






"""simple docstring"""







import math

import flax.linen as nn
import jax.numpy as jnp






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase = 1						,   lowerCAmelCase = 1						,   lowerCAmelCase = 1.0e4						,   lowerCAmelCase = False						,   lowerCAmelCase = 1.0						,   ):







       '''simple docstring'''
       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
       UpperCAmelCase						     =      float(embedding_dim // 2		)
       UpperCAmelCase						     =      math.log(max_timescale / min_timescale		) / (num_timescales - freq_shift)
       UpperCAmelCase						     =      min_timescale * jnp.exp(jnp.arange(lowerCAmelCase						,   dtype=jnp.floataa		) * -log_timescale_increment		)
       UpperCAmelCase						     =      jnp.expand_dims(lowerCAmelCase						,   1		) * jnp.expand_dims(lowerCAmelCase						,   0		)

       # scale embeddings
       UpperCAmelCase						     =      scale * emb

       if flip_sin_to_cos:
              UpperCAmelCase						     =      jnp.concatenate([jnp.cos(lowerCAmelCase		), jnp.sin(lowerCAmelCase		)]						,   axis=1		)
       else:
              UpperCAmelCase						     =      jnp.concatenate([jnp.sin(lowerCAmelCase		), jnp.cos(lowerCAmelCase		)]						,   axis=1		)
       UpperCAmelCase						     =      jnp.reshape(lowerCAmelCase						,   [jnp.shape(lowerCAmelCase		)[0], embedding_dim]		)
       return signal





class 	UpperCamelCase_			(							nn.Module	):
  _A							:       int					=						32
  _A							:       jnp.dtype					=						jnp.floataa



  @nn.compact
  def __call__(   self			,		snake_case__					)      ->      Tuple:



         """simple docstring"""





         UpperCAmelCase						     =      nn.Dense(self.time_embed_dim			,		dtype=self.dtype			,		name="""linear_1"""					)(lowerCamelCase_					)
         UpperCAmelCase						     =      nn.silu(lowerCamelCase_					)
         UpperCAmelCase						     =      nn.Dense(self.time_embed_dim			,		dtype=self.dtype			,		name="""linear_2"""					)(lowerCamelCase_					)
         return temb





class 	UpperCamelCase_			(							nn.Module	):
  _A							:       int					=						32
  _A							:       bool					=						False
  _A							:       float					=						1



  @nn.compact
  def __call__(   self			,		snake_case__					)      ->      Optional[Any]:



         """simple docstring"""





         return get_sinusoidal_embeddings(
             lowerCamelCase_			,		embedding_dim=self.dim			,		flip_sin_to_cos=self.flip_sin_to_cos			,		freq_shift=self.freq_shift					)




'''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)