




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)






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			)			)			)
