






"""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


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
    # See all ViT models at https://huggingface.co/models?filter=vit
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''vit'''





	def __init__( self   :					int      ,      lowerCAmelCase_   :					Any=768      ,      lowerCAmelCase_   :					str=12      ,      lowerCAmelCase_   :					Optional[int]=12      ,      lowerCAmelCase_   :					List[str]=3_072      ,      lowerCAmelCase_   :					Optional[int]="gelu"      ,      lowerCAmelCase_   :					List[str]=0.0      ,      lowerCAmelCase_   :					Dict=0.0      ,      lowerCAmelCase_   :					Optional[int]=0.0_2      ,      lowerCAmelCase_   :					Optional[Any]=1e-12      ,      lowerCAmelCase_   :					Any=224      ,      lowerCAmelCase_   :					str=16      ,      lowerCAmelCase_   :					Dict=3      ,      lowerCAmelCase_   :					str=True      ,      lowerCAmelCase_   :					List[str]=16      ,      **lowerCAmelCase_   :					Union[str, Any]      ,      )		->     Dict:
				super().__init__(**lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   hidden_size
				UpperCAmelCase_	:					str     	=   num_hidden_layers
				UpperCAmelCase_	:					Optional[Any]     	=   num_attention_heads
				UpperCAmelCase_	:					str     	=   intermediate_size
				UpperCAmelCase_	:					Optional[int]     	=   hidden_act
				UpperCAmelCase_	:					str     	=   hidden_dropout_prob
				UpperCAmelCase_	:					str     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Dict     	=   initializer_range
				UpperCAmelCase_	:					Tuple     	=   layer_norm_eps
				UpperCAmelCase_	:					Tuple     	=   image_size
				UpperCAmelCase_	:					Union[str, Any]     	=   patch_size
				UpperCAmelCase_	:					Tuple     	=   num_channels
				UpperCAmelCase_	:					Optional[int]     	=   qkv_bias
				UpperCAmelCase_	:					Tuple     	=   encoder_stride







class      UpperCamelCase_			(__A	):
	__magic_name__       =							version.parse('''1.11'''	)





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





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     float:
				return 1e-4







'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

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

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

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      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=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				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)
		__UpperCAmelCase       							=				parser.parse_args()

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