
"""simple docstring"""



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def    _UpperCAmelCase (					__lowerCamelCase  :			Union[str, Any]					,   __lowerCamelCase  :			Any					,   __lowerCamelCase  :			Tuple					,   __lowerCamelCase  :			Union[str, Any]					,   __lowerCamelCase  :			List[Any]						)	->   Dict:
  _snake_case   	=			StableDiffusionPipeline.from_pretrained(__A					,   torch_dtype=torch.floataa						)

  # load LoRA weight from .safetensors
  _snake_case   	=			load_file(__A						)

  _snake_case   	=			[]

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

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

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

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

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

  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=0.75, 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.)')

 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)








"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_						:    Optional[Any]				=      {
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Tuple				=      ['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    int				=      [
							    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SqueezeBertForMaskedLM',
							    'SqueezeBertForMultipleChoice',
							    'SqueezeBertForQuestionAnswering',
							    'SqueezeBertForSequenceClassification',
							    'SqueezeBertForTokenClassification',
							    'SqueezeBertModel',
							    'SqueezeBertModule',
							    'SqueezeBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

							A_						:    Optional[Any]				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





