


import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
 from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase=3, lowerCamelCase=32, lowerCamelCase=3, lowerCamelCase=10, lowerCamelCase=[10, 20, 30, 40], lowerCamelCase=[1, 1, 2, 1], lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase="relu", lowerCamelCase=3, lowerCamelCase=None, )			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						parent
           _lowercase			:				Any 						=						batch_size
           _lowercase			:				Tuple 						=						image_size
           _lowercase			:				Union[str, Any] 						=						num_channels
           _lowercase			:				int 						=						embeddings_size
           _lowercase			:				Any 						=						hidden_sizes
           _lowercase			:				str 						=						depths
           _lowercase			:				List[Any] 						=						is_training
           _lowercase			:				Dict 						=						use_labels
           _lowercase			:				List[str] 						=						hidden_act
           _lowercase			:				List[Any] 						=						num_labels
           _lowercase			:				Optional[Any] 						=						scope
           _lowercase			:				Union[str, Any] 						=						len(lowerCamelCase)
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _lowercase			:				List[str] 						=						None
           if self.use_labels:
               _lowercase			:				str 						=						ids_tensor([self.batch_size], self.num_labels)

           _lowercase			:				Tuple 						=						self.get_config()

           return config, pixel_values, labels
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           return RegNetConfig(
               num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						RegNetModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				List[str] 						=						model(lowerCamelCase)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.num_labels
           _lowercase			:				Optional[Any] 						=						RegNetForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase			:				Optional[Any] 						=						model(lowerCamelCase, labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))




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




           """simple docstring"""

           _lowercase			:				List[Any] 						=						self.prepare_config_and_inputs()
           _lowercase						,							_lowercase						,							_lowercase			:				Optional[Any] 						=						config_and_inputs
           _lowercase			:				str 						=						{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Optional[Any]       					=     (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
       lowercase_					:      Optional[int]       					=     (
           {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
           if is_torch_available()
           else {}
       )

       lowercase_					:      Union[str, Any]       					=     False
       lowercase_					:      List[str]       					=     False
       lowercase_					:      Optional[Any]       					=     False
       lowercase_					:      List[Any]       					=     False
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						RegNetModelTester(self)
           _lowercase			:				Optional[Any] 						=						ConfigTester(self, config_class=lowerCamelCase, has_text_modality=lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           return
       @unittest.skip(reason='RegNet does not use inputs_embeds')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       @unittest.skip(reason='RegNet does not support input and output embeddings')
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				Any 						=						model_class(lowerCamelCase)
               _lowercase			:				Tuple 						=						inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _lowercase			:				Any 						=						[*signature.parameters.keys()]

               _lowercase			:				Dict 						=						['pixel_values']
               self.assertListEqual(arg_names[:1], lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				List[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				Optional[Any] 						=						model_class(config=lowerCamelCase)
               for name, module in model.named_modules():
                   if isinstance(lowerCamelCase, (nn.BatchNormad, nn.GroupNorm)):
                       self.assertTrue(
                           torch.all(module.weight == 1), msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
                       self.assertTrue(
                           torch.all(module.bias == 0), msg=F'''Parameter {name} of model {model_class} seems not properly initialized''', )
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           def check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase):
               _lowercase			:				str 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()

               with torch.no_grad():
                   _lowercase			:				Any 						=						model(**self._prepare_for_class(lowerCamelCase, lowerCamelCase))

               _lowercase			:				List[str] 						=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               _lowercase			:				int 						=						self.model_tester.num_stages
               self.assertEqual(len(lowerCamelCase), expected_num_stages + 1)

               # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 2, self.model_tester.image_size // 2], )

           _lowercase						,							_lowercase			:				Any 						=						self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase			:				Any 						=						['basic', 'bottleneck']
           for model_class in self.all_model_classes:
               for layer_type in layers_type:
                   _lowercase			:				Dict 						=						layer_type
                   _lowercase			:				Any 						=						True
                   check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase)

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

                   check_hidden_states_output(lowerCamelCase, lowerCamelCase, lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)




       @slow
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase			:				Optional[Any] 						=						RegNetModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def 							UpperCamelCase_( )       ->		List[str]:
    _lowercase			:				int 						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image






@require_torch
@require_vision
class _lowerCamelCase(		unittest.TestCase  ):
       @cached_property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           return (
               AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )




       @slow
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Dict 						=						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowerCamelCase)

           _lowercase			:				Dict 						=						self.default_image_processor
           _lowercase			:				Optional[Any] 						=						prepare_img()
           _lowercase			:				List[str] 						=						image_processor(images=lowerCamelCase, return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase			:				Optional[int] 						=						model(**lowerCamelCase)

           # verify the logits
           _lowercase			:				List[str] 						=						torch.Size((1, 10_00))
           self.assertEqual(outputs.logits.shape, lowerCamelCase)

           _lowercase			:				Optional[int] 						=						torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]).to(lowerCamelCase)

           self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCamelCase, atol=1E-4))






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		lowercase__	     =						[]

		# 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:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.text_encoder
				else:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.unet

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

				lowercase__	     =						[]
				if "lora_down" in key:
						pair_keys.append(key.replace("""lora_down"""       ,       """lora_up"""	)	)
						pair_keys.append(__magic_name__	)
				else:
						pair_keys.append(__magic_name__	)
						pair_keys.append(key.replace("""lora_up"""       ,       """lora_down"""	)	)

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

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

		return pipeline


if __name__ == "__main__":
	A			:						int        =  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=0.75, 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			:						str        =  parser.parse_args()

	A			:						Tuple        =  args.base_model_path
	A			:						List[str]        =  args.checkpoint_path
	A			:						Optional[int]        =  args.dump_path
	A			:						Optional[int]        =  args.lora_prefix_unet
	A			:						Any        =  args.lora_prefix_text_encoder
	A			:						Any        =  args.alpha

	A			:						List[str]        =  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

