





'''simple docstring'''



import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_   :  Optional[int]								=      logging.get_logger()

@dataclass
class     A_ :

			'''simple docstring'''





			_lowerCAmelCase						=    42
			_lowerCAmelCase						=    field(default_factory=lowerCAmelCase_  )
			_lowerCAmelCase						=    field(default_factory=lowerCAmelCase_  )
			_lowerCAmelCase						=    field(default_factory=lowerCAmelCase_  )




			def       a	(					self			,     A_			,     A_			,     A_			,     A_   ):
								_UpperCamelCase											=   len(list(m.modules()   )   ) == 1 or isinstance(A_			,     nn.Convad   ) or isinstance(A_			,     nn.BatchNormad   )
								if has_not_submodules:
													self.traced.append(A_   )
													_UpperCamelCase											=   m




			def __call__(					self			,     A_   ):
								for name, m in self.module.named_modules():
													self.handles.append(m.register_forward_hook(partial(self._forward_hook			,     name=A_   )   )   )
								self.module(A_   )
								[x.remove() for x in self.handles]
								return self




			@property
			def       a	(					self   ):
								# check the len of the state_dict keys to see if we have learnable params
								return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()   )   ) > 0}




class     A_ (  nn.Module  ):

			'''simple docstring'''





			def __init__(					self			,     A_   ):
								super().__init__()

								_UpperCamelCase											=   []
								# - get the stem
								feature_blocks.append(("conv1", model.stem)   )
								# - get all the feature blocks
								for k, v in model.trunk_output.named_children():
													assert k.startswith("block"   ), F"Unexpected layer name {k}"
													_UpperCamelCase											=   len(A_   ) + 1
													feature_blocks.append((F"res{block_index}", v)   )

								_UpperCamelCase											=   nn.ModuleDict(A_   )




			def       a	(					self			,     A_   ):
								return get_trunk_forward_outputs(
								    A_			,     out_feat_keys=A_			,     feature_blocks=self._feature_blocks			,     )




class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			def       a	(					self   ):
								return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]




def  lowercase__(		_UpperCamelCase  : str				)->   Dict[str, str]:






					"""simple docstring"""

					_UpperCamelCase											=   RegNetConfig(depths=[2, 7, 17, 1] ,   hidden_sizes=[8, 8, 8, 8] ,   groups_width=8				)
					if "in1k" in model_name:
										_UpperCamelCase											=   RegNetForImageClassification(_UpperCamelCase				)
					else:
										_UpperCamelCase											=   RegNetModel(_UpperCamelCase				)
					# create from model (with small weights)
					_UpperCamelCase											=   FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27 ,   group_width=1010 ,   w_a=1744 ,   w_a=620.83 ,   w_m=2.52				)				)				)

					with torch.no_grad():
										_UpperCamelCase											=   from_model.eval()
										_UpperCamelCase											=   our_model.eval()

										_UpperCamelCase											=   torch.randn((1, 3, 32, 32)				)
										# trace both
										_UpperCamelCase											=   Tracker(_UpperCamelCase				)
										_UpperCamelCase											=   dest_tracker(_UpperCamelCase				).parametrized

										pprint(dest_tracker.nameamodule				)
										_UpperCamelCase											=   Tracker(_UpperCamelCase				)
										_UpperCamelCase											=   src_tracker(_UpperCamelCase				).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(_UpperCamelCase  : str				):
										_UpperCamelCase											=   OrderedDict()
										for name, module in dict_with_modules.items():
															for param_name, param in module.state_dict().items():
																				_UpperCamelCase											=   param
										return params_dict

					_UpperCamelCase											=   {}

					_UpperCamelCase											=   to_params_dict(_UpperCamelCase				)
					_UpperCamelCase											=   to_params_dict(_UpperCamelCase				)

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() ,   dst_state_dict.items()				):
										_UpperCamelCase											=   dest_key
										logger.info(f"{src_key} -> {dest_key}"				)
					# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
					if "in1k" in model_name:
										_UpperCamelCase											=   "classifier.1.weight"
										_UpperCamelCase											=   "classifier.1.bias"

					return from_to_ours_keys



def  lowercase__(		_UpperCamelCase  : Path ,   _UpperCamelCase  : str = None ,   _UpperCamelCase  : bool = True				)->   Optional[Any]:






					"""simple docstring"""

					_UpperCamelCase											=   "imagenet-1k-id2label.json"
					_UpperCamelCase											=   1000

					_UpperCamelCase											=   "huggingface/label-files"
					_UpperCamelCase											=   num_labels
					_UpperCamelCase											=   json.load(open(cached_download(hf_hub_url(_UpperCamelCase ,   _UpperCamelCase ,   repo_type="dataset"				)				) ,   "r"				)				)
					_UpperCamelCase											=   {int(_UpperCamelCase				): v for k, v in idalabel.items()}

					_UpperCamelCase											=   idalabel
					_UpperCamelCase											=   {v: k for k, v in idalabel.items()}

					_UpperCamelCase											=   partial(_UpperCamelCase ,   num_labels=_UpperCamelCase ,   idalabel=_UpperCamelCase ,   labelaid=_UpperCamelCase				)

					_UpperCamelCase											=   {
					    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1] ,   hidden_sizes=[2020, 4040, 11110, 28280] ,   groups_width=1010				),
					    # finetuned on imagenet
					    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1] ,   hidden_sizes=[2020, 4040, 11110, 28280] ,   groups_width=1010				),
					}

					# add seer weights logic
					def load_using_classy_vision(_UpperCamelCase  : str				) -> Tuple[Dict, Dict]:
										_UpperCamelCase											=   torch.hub.load_state_dict_from_url(_UpperCamelCase ,   model_dir=str(_UpperCamelCase				) ,   map_location="cpu"				)
										# check if we have a head, if yes add it
										_UpperCamelCase											=   files["classy_state_dict"]["base_model"]["model"]
										return model_state_dict["trunk"], model_state_dict["heads"]

					_UpperCamelCase											=   {
					    "regnet-y-10b-seer": partial(
					        _UpperCamelCase ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch" ,   ),
					    "regnet-y-10b-seer-in1k": partial(
					        _UpperCamelCase ,   "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch" ,   ),
					}

					_UpperCamelCase											=   get_from_to_our_keys(_UpperCamelCase				)

					if not (save_directory / f"{model_name}.pth").exists():
										logger.info("Loading original state_dict."				)
										_UpperCamelCase				,	_UpperCamelCase											=   names_to_from_model[model_name]()
										_UpperCamelCase											=   from_state_dict_trunk
										if "in1k" in model_name:
															# add the head
															_UpperCamelCase											=   {**from_state_dict_trunk, **from_state_dict_head}
										logger.info("Done!"				)

										_UpperCamelCase											=   {}

										_UpperCamelCase											=   list(from_state_dict.keys()				)
										_UpperCamelCase											=   R"\.block.-part."
										# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
										for key in from_state_dict.keys():
															# remove the weird "block[0,1]-part" from the key
															_UpperCamelCase											=   re.sub(_UpperCamelCase ,   "" ,   _UpperCamelCase				)
															# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
															_UpperCamelCase											=   from_to_ours_keys[src_key]
															# store the parameter with our key
															_UpperCamelCase											=   from_state_dict[key]
															not_used_keys.remove(_UpperCamelCase				)
										# check that all keys have been updated
										assert len(_UpperCamelCase				) == 0, f"Some keys where not used {','.join(_UpperCamelCase				)}"

										logger.info(f"The following keys were not used: {','.join(_UpperCamelCase				)}"				)

										# save our state dict to disk
										torch.save(_UpperCamelCase ,   save_directory / f"{model_name}.pth"				)

										del converted_state_dict
					else:
										logger.info("The state_dict was already stored on disk."				)
					if push_to_hub:
										logger.info(f"Token is {os.environ['HF_TOKEN']}"				)
										logger.info("Loading our model."				)
										# create our model
										_UpperCamelCase											=   names_to_config[model_name]
										_UpperCamelCase											=   RegNetModel
										if "in1k" in model_name:
															_UpperCamelCase											=   RegNetForImageClassification
										_UpperCamelCase											=   our_model_func(_UpperCamelCase				)
										# place our model to the meta device (so remove all the weights)
										our_model.to(torch.device("meta"				)				)
										logger.info("Loading state_dict in our model."				)
										# load state dict
										_UpperCamelCase											=   our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    _UpperCamelCase ,   _UpperCamelCase ,   [save_directory / f"{model_name}.pth"]				)
										logger.info("Finally, pushing!"				)
										# push it to hub
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / model_name ,   commit_message="Add model" ,   output_dir=save_directory / model_name ,   )
										_UpperCamelCase											=   384
										# we can use the convnext one
										_UpperCamelCase											=   AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" ,   size=_UpperCamelCase				)
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / model_name ,   commit_message="Add image processor" ,   output_dir=save_directory / model_name ,   )


if __name__ == "__main__":
			snake_case_   :  List[str]								=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default=None,
			    type=str,
			    help=(
			        '''The name of the model you wish to convert, it must be one of the supported regnet* architecture,'''
			        ''' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=Path,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    default=True,
			    type=bool,
			    required=False,
			    help='''If True, push model and image processor to the hub.''',
			)

			snake_case_   :  Any								=      parser.parse_args()

			snake_case_   :  Path								=      args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''



import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device



def  lowercase__(		_UpperCamelCase  : nn.Module				)->   Any:






					"""simple docstring"""

					_UpperCamelCase											=   {}
					for name in unet.attn_processors.keys():
										_UpperCamelCase											=   None if name.endswith("attn1.processor"				) else unet.config.cross_attention_dim
										if name.startswith("mid_block"				):
															_UpperCamelCase											=   unet.config.block_out_channels[-1]
										elif name.startswith("up_blocks"				):
															_UpperCamelCase											=   int(name[len("up_blocks."				)]				)
															_UpperCamelCase											=   list(reversed(unet.config.block_out_channels				)				)[block_id]
										elif name.startswith("down_blocks"				):
															_UpperCamelCase											=   int(name[len("down_blocks."				)]				)
															_UpperCamelCase											=   unet.config.block_out_channels[block_id]
										_UpperCamelCase											=   (
										    LoRAAttnProcessor2_0 if hasattr(_UpperCamelCase ,   "scaled_dot_product_attention"				) else LoRAAttnProcessor
										)
										_UpperCamelCase											=   lora_attn_processor_class(
										    hidden_size=_UpperCamelCase ,   cross_attention_dim=_UpperCamelCase				)
					_UpperCamelCase											=   AttnProcsLayers(_UpperCamelCase				)
					return lora_attn_procs, unet_lora_layers



def  lowercase__(		_UpperCamelCase  : nn.Module				)->   List[Any]:






					"""simple docstring"""

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



def  lowercase__(		_UpperCamelCase  : nn.Module				)->   Dict:






					"""simple docstring"""

					_UpperCamelCase											=   create_text_encoder_lora_attn_procs(_UpperCamelCase				)
					_UpperCamelCase											=   AttnProcsLayers(_UpperCamelCase				)
					return text_encoder_lora_layers



def  lowercase__(		_UpperCamelCase  : List[str] ,   _UpperCamelCase  : Optional[int]=False				)->   Optional[int]:






					"""simple docstring"""

					for _, attn_proc in text_lora_attn_procs.items():
										# set up.weights
										for layer_name, layer_module in attn_proc.named_modules():
															if layer_name.endswith("_lora"				):
																				_UpperCamelCase											=   (
																				    torch.randn_like(layer_module.up.weight				)
																				    if randn_weight
																				    else torch.zeros_like(layer_module.up.weight				)
																				)
																				_UpperCamelCase											=   torch.nn.Parameter(_UpperCamelCase				)

class     A_ (  unittest.TestCase  ):

			'''simple docstring'''





			def       a	(					self   ):
								torch.manual_seed(0   )
								_UpperCamelCase											=   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			,     )
								_UpperCamelCase											=   DDIMScheduler(
								    beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=A_			,     set_alpha_to_one=A_			,     steps_offset=1			,     )
								torch.manual_seed(0   )
								_UpperCamelCase											=   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			,     )
								_UpperCamelCase											=   CLIPTextConfig(
								    bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1e-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=10_00			,     )
								_UpperCamelCase											=   CLIPTextModel(A_   )
								_UpperCamelCase											=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

								_UpperCamelCase				,	_UpperCamelCase											=   create_unet_lora_layers(A_   )
								_UpperCamelCase											=   create_text_encoder_lora_layers(A_   )

								_UpperCamelCase											=   {
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "safety_checker": None,
								    "feature_extractor": None,
								}
								_UpperCamelCase											=   {
								    "unet_lora_layers": unet_lora_layers,
								    "text_encoder_lora_layers": text_encoder_lora_layers,
								    "unet_lora_attn_procs": unet_lora_attn_procs,
								}
								return pipeline_components, lora_components




			def       a	(					self   ):
								_UpperCamelCase											=   1
								_UpperCamelCase											=   10
								_UpperCamelCase											=   4
								_UpperCamelCase											=   (32, 32)

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

								_UpperCamelCase											=   {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "guidance_scale": 6.0,
								    "output_type": "numpy",
								}

								return noise, input_ids, pipeline_inputs

								# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb




			def       a	(					self   ):
								_UpperCamelCase											=   77

								_UpperCamelCase											=   torch.randint(2			,     56			,     size=(1, max_seq_length)			,     generator=torch.manual_seed(0   )   )

								_UpperCamelCase											=   {}
								_UpperCamelCase											=   inputs
								return prepared_inputs




			def       a	(					self			,     A_   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								LoraLoaderMixin.save_lora_weights(
								    save_directory=A_			,     unet_lora_layers=lora_components["unet_lora_layers"]			,     text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]			,     )
								self.assertTrue(os.path.isfile(os.path.join(A_			,     "pytorch_lora_weights.bin"   )   )   )




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

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

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

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

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   lora_components["unet_lora_attn_procs"]
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
													_UpperCamelCase											=   sd_pipe.unet
													unet.set_attn_processor(A_   )
													unet.save_attn_procs(A_   )
													self.assertTrue(os.path.isfile(os.path.join(A_			,     "pytorch_lora_weights.bin"   )   )   )
													sd_pipe.load_lora_weights(A_   )

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

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )

								_UpperCamelCase											=   self.get_dummy_tokens()

								# inference without lora
								_UpperCamelCase											=   pipe.text_encoder(**A_   )[0]
								assert outputs_without_lora.shape == (1, 77, 32)

								# create lora_attn_procs with zeroed out up.weights
								_UpperCamelCase											=   create_text_encoder_lora_attn_procs(pipe.text_encoder   )
								set_lora_up_weights(A_			,     randn_weight=A_   )

								# monkey patch
								pipe._modify_text_encoder(A_   )

								# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
								del text_attn_procs
								gc.collect()

								# inference with lora
								_UpperCamelCase											=   pipe.text_encoder(**A_   )[0]
								assert outputs_with_lora.shape == (1, 77, 32)

								assert torch.allclose(
								    A_			,     A_   ), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

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

								# monkey patch
								pipe._modify_text_encoder(A_   )

								# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
								del text_attn_procs
								gc.collect()

								# inference with lora
								_UpperCamelCase											=   pipe.text_encoder(**A_   )[0]
								assert outputs_with_lora.shape == (1, 77, 32)

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )

								_UpperCamelCase											=   self.get_dummy_tokens()

								# inference without lora
								_UpperCamelCase											=   pipe.text_encoder(**A_   )[0]
								assert outputs_without_lora.shape == (1, 77, 32)

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

								# monkey patch
								pipe._modify_text_encoder(A_   )

								# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
								del text_attn_procs
								gc.collect()

								# inference with lora
								_UpperCamelCase											=   pipe.text_encoder(**A_   )[0]
								assert outputs_with_lora.shape == (1, 77, 32)

								assert not torch.allclose(
								    A_			,     A_   ), "lora outputs should be different to without lora outputs"

								# remove monkey patch
								pipe._remove_text_encoder_monkey_patch()

								# inference with removed lora
								_UpperCamelCase											=   pipe.text_encoder(**A_   )[0]
								assert outputs_without_lora_removed.shape == (1, 77, 32)

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




			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

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

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

								_UpperCamelCase											=   sd_pipe(**A_			,     cross_attention_kwargs={"scale": 0.5}   ).images
								_UpperCamelCase											=   lora_images_with_scale[0, -3:, -3:, -1]

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




			def       a	(					self   ):
								with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(A_   )

													_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
													_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
													_UpperCamelCase											=   sd_pipe.to(A_   )
													sd_pipe.set_progress_bar_config(disable=A_   )

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

            # load LoRA weight file
													sd_pipe.load_lora_weights(A_   )

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




			@unittest.skipIf(torch_device != "cuda"			,     "This test is supposed to run on GPU"   )
			def       a	(					self   ):
								with tempfile.TemporaryDirectory() as tmpdirname:
													self.create_lora_weight_file(A_   )

													_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
													_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
													_UpperCamelCase											=   sd_pipe.to(A_   )
													sd_pipe.set_progress_bar_config(disable=A_   )

													# enable XFormers
													sd_pipe.enable_xformers_memory_efficient_attention()

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

            # load LoRA weight file
													sd_pipe.load_lora_weights(A_   )

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




			@unittest.skipIf(torch_device != "cuda"			,     "This test is supposed to run on GPU"   )
			def       a	(					self   ):
								_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_components()
								_UpperCamelCase											=   StableDiffusionPipeline(**A_   )
								_UpperCamelCase											=   sd_pipe.to(A_   )
								sd_pipe.set_progress_bar_config(disable=A_   )

								_UpperCamelCase				,	_UpperCamelCase				,	_UpperCamelCase											=   self.get_dummy_inputs()

								# enable XFormers
								sd_pipe.enable_xformers_memory_efficient_attention()

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

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

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

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



