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  __magic_name__  :





   """simple docstring"""




   def __init__(	self					:  Any		,  _lowercase					:  int		,  _lowercase					:  Optional[int]=13		,  _lowercase					:  Union[str, Any]=3		,  _lowercase					:  int=True		,  _lowercase					:  Union[str, Any]=True		,  _lowercase					:  Any=0.1		,  _lowercase					:  str=0.1		,  _lowercase					:  Tuple=224		,  _lowercase					:  Any=1_000		,  _lowercase					:  List[str]=[3, 3, 6, 4]		,  _lowercase					:  List[str]=[48, 56, 112, 220]		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= parent
    _UpperCamelCase:     int							= batch_size
    _UpperCamelCase:     Tuple							= num_channels
    _UpperCamelCase:     List[Any]							= is_training
    _UpperCamelCase:     Optional[Any]							= use_labels
    _UpperCamelCase:     Optional[Any]							= hidden_dropout_prob
    _UpperCamelCase:     List[Any]							= attention_probs_dropout_prob
    _UpperCamelCase:     Optional[Any]							= num_labels
    _UpperCamelCase:     List[Any]							= image_size
    _UpperCamelCase:     str							= layer_depths
    _UpperCamelCase:     Any							= embed_dims



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

    _UpperCamelCase:     Dict							= None
    if self.use_labels:
     _UpperCamelCase:     Union[str, Any]							= ids_tensor([self.batch_size]		,  self.num_labels							)

    _UpperCamelCase:     Tuple							= self.get_config()

    return config, pixel_values, labels



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    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=_lowercase		,  layer_scale_init_value=1E-5		,  )



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  List[Any]		,  _lowercase					:  Any		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= SwiftFormerModel(config=_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Any							= model(_lowercase							)
    self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, self.embed_dims[-1], 7, 7)							)



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= self.num_labels
    _UpperCamelCase:     Optional[Any]							= SwiftFormerForImageClassification(_lowercase							)
    model.to(_lowercase							)
    model.eval()
    _UpperCamelCase:     Tuple							= model(_lowercase		,  labels=_lowercase							)
    self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.num_labels)							)

    _UpperCamelCase:     str							= SwiftFormerForImageClassification(_lowercase							)
    model.to(_lowercase							)
    model.eval()

    _UpperCamelCase:     Optional[int]							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
    _UpperCamelCase:     int							= model(_lowercase							)
    self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.num_labels)							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    ((_UpperCamelCase)	,							(_UpperCamelCase)	,							(_UpperCamelCase)):     str							= self.prepare_config_and_inputs()
    _UpperCamelCase:     str							= {'''pixel_values''': pixel_values}
    return config, inputs_dict




@require_torch
class  __magic_name__  (		__a      ,	__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Union[str, Any]  			=   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
   lowerCAmelCase      :    List[str]  			=   (
       {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
       if is_torch_available()
       else {}
   )

   lowerCAmelCase      :    Optional[Any]  			=   False
   lowerCAmelCase      :    Optional[int]  			=   False
   lowerCAmelCase      :    Optional[Any]  			=   False
   lowerCAmelCase      :    Union[str, Any]  			=   False
   lowerCAmelCase      :    Optional[Any]  			=   False



   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= SwiftFormerModelTester(self							)
    _UpperCamelCase:     int							= ConfigTester(
        self		,  config_class=_lowercase		,  has_text_modality=_lowercase		,  hidden_size=37		,  num_attention_heads=12		,  num_hidden_layers=12		,  )



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    self.config_tester.run_common_tests()



   @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''							)
   def   lowerCAmelCase						(	self					:  Tuple							):



    """simple docstring"""



    pass



   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase:     int							= self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     _UpperCamelCase:     Any							= model_class(_lowercase							)
     _UpperCamelCase:     Union[str, Any]							= model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(_lowercase		,  nn.Linear							)							)



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    _UpperCamelCase	,							_UpperCamelCase:     Dict							= self.model_tester.prepare_config_and_inputs_for_common()

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

     _UpperCamelCase:     Dict							= ['''pixel_values''']
     self.assertListEqual(arg_names[:1]		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowercase							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_lowercase							)



   @slow
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _UpperCamelCase:     str							= SwiftFormerModel.from_pretrained(_lowercase							)
     self.assertIsNotNone(_lowercase							)



   @unittest.skip(reason='''SwiftFormer does not output attentions'''							)
   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    pass



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    def check_hidden_states_output(_lowercase					:  int		,  _lowercase					:  Union[str, Any]		,  _lowercase					:  Optional[Any]							):
     _UpperCamelCase:     int							= model_class(_lowercase							)
     model.to(_lowercase							)
     model.eval()

     with torch.no_grad():
      _UpperCamelCase:     Optional[int]							= model(**self._prepare_for_class(_lowercase		,  _lowercase							)							)

     _UpperCamelCase:     Union[str, Any]							= outputs.hidden_states

     _UpperCamelCase:     Union[str, Any]							= 8
     self.assertEqual(len(_lowercase							)		,  _lowercase							)  # 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(_lowercase							)							):
      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),
              ]							)		,  )

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

    for model_class in self.all_model_classes:
     _UpperCamelCase:     List[str]							= True
     check_hidden_states_output(_lowercase		,  _lowercase		,  _lowercase							)

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

     check_hidden_states_output(_lowercase		,  _lowercase		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    def _config_zero_init(_lowercase					:  int							):
     _UpperCamelCase:     Dict							= copy.deepcopy(_lowercase							)
     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(_lowercase		,  _lowercase		,  1E-10							)
      if isinstance(getattr(_lowercase		,  _lowercase		,  _lowercase							)		,  _lowercase							):
       _UpperCamelCase:     int							= _config_zero_init(getattr(_lowercase		,  _lowercase							)							)
       setattr(_lowercase		,  _lowercase		,  _lowercase							)
     return configs_no_init

    _UpperCamelCase	,							_UpperCamelCase:     Tuple							= self.model_tester.prepare_config_and_inputs_for_common()

    _UpperCamelCase:     Optional[Any]							= _config_zero_init(_lowercase							)
    for model_class in self.all_model_classes:
     _UpperCamelCase:     int							= model_class(config=_lowercase							)
     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   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    pass




def       lowerCAmelCase_     (		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 return image





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





   """simple docstring"""




   @cached_property
   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''							) if is_vision_available() else None



   @slow
   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''							).to(_lowercase							)

    _UpperCamelCase:     Any							= self.default_image_processor
    _UpperCamelCase:     List[Any]							= prepare_img()
    _UpperCamelCase:     Tuple							= image_processor(images=_lowercase		,  return_tensors='''pt'''							).to(_lowercase							)

    # forward pass
    with torch.no_grad():
     _UpperCamelCase:     str							= model(**_lowercase							)

    # verify the logits
    _UpperCamelCase:     str							= torch.Size((1, 1_000)							)
    self.assertEqual(outputs.logits.shape		,  _lowercase							)

    _UpperCamelCase:     str							= torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]							).to(_lowercase							)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]		,  _lowercase		,  atol=1E-4							)							)
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     Optional[int]							= []

 # 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:
   _UpperCamelCase:     int							= key.split('''.'''		)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     str							= pipeline.text_encoder
  else:
   _UpperCamelCase:     Optional[Any]							= key.split('''.'''		)[0].split(LORA_PREFIX_UNET + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     Any							= pipeline.unet

  # find the target layer
  _UpperCamelCase:     Union[str, Any]							= layer_infos.pop(0		)
  while len(lowercase		) > -1:
   try:
    _UpperCamelCase:     Union[str, Any]							= curr_layer.__getattr__(lowercase		)
    if len(lowercase		) > 0:
     _UpperCamelCase:     Tuple							= layer_infos.pop(0		)
    elif len(lowercase		) == 0:
     break
   except Exception:
    if len(lowercase		) > 0:
     temp_name += "_" + layer_infos.pop(0		)
    else:
     _UpperCamelCase:     List[Any]							= layer_infos.pop(0		)

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

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

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

 return pipeline


if __name__ == "__main__":
  UpperCAmelCase_			    =							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.)''')

  UpperCAmelCase_			    =							parser.parse_args()

  UpperCAmelCase_			    =							args.base_model_path
  UpperCAmelCase_			    =							args.checkpoint_path
  UpperCAmelCase_			    =							args.dump_path
  UpperCAmelCase_			    =							args.lora_prefix_unet
  UpperCAmelCase_			    =							args.lora_prefix_text_encoder
  UpperCAmelCase_			    =							args.alpha

  UpperCAmelCase_			    =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)