






"""simple docstring"""





import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

UpperCamelCase					=     {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

UpperCamelCase					=     {
    """microsoft/deberta-base""": 5_12,
    """microsoft/deberta-large""": 5_12,
    """microsoft/deberta-xlarge""": 5_12,
    """microsoft/deberta-base-mnli""": 5_12,
    """microsoft/deberta-large-mnli""": 5_12,
    """microsoft/deberta-xlarge-mnli""": 5_12,
}

UpperCamelCase					=     {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}






def 							A(    ):
       """simple docstring"""





       lowercase__:							Dict         =     (
           list(range(ord("!"						)		,  ord("~"						) + 1						)						) + list(range(ord("¡"						)		,  ord("¬"						) + 1						)						) + list(range(ord("®"						)		,  ord("ÿ"						) + 1						)						)
       )
       lowercase__:							int         =     bs[:]
       lowercase__:							Union[str, Any]         =     0
       for b in range(2**8						):
              if b not in bs:
                     bs.append(snake_case_						)
                     cs.append(2**8 + n						)
                     n += 1
       lowercase__:							Optional[Any]         =     [chr(snake_case_						) for n in cs]
       return dict(zip(snake_case_		,  snake_case_						)						)






def 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							int         =     set()
       lowercase__:							str         =     word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              lowercase__:							int         =     char
       return pairs







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							VOCAB_FILES_NAMES
   UpperCamelCase__     	=							PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase__     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase__     	=							["""input_ids""", """attention_mask""", """token_type_ids"""]





   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_="replace"					, UpperCAmelCase_="[CLS]"					, UpperCAmelCase_="[SEP]"					, UpperCAmelCase_="[SEP]"					, UpperCAmelCase_="[CLS]"					, UpperCAmelCase_="[UNK]"					, UpperCAmelCase_="[PAD]"					, UpperCAmelCase_="[MASK]"					, UpperCAmelCase_=False					, UpperCAmelCase_=False					, **UpperCAmelCase_					, )			->   Dict:




          '''simple docstring'''




          lowercase__:							List[str]         =     AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_					, UpperCAmelCase_) else bos_token
          lowercase__:							Optional[int]         =     AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_					, UpperCAmelCase_) else eos_token
          lowercase__:							int         =     AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_					, UpperCAmelCase_) else sep_token
          lowercase__:							Optional[Any]         =     AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_					, UpperCAmelCase_) else cls_token
          lowercase__:							List[Any]         =     AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_					, UpperCAmelCase_) else unk_token
          lowercase__:							Tuple         =     AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_					, UpperCAmelCase_) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          lowercase__:							str         =     AddedToken(UpperCAmelCase_					, lstrip=UpperCAmelCase_					, rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_					, UpperCAmelCase_) else mask_token

          super().__init__(
              errors=UpperCAmelCase_					, bos_token=UpperCAmelCase_					, eos_token=UpperCAmelCase_					, unk_token=UpperCAmelCase_					, sep_token=UpperCAmelCase_					, cls_token=UpperCAmelCase_					, pad_token=UpperCAmelCase_					, mask_token=UpperCAmelCase_					, add_prefix_space=UpperCAmelCase_					, add_bos_token=UpperCAmelCase_					, **UpperCAmelCase_					, )
          lowercase__:							int         =     add_bos_token

          with open(UpperCAmelCase_					, encoding="utf-8") as vocab_handle:
                 lowercase__:							List[Any]         =     json.load(UpperCAmelCase_)
          lowercase__:							Union[str, Any]         =     {v: k for k, v in self.encoder.items()}
          lowercase__:							Union[str, Any]         =     errors  # how to handle errors in decoding
          lowercase__:							Optional[int]         =     bytes_to_unicode()
          lowercase__:							Optional[int]         =     {v: k for k, v in self.byte_encoder.items()}
          with open(UpperCAmelCase_					, encoding="utf-8") as merges_handle:
                 lowercase__:							int         =     merges_handle.read().split("\n")[1:-1]
          lowercase__:							Union[str, Any]         =     [tuple(merge.split()) for merge in bpe_merges]
          lowercase__:							int         =     dict(zip(UpperCAmelCase_					, range(len(UpperCAmelCase_))))
          lowercase__:							Tuple         =     {}
          lowercase__:							int         =     add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          lowercase__:							List[Any]         =     re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")





   @property
   # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          return len(self.encoder)





   def __lowercase							(       self)			->   int:




          '''simple docstring'''




          return dict(self.encoder					, **self.added_tokens_encoder)





   def __lowercase							(       self					, UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




          if token in self.cache:
                 return self.cache[token]
          lowercase__:							str         =     tuple(UpperCAmelCase_)
          lowercase__:							List[Any]         =     get_pairs(UpperCAmelCase_)

          if not pairs:
                 return token

          while True:
                 lowercase__:							List[str]         =     min(UpperCAmelCase_					, key=lambda UpperCAmelCase_: self.bpe_ranks.get(UpperCAmelCase_					, float("inf")))
                 if bigram not in self.bpe_ranks:
                        break
                 lowercase__    ,      lowercase__:							int         =     bigram
                 lowercase__:							Union[str, Any]         =     []
                 lowercase__:							Union[str, Any]         =     0
                 while i < len(UpperCAmelCase_):
                        try:
                               lowercase__:							str         =     word.index(UpperCAmelCase_					, UpperCAmelCase_)
                        except ValueError:
                               new_word.extend(word[i:])
                               break
                        else:
                               new_word.extend(word[i:j])
                               lowercase__:							Union[str, Any]         =     j

                        if word[i] == first and i < len(UpperCAmelCase_) - 1 and word[i + 1] == second:
                               new_word.append(first + second)
                               i += 2
                        else:
                               new_word.append(word[i])
                               i += 1
                 lowercase__:							List[Any]         =     tuple(UpperCAmelCase_)
                 lowercase__:							List[Any]         =     new_word
                 if len(UpperCAmelCase_) == 1:
                        break
                 else:
                        lowercase__:							Optional[Any]         =     get_pairs(UpperCAmelCase_)
          lowercase__:							List[str]         =     " ".join(UpperCAmelCase_)
          lowercase__:							Dict         =     word
          return word





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None)			->   List[int]:




          '''simple docstring'''




          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowercase__:							List[str]         =     [self.cls_token_id]
          lowercase__:							int         =     [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None					, UpperCAmelCase_ = False)			->   List[int]:




          '''simple docstring'''




          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=UpperCAmelCase_					, token_ids_a=UpperCAmelCase_					, already_has_special_tokens=UpperCAmelCase_)

          if token_ids_a is None:
                 return [1] + ([0] * len(UpperCAmelCase_)) + [1]
          return [1] + ([0] * len(UpperCAmelCase_)) + [1] + ([0] * len(UpperCAmelCase_)) + [1]





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None)			->   List[int]:




          '''simple docstring'''




          lowercase__:							Dict         =     [self.sep_token_id]
          lowercase__:							Optional[Any]         =     [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) * [0] + len(token_ids_a + sep) * [1]





   def __lowercase							(       self					, UpperCAmelCase_)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							List[Any]         =     []
          for token in re.findall(self.pat					, UpperCAmelCase_):
                 lowercase__:							int         =     "".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(UpperCAmelCase_).split(" "))
          return bpe_tokens





   def __lowercase							(       self					, UpperCAmelCase_)			->   Union[str, Any]:




          '''simple docstring'''




          return self.encoder.get(UpperCAmelCase_					, self.encoder.get(self.unk_token))





   def __lowercase							(       self					, UpperCAmelCase_)			->   int:




          '''simple docstring'''




          return self.decoder.get(UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							int         =     "".join(UpperCAmelCase_)
          lowercase__:							Any         =     bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"					, errors=self.errors)
          return text





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_ = None)			->   Tuple[str]:




          '''simple docstring'''




          if not os.path.isdir(UpperCAmelCase_):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
                 return
          lowercase__:							List[str]         =     os.path.join(
              UpperCAmelCase_					, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
          lowercase__:							Union[str, Any]         =     os.path.join(
              UpperCAmelCase_					, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

          with open(UpperCAmelCase_					, "w"					, encoding="utf-8") as f:
                 f.write(json.dumps(self.encoder					, indent=2					, sort_keys=UpperCAmelCase_					, ensure_ascii=UpperCAmelCase_) + "\n")

          lowercase__:							Any         =     0
          with open(UpperCAmelCase_					, "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 UpperCAmelCase_: 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!")
                               lowercase__:							int         =     token_index
                        writer.write(" ".join(UpperCAmelCase_) + "\n")
                        index += 1

          return vocab_file, merge_file





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_=False					, **UpperCAmelCase_)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     kwargs.pop("add_prefix_space"					, self.add_prefix_space)
          if (is_split_into_words or add_prefix_space) and (len(UpperCAmelCase_) > 0 and not text[0].isspace()):
                 lowercase__:							Tuple         =     " " + text
          return (text, kwargs)





   def __lowercase							(       self					, UpperCAmelCase_)			->   List[int]:




          '''simple docstring'''




          lowercase__:							Tuple         =     []
          for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_) + [self.eos_token_id])
          if len(UpperCAmelCase_) > self.model_max_length:
                 lowercase__:							Union[str, Any]         =     input_ids[-self.model_max_length :]
          return input_ids














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 						A_					(						lowercase_			)				->List[str]:







 """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(lowercase_	,  'scaled_dot_product_attention'			) else LoRAAttnProcessor
  )
  SCREAMING_SNAKE_CASE				    =		lora_attn_processor_class(
      hidden_size=lowercase_	,  cross_attention_dim=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		AttnProcsLayers(lowercase_			)
 return lora_attn_procs, unet_lora_layers





def 						A_					(						lowercase_			)				->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		{}
 SCREAMING_SNAKE_CASE				    =		(
     LoRAAttnProcessor2_0 if hasattr(lowercase_	,  'scaled_dot_product_attention'			) else LoRAAttnProcessor
 )
 for name, module in text_encoder.named_modules():
  if name.endswith(lowercase_			):
   SCREAMING_SNAKE_CASE				    =		lora_attn_processor_class(
       hidden_size=module.out_proj.out_features	,  cross_attention_dim=lowercase_			)
 return text_lora_attn_procs





def 						A_					(						lowercase_			)				->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_attn_procs(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		AttnProcsLayers(lowercase_			)
 return text_encoder_lora_layers





def 						A_					(						lowercase_	,  lowercase_=False			)				->Optional[Any]:







 """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(lowercase_			)
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Dict)							->	str:

      """simple docstring"""
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE				    =		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  ,  )
      SCREAMING_SNAKE_CASE				    =		DDIMScheduler(
          beta_start=0.0_00_85  ,  beta_end=0.0_12  ,  beta_schedule='scaled_linear'  ,  clip_sample=lowerCAmelCase__  ,  set_alpha_to_one=lowerCAmelCase__  ,  steps_offset=1  ,  )
      torch.manual_seed(0)
      SCREAMING_SNAKE_CASE				    =		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  ,  )
      SCREAMING_SNAKE_CASE				    =		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  ,  )
      SCREAMING_SNAKE_CASE				    =		CLIPTextModel(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		create_unet_lora_layers(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_layers(lowerCAmelCase__)

      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     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		1
      SCREAMING_SNAKE_CASE				    =		1_0
      SCREAMING_SNAKE_CASE				    =		4
      SCREAMING_SNAKE_CASE				    =		(3_2, 3_2)

      SCREAMING_SNAKE_CASE				    =		torch.manual_seed(0)
      SCREAMING_SNAKE_CASE				    =		floats_tensor((batch_size, num_channels) + sizes)
      SCREAMING_SNAKE_CASE				    =		torch.randint(1  ,  lowerCAmelCase__  ,  size=(batch_size, sequence_length)  ,  generator=lowerCAmelCase__)

      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     __UpperCamelCase						(		self					:	List[str])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		7_7

      SCREAMING_SNAKE_CASE				    =		torch.randint(2  ,  5_6  ,  size=(1, max_seq_length)  ,  generator=torch.manual_seed(0))

      SCREAMING_SNAKE_CASE				    =		{}
      SCREAMING_SNAKE_CASE				    =		inputs
      return prepared_inputs





     def     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	List[Any])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      LoraLoaderMixin.save_lora_weights(
          save_directory=lowerCAmelCase__  ,  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(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))





     def     __UpperCamelCase						(		self					:	Any)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  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(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





     def     __UpperCamelCase						(		self					:	Tuple)							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  unet_lora_layers=lora_components['unet_lora_layers']  ,  text_encoder_lora_layers=lora_components['text_encoder_lora_layers']  ,  safe_serialization=lowerCAmelCase__  ,  )
       self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.safetensors')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





     def     __UpperCamelCase						(		self					:	List[str])							->	List[str]:

      """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(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).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(lowerCAmelCase__)
       unet.save_attn_procs(lowerCAmelCase__)
       self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





     def     __UpperCamelCase						(		self					:	Dict)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		self.get_dummy_tokens()

      # inference without lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_without_lora.shape == (1, 7_7, 3_2)

      # 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(lowerCAmelCase__  ,  randn_weight=lowerCAmelCase__)

      # monkey patch
      pipe._modify_text_encoder(lowerCAmelCase__)

      # 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(**lowerCAmelCase__)[0]
      assert outputs_with_lora.shape == (1, 7_7, 3_2)

      assert torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "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(lowerCAmelCase__  ,  randn_weight=lowerCAmelCase__)

      # monkey patch
      pipe._modify_text_encoder(lowerCAmelCase__)

      # 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(**lowerCAmelCase__)[0]
      assert outputs_with_lora.shape == (1, 7_7, 3_2)

      assert not torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		self.get_dummy_tokens()

      # inference without lora
      SCREAMING_SNAKE_CASE				    =		pipe.text_encoder(**lowerCAmelCase__)[0]
      assert outputs_without_lora.shape == (1, 7_7, 3_2)

      # create lora_attn_procs with randn up.weights
      SCREAMING_SNAKE_CASE				    =		create_text_encoder_lora_attn_procs(pipe.text_encoder)
      set_lora_up_weights(lowerCAmelCase__  ,  randn_weight=lowerCAmelCase__)

      # monkey patch
      pipe._modify_text_encoder(lowerCAmelCase__)

      # 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(**lowerCAmelCase__)[0]
      assert outputs_with_lora.shape == (1, 7_7, 3_2)

      assert not torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "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(**lowerCAmelCase__)[0]
      assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

      assert torch.allclose(
          lowerCAmelCase__  ,  lowerCAmelCase__), "remove lora monkey patch should restore the original outputs"





     def     __UpperCamelCase						(		self					:	Dict)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  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(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__  ,  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(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))





     def     __UpperCamelCase						(		self					:	str)							->	Any:

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
       self.create_lora_weight_file(lowerCAmelCase__)

       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
       sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

       # check if vanilla attention processors are used
       for _, module in sd_pipe.unet.named_modules():
        if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
         self.assertIsInstance(module.processor  ,  (AttnProcessor, AttnProcessor2_0))

            # load LoRA weight file
       sd_pipe.load_lora_weights(lowerCAmelCase__)

       # check if lora attention processors are used
       for _, module in sd_pipe.unet.named_modules():
        if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
         SCREAMING_SNAKE_CASE				    =		(
             LoRAAttnProcessor2_0 if hasattr(lowerCAmelCase__  ,  'scaled_dot_product_attention') else LoRAAttnProcessor
         )
         self.assertIsInstance(module.processor  ,  lowerCAmelCase__)





     @unittest.skipIf(torch_device != 'cuda'  ,  'This test is supposed to run on GPU')
     def     __UpperCamelCase						(		self					:	str)							->	int:

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
       self.create_lora_weight_file(lowerCAmelCase__)

       SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
       SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
       sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

       # 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(lowerCAmelCase__  ,  lowerCAmelCase__):
         self.assertIsInstance(module.processor  ,  lowerCAmelCase__)

            # load LoRA weight file
       sd_pipe.load_lora_weights(lowerCAmelCase__)

       # check if lora attention processors are used
       for _, module in sd_pipe.unet.named_modules():
        if isinstance(lowerCAmelCase__  ,  lowerCAmelCase__):
         self.assertIsInstance(module.processor  ,  lowerCAmelCase__)





     @unittest.skipIf(torch_device != 'cuda'  ,  'This test is supposed to run on GPU')
     def     __UpperCamelCase						(		self					:	int)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_components()
      SCREAMING_SNAKE_CASE				    =		StableDiffusionPipeline(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		sd_pipe.to(lowerCAmelCase__)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__)

      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(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
       LoraLoaderMixin.save_lora_weights(
           save_directory=lowerCAmelCase__  ,  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(lowerCAmelCase__  ,  'pytorch_lora_weights.bin')))
       sd_pipe.load_lora_weights(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		sd_pipe(**lowerCAmelCase__).images
      SCREAMING_SNAKE_CASE				    =		lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(lowerCAmelCase__)  ,  torch.from_numpy(lowerCAmelCase__)))




