import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase      			=     logging.get_logger(__name__)


@dataclass
class _a       :

	'''simple docstring'''



	lowerCamelCase_   :      nn.Module
	lowerCamelCase_   :      List[nn.Module]       =	field(default_factory=lowerCAmelCase__ )
	lowerCamelCase_   :      list       =	field(default_factory=lowerCAmelCase__ )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			__A							:   int	  =				len(list(m.modules()   )   ) == 1 or isinstance(__UpperCAmelCase							,			nn.Convad   ) or isinstance(__UpperCAmelCase							,			nn.BatchNormad   )
			if has_not_submodules:
					if not isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
							self.traced.append(__UpperCAmelCase   )

	def __call__(       self							,			__UpperCAmelCase   ):
			for m in self.module.modules():
					self.handles.append(m.register_forward_hook(self._forward_hook   )   )
			self.module(__UpperCAmelCase   )
			[x.remove() for x in self.handles]
			return self







	@property
	def 					__UpperCAmelCase(       self   ):
			# check the len of the state_dict keys to see if we have learnable params
			return list(filter(lambda __UpperCAmelCase   : len(list(x.state_dict().keys()   )   ) > 0							,			self.traced   )   )


@dataclass
class _a       :

	'''simple docstring'''



	lowerCamelCase_   :      nn.Module
	lowerCamelCase_   :      nn.Module
	lowerCamelCase_   :      int       =	0
	lowerCamelCase_   :      List       =	field(default_factory=lowerCAmelCase__ )
	lowerCamelCase_   :      List       =	field(default_factory=lowerCAmelCase__ )

	def __call__(       self							,			__UpperCAmelCase   ):
			__A							:   List[str]	  =				Tracker(self.dest   )(__UpperCAmelCase   ).parametrized
			__A							:   Dict	  =				Tracker(self.src   )(__UpperCAmelCase   ).parametrized

			__A							:   Dict	  =				list(filter(lambda __UpperCAmelCase   : type(__UpperCAmelCase   ) not in self.src_skip							,			__UpperCAmelCase   )   )
			__A							:   Optional[Any]	  =				list(filter(lambda __UpperCAmelCase   : type(__UpperCAmelCase   ) not in self.dest_skip							,			__UpperCAmelCase   )   )

			if len(__UpperCAmelCase   ) != len(__UpperCAmelCase   ):
					raise Exception(
					    F"Numbers of operations are different. Source module has {len(__UpperCAmelCase   )} operations while"
					    F" destination module has {len(__UpperCAmelCase   )}."   )

			for dest_m, src_m in zip(__UpperCAmelCase							,			__UpperCAmelCase   ):
					dest_m.load_state_dict(src_m.state_dict()   )
					if self.verbose == 1:
							print(F"Transfered from={src_m} to={dest_m}"   )



def    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	nn.Module:
		# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
		__A							:   List[str]	  =				from_model.state_dict()
		__A							:   Any	  =				our_model.state_dict()
		__A							:   Optional[Any]	  =				our_model.config
		__A							:   str	  =				[]
		for stage_idx in range(len(config.hidden_sizes    )    ):
				for block_id in range(config.depths[stage_idx]    ):
						__A							:   Optional[int]	  =				F"block{stage_idx + 1}.{block_id}.layer_scale_1"
						__A							:   List[str]	  =				F"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

						all_keys.append((from_key, to_key)    )
						__A							:   List[str]	  =				F"block{stage_idx + 1}.{block_id}.layer_scale_2"
						__A							:   int	  =				F"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

						all_keys.append((from_key, to_key)    )

		for from_key, to_key in all_keys:
				__A							:   Tuple	  =				from_state_dict.pop(_lowercase    )

		our_model.load_state_dict(_lowercase    )
		return our_model



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase ,     _lowercase = True ,     )							->	str:
		print(F"Downloading weights for {name}..."    )
		__A							:   Optional[Any]	  =				cached_download(_lowercase    )
		print(F"Converting {name}..."    )
		__A							:   Optional[int]	  =				torch.load(_lowercase    )["state_dict"]
		from_model.load_state_dict(_lowercase    )
		from_model.eval()
		with torch.no_grad():
				__A							:   Union[str, Any]	  =				VanForImageClassification(_lowercase    ).eval()
				__A							:   int	  =				ModuleTransfer(src=_lowercase ,     dest=_lowercase    )
				__A							:   Tuple	  =				torch.randn((1, 3, 224, 224)    )
				module_transfer(_lowercase    )
				__A							:   Optional[int]	  =				copy_parameters(_lowercase ,     _lowercase    )

		if not torch.allclose(from_model(_lowercase    ) ,     our_model(_lowercase    ).logits    ):
				raise ValueError("The model logits don't match the original one."    )

		__A							:   Union[str, Any]	  =				name
		print(_lowercase    )

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name ,     commit_message="Add model" ,     use_temp_dir=_lowercase ,     )

				# we can use the convnext one
				__A							:   Optional[int]	  =				AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"    )
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name ,     commit_message="Add image processor" ,     use_temp_dir=_lowercase ,     )

				print(F"Pushed {checkpoint_name}"    )



def    lowerCamelCase_   (  _lowercase ,     _lowercase = None ,     _lowercase = True    )							->	Union[str, Any]:
		__A							:   Optional[int]	  =				"imagenet-1k-id2label.json"
		__A							:   Optional[int]	  =				1_000

		__A							:   Any	  =				"huggingface/label-files"
		__A							:   int	  =				num_labels
		__A							:   Tuple	  =				json.load(open(hf_hub_download(_lowercase ,     _lowercase ,     repo_type="dataset"    ) ,     "r"    )    )
		__A							:   Optional[Any]	  =				{int(_lowercase    ): v for k, v in idalabel.items()}

		__A							:   Tuple	  =				idalabel
		__A							:   List[Any]	  =				{v: k for k, v in idalabel.items()}

		__A							:   Tuple	  =				partial(_lowercase ,     num_labels=_lowercase ,     idalabel=_lowercase ,     labelaid=_lowercase    )

		__A							:   Optional[int]	  =				{
		    "van-tiny": ImageNetPreTrainedConfig(
		        hidden_sizes=[32, 64, 160, 256] ,     depths=[3, 3, 5, 2] ,     mlp_ratios=[8, 8, 4, 4] ,     ),
		    "van-small": ImageNetPreTrainedConfig(
		        hidden_sizes=[64, 128, 320, 512] ,     depths=[2, 2, 4, 2] ,     mlp_ratios=[8, 8, 4, 4] ,     ),
		    "van-base": ImageNetPreTrainedConfig(
		        hidden_sizes=[64, 128, 320, 512] ,     depths=[3, 3, 12, 3] ,     mlp_ratios=[8, 8, 4, 4] ,     ),
		    "van-large": ImageNetPreTrainedConfig(
		        hidden_sizes=[64, 128, 320, 512] ,     depths=[3, 5, 27, 3] ,     mlp_ratios=[8, 8, 4, 4] ,     ),
		}

		__A							:   Any	  =				{
		    "van-tiny": van_tiny,
		    "van-small": van_small,
		    "van-base": van_base,
		    "van-large": van_large,
		}

		__A							:   Tuple	  =				{
		    "van-tiny": (
		        "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
		    ),
		    "van-small": (
		        "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
		    ),
		    "van-base": (
		        "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
		    ),
		    "van-large": (
		        "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
		    ),
		}

		if model_name:
				convert_weight_and_push(
				    _lowercase ,     names_to_config[model_name] ,     checkpoint=names_to_original_checkpoints[model_name] ,     from_model=names_to_original_models[model_name]() ,     save_directory=_lowercase ,     push_to_hub=_lowercase ,     )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    _lowercase ,     _lowercase ,     checkpoint=names_to_original_checkpoints[model_name] ,     from_model=names_to_original_models[model_name]() ,     save_directory=_lowercase ,     push_to_hub=_lowercase ,     )


if __name__ == "__main__":
					UpperCamelCase      			=     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 resnet* architecture,'
					        ' currently: van-tiny/small/base/large. 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(
					    '--van_dir',
					    required=True,
					    type=Path,
					    help=(
					        'A path to VAN\'s original implementation directory. You can download from here:'
					        ' https://github.com/Visual-Attention-Network/VAN-Classification'
					    ),
					)
					parser.add_argument(
					    '--push_to_hub',
					    default=True,
					    type=bool,
					    required=False,
					    help='If True, push model and image processor to the hub.',
					)

					UpperCamelCase      			=     parser.parse_args()
					UpperCamelCase      			=     args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					UpperCamelCase      			=     args.van_dir
					# append the path to the parents to maskformer dir
					sys.path.append(str(van_dir.parent))
					from van.models.van import van_base, van_large, van_small, van_tiny

					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






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



def    lowerCamelCase_   (  _lowercase    )							->	Optional[int]:
		__A							:   int	  =				{}
		__A							:   int	  =				(
		    LoRAAttnProcessor2_0 if hasattr(_lowercase ,     "scaled_dot_product_attention"    ) else LoRAAttnProcessor
		)
		for name, module in text_encoder.named_modules():
				if name.endswith(_lowercase    ):
						__A							:   Any	  =				lora_attn_processor_class(
						    hidden_size=module.out_proj.out_features ,     cross_attention_dim=_lowercase    )
		return text_lora_attn_procs



def    lowerCamelCase_   (  _lowercase    )							->	Optional[int]:
		__A							:   List[Any]	  =				create_text_encoder_lora_attn_procs(_lowercase    )
		__A							:   Optional[int]	  =				AttnProcsLayers(_lowercase    )
		return text_encoder_lora_layers



def    lowerCamelCase_   (  _lowercase ,     _lowercase=False    )							->	str:
		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"    ):
								__A							:   Optional[int]	  =				(
								    torch.randn_like(layer_module.up.weight    )
								    if randn_weight
								    else torch.zeros_like(layer_module.up.weight    )
								)
								__A							:   List[Any]	  =				torch.nn.Parameter(_lowercase    )





class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			torch.manual_seed(0   )
			__A							:   Any	  =				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							,			)
			__A							:   List[str]	  =				DDIMScheduler(
			    beta_start=0.0_00_85							,			beta_end=0.0_12							,			beta_schedule="scaled_linear"							,			clip_sample=__UpperCAmelCase							,			set_alpha_to_one=__UpperCAmelCase							,			steps_offset=1							,			)
			torch.manual_seed(0   )
			__A							:   int	  =				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							,			)
			__A							:   Dict	  =				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=1_000							,			)
			__A							:   List[str]	  =				CLIPTextModel(__UpperCAmelCase   )
			__A							:   Optional[int]	  =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

			__A  ,       __A							:   List[str]	  =				create_unet_lora_layers(__UpperCAmelCase   )
			__A							:   Union[str, Any]	  =				create_text_encoder_lora_layers(__UpperCAmelCase   )

			__A							:   Dict	  =				{
			    "unet": unet,
			    "scheduler": scheduler,
			    "vae": vae,
			    "text_encoder": text_encoder,
			    "tokenizer": tokenizer,
			    "safety_checker": None,
			    "feature_extractor": None,
			}
			__A							:   Union[str, Any]	  =				{
			    "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   ):
			__A							:   Optional[Any]	  =				1
			__A							:   Any	  =				10
			__A							:   Tuple	  =				4
			__A							:   str	  =				(32, 32)

			__A							:   int	  =				torch.manual_seed(0   )
			__A							:   Optional[Any]	  =				floats_tensor((batch_size, num_channels) + sizes   )
			__A							:   int	  =				torch.randint(1							,			__UpperCAmelCase							,			size=(batch_size, sequence_length)							,			generator=__UpperCAmelCase   )

			__A							:   Any	  =				{
			    "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   ):
			__A							:   List[str]	  =				77

			__A							:   List[Any]	  =				torch.randint(2							,			56							,			size=(1, max_seq_length)							,			generator=torch.manual_seed(0   )   )

			__A							:   Optional[Any]	  =				{}
			__A							:   Optional[int]	  =				inputs
			return prepared_inputs

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A  ,       __A							:   Optional[Any]	  =				self.get_dummy_components()
			LoraLoaderMixin.save_lora_weights(
			    save_directory=__UpperCAmelCase							,			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(__UpperCAmelCase							,			"pytorch_lora_weights.bin"   )   )   )

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Tuple	  =				self.get_dummy_components()
			__A							:   Optional[Any]	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Dict	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   Dict	  =				self.get_dummy_inputs()

			__A							:   Any	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				original_images[0, -3:, -3:, -1]

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

			__A							:   str	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   int	  =				lora_images[0, -3:, -3:, -1]

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

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Dict	  =				self.get_dummy_components()
			__A							:   int	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Tuple	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   str	  =				self.get_dummy_inputs()

			__A							:   List[str]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				original_images[0, -3:, -3:, -1]

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

			__A							:   Any	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   List[str]	  =				lora_images[0, -3:, -3:, -1]

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

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Any	  =				self.get_dummy_components()
			__A							:   Optional[Any]	  =				lora_components["unet_lora_attn_procs"]
			__A							:   Optional[Any]	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Any	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   List[str]	  =				self.get_dummy_inputs()

			__A							:   str	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   List[Any]	  =				original_images[0, -3:, -3:, -1]

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

			__A							:   Optional[Any]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				lora_images[0, -3:, -3:, -1]

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

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   List[str]	  =				self.get_dummy_components()
			__A							:   Dict	  =				StableDiffusionPipeline(**__UpperCAmelCase   )

			__A							:   int	  =				self.get_dummy_tokens()

			# inference without lora
			__A							:   Dict	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_without_lora.shape == (1, 77, 32)

			# create lora_attn_procs with zeroed out up.weights
			__A							:   Any	  =				create_text_encoder_lora_attn_procs(pipe.text_encoder   )
			set_lora_up_weights(__UpperCAmelCase							,			randn_weight=__UpperCAmelCase   )

			# monkey patch
			pipe._modify_text_encoder(__UpperCAmelCase   )

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

			# inference with lora
			__A							:   Union[str, Any]	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_with_lora.shape == (1, 77, 32)

			assert torch.allclose(
			    __UpperCAmelCase							,			__UpperCAmelCase   ), "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
			__A							:   Optional[Any]	  =				create_text_encoder_lora_attn_procs(pipe.text_encoder   )
			set_lora_up_weights(__UpperCAmelCase							,			randn_weight=__UpperCAmelCase   )

			# monkey patch
			pipe._modify_text_encoder(__UpperCAmelCase   )

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

			# inference with lora
			__A							:   Tuple	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_with_lora.shape == (1, 77, 32)

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

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   Any	  =				self.get_dummy_components()
			__A							:   Any	  =				StableDiffusionPipeline(**__UpperCAmelCase   )

			__A							:   str	  =				self.get_dummy_tokens()

			# inference without lora
			__A							:   Tuple	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_without_lora.shape == (1, 77, 32)

			# create lora_attn_procs with randn up.weights
			__A							:   Union[str, Any]	  =				create_text_encoder_lora_attn_procs(pipe.text_encoder   )
			set_lora_up_weights(__UpperCAmelCase							,			randn_weight=__UpperCAmelCase   )

			# monkey patch
			pipe._modify_text_encoder(__UpperCAmelCase   )

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

			# inference with lora
			__A							:   List[str]	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_with_lora.shape == (1, 77, 32)

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

			# remove monkey patch
			pipe._remove_text_encoder_monkey_patch()

			# inference with removed lora
			__A							:   Optional[int]	  =				pipe.text_encoder(**__UpperCAmelCase   )[0]
			assert outputs_without_lora_removed.shape == (1, 77, 32)

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

	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   List[str]	  =				self.get_dummy_components()
			__A							:   Tuple	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Optional[Any]	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   Tuple	  =				self.get_dummy_inputs()

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

			__A							:   int	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   List[str]	  =				lora_images[0, -3:, -3:, -1]

			__A							:   Dict	  =				sd_pipe(**__UpperCAmelCase							,			cross_attention_kwargs={"scale": 0.5}   ).images
			__A							:   Optional[int]	  =				lora_images_with_scale[0, -3:, -3:, -1]

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

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

					__A  ,       __A							:   Optional[Any]	  =				self.get_dummy_components()
					__A							:   int	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
					__A							:   List[Any]	  =				sd_pipe.to(__UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

            # load LoRA weight file
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

					# check if lora attention processors are used
					for _, module in sd_pipe.unet.named_modules():
							if isinstance(__UpperCAmelCase							,			__UpperCAmelCase   ):
									__A							:   Optional[Any]	  =				(
									    LoRAAttnProcessor2_0 if hasattr(__UpperCAmelCase							,			"scaled_dot_product_attention"   ) else LoRAAttnProcessor
									)
									self.assertIsInstance(module.processor							,			__UpperCAmelCase   )

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

					__A  ,       __A							:   Optional[int]	  =				self.get_dummy_components()
					__A							:   int	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
					__A							:   Optional[int]	  =				sd_pipe.to(__UpperCAmelCase   )
					sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

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

            # load LoRA weight file
					sd_pipe.load_lora_weights(__UpperCAmelCase   )

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







	@unittest.skipIf(torch_device != "cuda"							,			"This test is supposed to run on GPU"   )
	def 					__UpperCAmelCase(       self   ):
			__A  ,       __A							:   List[str]	  =				self.get_dummy_components()
			__A							:   Tuple	  =				StableDiffusionPipeline(**__UpperCAmelCase   )
			__A							:   Any	  =				sd_pipe.to(__UpperCAmelCase   )
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A  ,       __A  ,       __A							:   Optional[int]	  =				self.get_dummy_inputs()

			# enable XFormers
			sd_pipe.enable_xformers_memory_efficient_attention()

			__A							:   Union[str, Any]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				original_images[0, -3:, -3:, -1]

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

			__A							:   Optional[Any]	  =				sd_pipe(**__UpperCAmelCase   ).images
			__A							:   Tuple	  =				lora_images[0, -3:, -3:, -1]

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





