
  
  
  
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	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			,    )
  				SCREAMING_SNAKE_CASE_			=	DDIMScheduler(
  				    beta_start=0.0_0_0_8_5			,    beta_end=0.0_1_2			,    beta_schedule='scaled_linear'			,    clip_sample=SCREAMING_SNAKE_CASE			,    set_alpha_to_one=SCREAMING_SNAKE_CASE			,    steps_offset=1			,    )
  				torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	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			,    )
  				SCREAMING_SNAKE_CASE_			=	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=1000			,    )
  				SCREAMING_SNAKE_CASE_			=	CLIPTextModel(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	create_unet_lora_layers(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_layers(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    'unet': unet,
  				    'scheduler': scheduler,
  				    'vae': vae,
  				    'text_encoder': text_encoder,
  				    'tokenizer': tokenizer,
  				    'safety_checker': None,
  				    'feature_extractor': None,
  				}
  				SCREAMING_SNAKE_CASE_			=	{
  				    '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 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	1
  				SCREAMING_SNAKE_CASE_			=	10
  				SCREAMING_SNAKE_CASE_			=	4
  				SCREAMING_SNAKE_CASE_			=	(32, 32)
  
  				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
  				SCREAMING_SNAKE_CASE_			=	floats_tensor((batch_size, num_channels) + sizes			)
  				SCREAMING_SNAKE_CASE_			=	torch.randint(1			,    SCREAMING_SNAKE_CASE			,    size=(batch_size, sequence_length)			,    generator=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	{
  				    '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 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	77
  
  				SCREAMING_SNAKE_CASE_			=	torch.randint(2			,    56			,    size=(1, max_seq_length)			,    generator=torch.manual_seed(0			)			)
  
  				SCREAMING_SNAKE_CASE_			=	{}
  				SCREAMING_SNAKE_CASE_			=	inputs
  				return prepared_inputs
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				LoraLoaderMixin.save_lora_weights(
  				    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    unet_lora_layers=lora_components['unet_lora_layers']			,    text_encoder_lora_layers=lora_components['text_encoder_lora_layers']			,    safe_serialization=SCREAMING_SNAKE_CASE			,    )
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.safetensors'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	lora_components['unet_lora_attn_procs']
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.unet
  							unet.set_attn_processor(SCREAMING_SNAKE_CASE			)
  							unet.save_attn_procs(SCREAMING_SNAKE_CASE			)
  							self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with zeroed out up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "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
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	self.get_dummy_tokens()
  
  				# inference without lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora.shape == (1, 77, 32)
  
  				# create lora_attn_procs with randn up.weights
  				SCREAMING_SNAKE_CASE_			=	create_text_encoder_lora_attn_procs(pipe.text_encoder			)
  				set_lora_up_weights(SCREAMING_SNAKE_CASE			,    randn_weight=SCREAMING_SNAKE_CASE			)
  
  				# monkey patch
  				pipe._modify_text_encoder(SCREAMING_SNAKE_CASE			)
  
  				# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
  				del text_attn_procs
  				gc.collect()
  
  				# inference with lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_with_lora.shape == (1, 77, 32)
  
  				assert not torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "lora outputs should be different to without lora outputs"
  
  				# remove monkey patch
  				pipe._remove_text_encoder_monkey_patch()
  
  				# inference with removed lora
  				SCREAMING_SNAKE_CASE_			=	pipe.text_encoder(**SCREAMING_SNAKE_CASE			)[0]
  				assert outputs_without_lora_removed.shape == (1, 77, 32)
  
  				assert torch.allclose(
  				    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			), "remove lora monkey patch should restore the original outputs"
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			,    cross_attention_kwargs={'scale': 0.5}			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images_with_scale[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(
  				    torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# check if vanilla attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    (AttnProcessor, AttnProcessor2_0)			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													SCREAMING_SNAKE_CASE_			=	(
  													    LoRAAttnProcessor2_0 if hasattr(SCREAMING_SNAKE_CASE			,    'scaled_dot_product_attention'			) else LoRAAttnProcessor
  													)
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							self.create_lora_weight_file(SCREAMING_SNAKE_CASE			)
  
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  							SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  							sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  							# 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(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
              # load LoRA weight file
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  							# check if lora attention processors are used
  							for _, module in sd_pipe.unet.named_modules():
  										if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  													self.assertIsInstance(module.processor			,    SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@unittest.skipIf(torch_device != 'cuda'			,    'This test is supposed to run on GPU'			)
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()
  				SCREAMING_SNAKE_CASE_			=	StableDiffusionPipeline(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	sd_pipe.to(SCREAMING_SNAKE_CASE			)
  				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	self.get_dummy_inputs()
  
  				# enable XFormers
  				sd_pipe.enable_xformers_memory_efficient_attention()
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	original_images[0, -3:, -3:, -1]
  
  				with tempfile.TemporaryDirectory() as tmpdirname:
  							LoraLoaderMixin.save_lora_weights(
  							    save_directory=SCREAMING_SNAKE_CASE			,    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(SCREAMING_SNAKE_CASE			,    'pytorch_lora_weights.bin'			)			)			)
  							sd_pipe.load_lora_weights(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	sd_pipe(**SCREAMING_SNAKE_CASE			).images
  				SCREAMING_SNAKE_CASE_			=	lora_images[0, -3:, -3:, -1]
  
  				# Outputs shouldn't match.
  				self.assertFalse(torch.allclose(torch.from_numpy(SCREAMING_SNAKE_CASE			)			,    torch.from_numpy(SCREAMING_SNAKE_CASE			)			)			)
  