






"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a       :	Any             = logging.get_logger(__name__)

__a       :	List[str]             = {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





       """simple docstring"""





       _SCREAMING_SNAKE_CASE    ='cvt'




       def __init__(   self:     str				,		__A:     Optional[Any]=3				,		__A:     Optional[Any]=[7, 3, 3]				,		__A:     Dict=[4, 2, 2]				,		__A:     Optional[int]=[2, 1, 1]				,		__A:     Dict=[64, 192, 384]				,		__A:     Any=[1, 3, 6]				,		__A:     str=[1, 2, 10]				,		__A:     Tuple=[4.0, 4.0, 4.0]				,		__A:     int=[0.0, 0.0, 0.0]				,		__A:     Optional[int]=[0.0, 0.0, 0.0]				,		__A:     Optional[int]=[0.0, 0.0, 0.1]				,		__A:     Union[str, Any]=[True, True, True]				,		__A:     Any=[False, False, True]				,		__A:     Union[str, Any]=["dw_bn", "dw_bn", "dw_bn"]				,		__A:     List[Any]=[3, 3, 3]				,		__A:     Optional[int]=[1, 1, 1]				,		__A:     Optional[int]=[2, 2, 2]				,		__A:     Optional[int]=[1, 1, 1]				,		__A:     List[str]=[1, 1, 1]				,		__A:     int=0.0_2				,		__A:     Optional[int]=1e-12				,		**__A:     Union[str, Any]				,		):

            '''simple docstring'''

            super().__init__(**__A    )
            a__          =  num_channels
            a__          =  patch_sizes
            a__          =  patch_stride
            a__          =  patch_padding
            a__          =  embed_dim
            a__          =  num_heads
            a__          =  depth
            a__          =  mlp_ratio
            a__          =  attention_drop_rate
            a__          =  drop_rate
            a__          =  drop_path_rate
            a__          =  qkv_bias
            a__          =  cls_token
            a__          =  qkv_projection_method
            a__          =  kernel_qkv
            a__          =  padding_kv
            a__          =  stride_kv
            a__          =  padding_q
            a__          =  stride_q
            a__          =  initializer_range
            a__          =  layer_norm_eps










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')['''model''']
					a__          =  list(state_dict.keys())

					# extract state_dict for VQVAE
					a__          =  {}
					a__          =  '''first_stage_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

    # extract state_dict for UNetLDM
					a__          =  {}
					a__          =  '''model.diffusion_model.'''
					for key in keys:
										if key.startswith(lowerCamelCase_):
															a__          =  state_dict[key]

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					a__          =  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=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = 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)
	__a       :	Optional[int]             = parser.parse_args()

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


