


'''simple docstring'''




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
 import torch

if is_vision_available():
 import PIL


UpperCAmelCase_      :		Optional[int]          =		logging.get_logger(__name__)


def    UpperCAmelCase_  (       A		,	A		,	A		,	A      ):
   '''simple docstring'''





   def constraint_to_multiple_of(A		,	A		,	A=0		,	A=None      ):
      _a					:  Dict						=  round(val / multiple      ) * multiple

      if max_val is not None and x > max_val:
         _a					:  Any						=  math.floor(val / multiple      ) * multiple

      if x < min_val:
         _a					:  Union[str, Any]						=  math.ceil(val / multiple      ) * multiple

      return x

   _a					:  Tuple						=  (output_size, output_size) if isinstance(A		,	A      ) else output_size

   _a		,						_a					:  str						=  get_image_size(A      )
   _a		,						_a					:  Optional[Any]						=  output_size

   # determine new height and width
   _a					:  Optional[int]						=  output_height / input_height
   _a					:  Optional[Any]						=  output_width / input_width

   if keep_aspect_ratio:
      # scale as little as possible
      if abs(1 - scale_width      ) < abs(1 - scale_height      ):
         # fit width
         _a					:  int						=  scale_width
      else:
         # fit height
         _a					:  str						=  scale_height

   _a					:  int						=  constraint_to_multiple_of(scale_height * input_height		,	multiple=A      )
   _a					:  str						=  constraint_to_multiple_of(scale_width * input_width		,	multiple=A      )

   return (new_height, new_width)







class 					a			(				UpperCAmelCase__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[Any]							  =		["""pixel_values"""]




 def __init__(    self		,      lowerCamelCase_ = True		,      lowerCamelCase_ = None		,      lowerCamelCase_ = PILImageResampling.BILINEAR		,      lowerCamelCase_ = False		,      lowerCamelCase_ = 1		,      lowerCamelCase_ = True		,      lowerCamelCase_ = 1 / 2_5_5		,      lowerCamelCase_ = True		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    None:
    super().__init__(**_UpperCAmelCase      )
    _a					:  Optional[int]						=  size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
    _a					:  Dict						=  get_size_dict(_UpperCAmelCase      )
    _a					:  Optional[Any]						=  do_resize
    _a					:  Optional[int]						=  size
    _a					:  List[Any]						=  keep_aspect_ratio
    _a					:  Union[str, Any]						=  ensure_multiple_of
    _a					:  Any						=  resample
    _a					:  Optional[int]						=  do_rescale
    _a					:  Any						=  rescale_factor
    _a					:  Optional[int]						=  do_normalize
    _a					:  List[str]						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _a					:  Union[str, Any]						=  image_std if image_std is not None else IMAGENET_STANDARD_STD




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = False		,      lowerCamelCase_ = 1		,      lowerCamelCase_ = PILImageResampling.BICUBIC		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    np.ndarray:
    _a					:  Union[str, Any]						=  get_size_dict(_UpperCAmelCase      )
    if "height" not in size or "width" not in size:
       raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''      )
    _a					:  Dict						=  get_resize_output_image_size(
        _UpperCAmelCase		,      output_size=(size['height'], size['width'])		,      keep_aspect_ratio=_UpperCAmelCase		,      multiple=_UpperCAmelCase		,      )
    return resize(_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    int:
    return rescale(_UpperCAmelCase		,      scale=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      **lowerCamelCase_		,      )      ->    np.ndarray:
    return normalize(_UpperCAmelCase		,      mean=_UpperCAmelCase		,      std=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = None		,      lowerCamelCase_ = ChannelDimension.FIRST		,      **lowerCamelCase_		,      )      ->    PIL.Image.Image:
    _a					:  Dict						=  do_resize if do_resize is not None else self.do_resize
    _a					:  str						=  size if size is not None else self.size
    _a					:  List[str]						=  get_size_dict(_UpperCAmelCase      )
    _a					:  Tuple						=  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
    _a					:  List[str]						=  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
    _a					:  int						=  resample if resample is not None else self.resample
    _a					:  Union[str, Any]						=  do_rescale if do_rescale is not None else self.do_rescale
    _a					:  int						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    _a					:  str						=  do_normalize if do_normalize is not None else self.do_normalize
    _a					:  Dict						=  image_mean if image_mean is not None else self.image_mean
    _a					:  Optional[int]						=  image_std if image_std is not None else self.image_std

    _a					:  Dict						=  make_list_of_images(_UpperCAmelCase      )

    if not valid_images(_UpperCAmelCase      ):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.'      )

    if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'      )

    if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

    # All transformations expect numpy arrays.
    _a					:  str						=  [to_numpy_array(_UpperCAmelCase      ) for image in images]

    if do_resize:
       _a					:  List[Any]						=  [self.resize(image=_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase      ) for image in images]

    if do_rescale:
       _a					:  List[Any]						=  [self.rescale(image=_UpperCAmelCase		,      scale=_UpperCAmelCase      ) for image in images]

    if do_normalize:
       _a					:  Union[str, Any]						=  [self.normalize(image=_UpperCAmelCase		,      mean=_UpperCAmelCase		,      std=_UpperCAmelCase      ) for image in images]

    _a					:  Any						=  [to_channel_dimension_format(_UpperCAmelCase		,      _UpperCAmelCase      ) for image in images]

    _a					:  str						=  {'pixel_values': images}
    return BatchFeature(data=_UpperCAmelCase		,      tensor_type=_UpperCAmelCase      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    Dict:
    _a					:  int						=  outputs.logits

    # Resize logits and compute semantic segmentation maps
    if target_sizes is not None:
       if len(_UpperCAmelCase      ) != len(_UpperCAmelCase      ):
          raise ValueError(
              'Make sure that you pass in as many target sizes as the batch dimension of the logits'      )

       if is_torch_tensor(_UpperCAmelCase      ):
          _a					:  Any						=  target_sizes.numpy()

       _a					:  Optional[int]						=  []

       for idx in range(len(_UpperCAmelCase      )      ):
          _a					:  Dict						=  torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0      )		,      size=target_sizes[idx]		,      mode='bilinear'		,      align_corners=_UpperCAmelCase      )
          _a					:  Optional[int]						=  resized_logits[0].argmax(dim=0      )
          semantic_segmentation.append(_UpperCAmelCase      )
    else:
       _a					:  str						=  logits.argmax(dim=1      )
       _a					:  str						=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

    return semantic_segmentation






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)

