





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE							,     torch_dtype=torch.floataa				)

    # load LoRA weight from .safetensors
    _A     =							load_file(_SCREAMING_SNAKE_CASE				)

    _A     =							[]

    # directly update weight in diffusers model
    for key in state_dict:
        # it is suggested to print out the key, it usually will be something like below
        # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

        # as we have set the alpha beforehand, so just skip
        if ".alpha" in key or key in visited:
            continue

        if "text" in key:
            _A     =							key.split('.'				)[0].split(LORA_PREFIX_TEXT_ENCODER + '_'				)[-1].split('_'				)
            _A     =							pipeline.text_encoder
        else:
            _A     =							key.split('.'				)[0].split(LORA_PREFIX_UNET + '_'				)[-1].split('_'				)
            _A     =							pipeline.unet

        # find the target layer
        _A     =							layer_infos.pop(0				)
        while len(_SCREAMING_SNAKE_CASE				) > -1:
            try:
                _A     =							curr_layer.__getattr__(_SCREAMING_SNAKE_CASE				)
                if len(_SCREAMING_SNAKE_CASE				) > 0:
                    _A     =							layer_infos.pop(0				)
                elif len(_SCREAMING_SNAKE_CASE				) == 0:
                    break
            except Exception:
                if len(_SCREAMING_SNAKE_CASE				) > 0:
                    temp_name += "_" + layer_infos.pop(0				)
                else:
                    _A     =							layer_infos.pop(0				)

        _A     =							[]
        if "lora_down" in key:
            pair_keys.append(key.replace('lora_down'							,     'lora_up'				)				)
            pair_keys.append(_SCREAMING_SNAKE_CASE				)
        else:
            pair_keys.append(_SCREAMING_SNAKE_CASE				)
            pair_keys.append(key.replace('lora_up'							,     'lora_down'				)				)

        # update weight
        if len(state_dict[pair_keys[0]].shape				) == 4:
            _A     =							state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
            _A     =							state_dict[pair_keys[1]].squeeze(3				).squeeze(2				).to(torch.floataa				)
            curr_layer.weight.data += alpha * torch.mm(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				).unsqueeze(2				).unsqueeze(3				)
        else:
            _A     =							state_dict[pair_keys[0]].to(torch.floataa				)
            _A     =							state_dict[pair_keys[1]].to(torch.floataa				)
            curr_layer.weight.data += alpha * torch.mm(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

        # update visited list
        for item in pair_keys:
            visited.append(_SCREAMING_SNAKE_CASE				)

    return pipeline


if __name__ == "__main__":
 __A							:			List[str]         =			argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.7_5, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 __A							:			int         =			parser.parse_args()

 __A							:			Optional[Any]         =			args.base_model_path
 __A							:			str         =			args.checkpoint_path
 __A							:			Union[str, Any]         =			args.dump_path
 __A							:			Tuple         =			args.lora_prefix_unet
 __A							:			Optional[int]         =			args.lora_prefix_text_encoder
 __A							:			Tuple         =			args.alpha

 __A							:			Optional[int]         =			convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 __A							:			Optional[int]         =			pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









'''simple docstring'''
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import SwiftFormerForImageClassification, SwiftFormerModel
					from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=3							,      a_=True							,      a_=True							,      a_=0.1							,      a_=0.1							,      a_=2_2_4							,      a_=1_0_0_0							,      a_=[3, 3, 6, 4]							,      a_=[4_8, 5_6, 1_1_2, 2_2_0]							,      )    ->  Union[str, Any]:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		List[Any]   					=		batch_size
					lowercase						:		Union[str, Any]   					=		num_channels
					lowercase						:		str   					=		is_training
					lowercase						:		Any   					=		use_labels
					lowercase						:		Optional[Any]   					=		hidden_dropout_prob
					lowercase						:		List[str]   					=		attention_probs_dropout_prob
					lowercase						:		Optional[Any]   					=		num_labels
					lowercase						:		str   					=		image_size
					lowercase						:		Dict   					=		layer_depths
					lowercase						:		List[str]   					=		embed_dims

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		int   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

					lowercase						:		int   					=		None
					if self.use_labels:
						lowercase						:		Optional[Any]   					=		ids_tensor([self.batch_size]							,      self.num_labels							)

					lowercase						:		List[Any]   					=		self.get_config()

					return config, pixel_values, labels

				def 	a__		(     self							)    ->  List[Any]:
					return SwiftFormerConfig(
					    depths=self.layer_depths							,      embed_dims=self.embed_dims							,      mlp_ratio=4							,      downsamples=[True, True, True, True]							,      hidden_act="gelu"							,      num_labels=self.num_labels							,      down_patch_size=3							,      down_stride=2							,      down_pad=1							,      drop_rate=0.0							,      drop_path_rate=0.0							,      use_layer_scale=a_							,      layer_scale_init_value=1e-5							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Optional[Any]:
					lowercase						:		Tuple   					=		SwiftFormerModel(config=a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Union[str, Any]   					=		model(a_							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.embed_dims[-1], 7, 7)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Any:
					lowercase						:		Dict   					=		self.num_labels
					lowercase						:		Optional[int]   					=		SwiftFormerForImageClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Tuple   					=		model(a_							,      labels=a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)

					lowercase						:		Tuple   					=		SwiftFormerForImageClassification(a_							)
					model.to(a_							)
					model.eval()

					lowercase						:		List[str]   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
					lowercase						:		str   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)


				def 	a__		(     self							)    ->  Dict:
					((lowercase)					,		(lowercase)					,		(lowercase))						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase						:		Dict   					=		{"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				_snake_case						       =				(
				    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  str:
					lowercase						:		Optional[int]   					=		SwiftFormerModelTester(self							)
					lowercase						:		int   					=		ConfigTester(
					    self							,      config_class=a_							,      has_text_modality=a_							,      hidden_size=3_7							,      num_attention_heads=1_2							,      num_hidden_layers=1_2							,      )

				def 	a__		(     self							)    ->  Any:
					self.config_tester.run_common_tests()

				@unittest.skip(reason="SwiftFormer does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  int:
					pass

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase					,		lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Optional[int]   					=		model_class(a_							)
						lowercase						:		Tuple   					=		model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(a_							,      nn.Linear							)							)

				def 	a__		(     self							)    ->  List[Any]:
					lowercase					,		lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		str   					=		model_class(a_							)
						lowercase						:		int   					=		inspect.signature(model.forward							)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						lowercase						:		Union[str, Any]   					=		[*signature.parameters.keys()]

						lowercase						:		int   					=		["pixel_values"]
						self.assertListEqual(arg_names[:1]							,      a_							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_							)

				@slow
				def 	a__		(     self							)    ->  Dict:
					for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase						:		Optional[int]   					=		SwiftFormerModel.from_pretrained(a_							)
						self.assertIsNotNone(a_							)

				@unittest.skip(reason="SwiftFormer does not output attentions"							)
				def 	a__		(     self							)    ->  Optional[Any]:
					pass

				def 	a__		(     self							)    ->  str:
					def check_hidden_states_output(a_							,      a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		model_class(a_							)
						model.to(a_							)
						model.eval()

						with torch.no_grad():
							lowercase						:		List[str]   					=		model(**self._prepare_for_class(a_							,      a_							)							)

						lowercase						:		str   					=		outputs.hidden_states

						lowercase						:		str   					=		8
						self.assertEqual(len(a_							)							,      a_							)  # TODO

						# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
						# with the width and height being successively divided by 2, after every 2 blocks
						for i in range(len(a_							)							):
							self.assertEqual(
							    hidden_states[i].shape							,      torch.Size(
							        [
							            self.model_tester.batch_size,
							            self.model_tester.embed_dims[i // 2],
							            (self.model_tester.image_size // 4) // 2 ** (i // 2),
							            (self.model_tester.image_size // 4) // 2 ** (i // 2),
							        ]							)							,      )

					lowercase					,		lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Dict   					=		True
						check_hidden_states_output(a_							,      a_							,      a_							)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						lowercase						:		str   					=		True

						check_hidden_states_output(a_							,      a_							,      a_							)

				def 	a__		(     self							)    ->  Optional[Any]:
					def _config_zero_init(a_							):
						lowercase						:		str   					=		copy.deepcopy(a_							)
						for key in configs_no_init.__dict__.keys():
							if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
								setattr(a_							,      a_							,      1e-1_0							)
							if isinstance(getattr(a_							,      a_							,      a_							)							,      a_							):
								lowercase						:		List[Any]   					=		_config_zero_init(getattr(a_							,      a_							)							)
								setattr(a_							,      a_							,      a_							)
						return configs_no_init

					lowercase					,		lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs_for_common()

					lowercase						:		List[str]   					=		_config_zero_init(a_							)
					for model_class in self.all_model_classes:
						lowercase						:		Tuple   					=		model_class(config=a_							)
						for name, param in model.named_parameters():
							if param.requires_grad:
								self.assertIn(
								    ((param.data.mean() * 1e9) / 1e9).round().item()							,      [0.0, 1.0]							,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,      )


				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
				def 	a__		(     self							)    ->  Tuple:
					pass




def 	_A  (       )  ->				List[str]:
	lowercase						:		Optional[int]   					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






@require_torch
@require_vision
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@cached_property
				def 	a__		(     self							)    ->  List[Any]:
					return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"							) if is_vision_available() else None


				@slow
				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Tuple   					=		SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"							).to(a_							)

					lowercase						:		Any   					=		self.default_image_processor
					lowercase						:		Optional[int]   					=		prepare_img()
					lowercase						:		Any   					=		image_processor(images=a_							,      return_tensors="pt"							).to(a_							)

					# forward pass
					with torch.no_grad():
						lowercase						:		List[str]   					=		model(**a_							)

					# verify the logits
					lowercase						:		str   					=		torch.Size((1, 1_0_0_0)							)
					self.assertEqual(outputs.logits.shape							,      a_							)

					lowercase						:		List[Any]   					=		torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]							).to(a_							)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      a_							,      atol=1e-4							)							)


