
"""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


_A			:       List[Any]   		=       logging.get_logger(__name__)

_A			:       int   		=       {
    """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 		a__						(      a_,			a_						):
   __lowerCAmelCase            =				"""swin"""

   __lowerCAmelCase            =				{
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }
   def __init__(			self	,    _a=224	,    _a=4	,    _a=3	,    _a=96	,    _a=[2, 2, 6, 2]	,    _a=[3, 6, 12, 24]	,    _a=7	,    _a=4.0	,    _a=True	,    _a=0.0	,    _a=0.0	,    _a=0.1	,    _a="gelu"	,    _a=False	,    _a=0.0_2	,    _a=1E-5	,    _a=32	,    _a=None	,    _a=None	,    **_a	,    ):
          super().__init__(**_a				)

          lowercase    :				Dict       		=							image_size
          lowercase    :				Dict       		=							patch_size
          lowercase    :				Dict       		=							num_channels
          lowercase    :				Union[str, Any]       		=							embed_dim
          lowercase    :				Any       		=							depths
          lowercase    :				Tuple       		=							len(_a				)
          lowercase    :				Any       		=							num_heads
          lowercase    :				Optional[Any]       		=							window_size
          lowercase    :				Optional[int]       		=							mlp_ratio
          lowercase    :				List[Any]       		=							qkv_bias
          lowercase    :				List[str]       		=							hidden_dropout_prob
          lowercase    :				Tuple       		=							attention_probs_dropout_prob
          lowercase    :				Optional[Any]       		=							drop_path_rate
          lowercase    :				Tuple       		=							hidden_act
          lowercase    :				Tuple       		=							use_absolute_embeddings
          lowercase    :				Dict       		=							layer_norm_eps
          lowercase    :				Union[str, Any]       		=							initializer_range
          lowercase    :				Tuple       		=							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
          lowercase    :				Optional[Any]       		=							int(embed_dim * 2 ** (len(_a				) - 1)				)
          lowercase    :				Dict       		=							["stem"] + [f"""stage{idx}""" for idx in range(1	,    len(_a				) + 1				)]
          lowercase		,    lowercase    :				Any       		=							get_aligned_output_features_output_indices(
              out_features=_a	,    out_indices=_a	,    stage_names=self.stage_names				)


class 		a__						(      a_						):
   __lowerCAmelCase            =				version.parse("""1.11"""						)
   @property
   def 			__magic_name__			(			self				):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]				)

   @property
   def 			__magic_name__			(			self				):
          return 1E-4




"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

       # extract state_dict for VQVAE
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				Union[str, Any]       		=							"first_stage_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				List[Any]       		=							state_dict[key]

    # extract state_dict for UNetLDM
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				List[Any]       		=							"model.diffusion_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				Optional[Any]       		=							state_dict[key]

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							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=__snake_case     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[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)
   _A			:       Dict   		=       parser.parse_args()

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


