



"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase    :Dict     =					logging.get_logger(__name__)

_lowerCAmelCase    :Tuple     =					{
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}
class 							_UpperCAmelCase							( a		,a      ):





     '''simple docstring'''



     a__									='''swin'''

     a__									={
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }





     def __init__(					self       ,	A=2_2_4       ,	A=4       ,	A=3       ,	A=9_6       ,	A=[2, 2, 6, 2]       ,	A=[3, 6, 1_2, 2_4]       ,	A=7       ,	A=4.0       ,	A=True       ,	A=0.0       ,	A=0.0       ,	A=0.1       ,	A="gelu"       ,	A=False       ,	A=0.02       ,	A=1E-5       ,	A=3_2       ,	A=None       ,	A=None       ,	**A       ,	)							->					List[Any]:
         super().__init__(**A		)

         _UpperCAmelCase							:  List[Any]											=    image_size
         _UpperCAmelCase							:  Tuple											=    patch_size
         _UpperCAmelCase							:  str											=    num_channels
         _UpperCAmelCase							:  Optional[Any]											=    embed_dim
         _UpperCAmelCase							:  str											=    depths
         _UpperCAmelCase							:  Dict											=    len(A		)
         _UpperCAmelCase							:  Dict											=    num_heads
         _UpperCAmelCase							:  Tuple											=    window_size
         _UpperCAmelCase							:  Optional[int]											=    mlp_ratio
         _UpperCAmelCase							:  Any											=    qkv_bias
         _UpperCAmelCase							:  Optional[int]											=    hidden_dropout_prob
         _UpperCAmelCase							:  Optional[int]											=    attention_probs_dropout_prob
         _UpperCAmelCase							:  Dict											=    drop_path_rate
         _UpperCAmelCase							:  int											=    hidden_act
         _UpperCAmelCase							:  Union[str, Any]											=    use_absolute_embeddings
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_eps
         _UpperCAmelCase							:  Optional[int]											=    initializer_range
         _UpperCAmelCase							:  List[str]											=    encoder_stride
         # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         _UpperCAmelCase							:  Union[str, Any]											=    int(embed_dim * 2 ** (len(A		) - 1)		)
         _UpperCAmelCase							:  Dict											=    ['''stem'''] + [f'stage{idx}' for idx in range(1       ,	len(A		) + 1		)]
         _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    get_aligned_output_features_output_indices(
             out_features=A       ,	out_indices=A       ,	stage_names=self.stage_names		)



class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=version.parse('''1.11'''      )





     @property
     def  __lowerCAmelCase	(					self		)							->					Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]		)





     @property
     def  __lowerCAmelCase	(					self		)							->					float:
         return 1E-4







"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


