
"""simple docstring"""

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


UpperCamelCase     :    Optional[Any]       =			logging.get_logger(__name__)

UpperCamelCase     :    int       =			{
    "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
    "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"falcon"
     lowercase						       =						["past_key_values"]





     def __init__(     self     ,					__UpperCAmelCase=6_5024     ,					__UpperCAmelCase=4544     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=71     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=11     ,					__UpperCAmelCase=11     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	vocab_size
      # Backward compatibility with n_embed kwarg
      __UpperCamelCase      =	kwargs.pop('n_embed'     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	hidden_size if n_embed is None else n_embed
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	layer_norm_epsilon
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	use_cache
      __UpperCamelCase      =	hidden_dropout
      __UpperCamelCase      =	attention_dropout

      __UpperCamelCase      =	bos_token_id
      __UpperCamelCase      =	eos_token_id
      __UpperCamelCase      =	num_attention_heads if num_kv_heads is None else num_kv_heads
      __UpperCamelCase      =	alibi
      __UpperCamelCase      =	new_decoder_architecture
      __UpperCamelCase      =	multi_query  # Ignored when new_decoder_architecture is True
      __UpperCamelCase      =	parallel_attn
      __UpperCamelCase      =	bias

      super().__init__(bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase				)





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self.hidden_size // self.num_attention_heads







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return not self.alibi





"""simple docstring"""

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 							A						(    snake_case				:Dict    , snake_case				:Optional[Any]    , snake_case				:List[Any]		)				->     Optional[Any]:
 __UpperCamelCase      =	OmegaConf.load(snake_case		)
 __UpperCamelCase      =	torch.load(snake_case    , map_location='cpu'		)['model']
 __UpperCamelCase      =	list(state_dict.keys()		)

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

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

 __UpperCamelCase      =	config.model.params.first_stage_config.params
 __UpperCamelCase      =	config.model.params.unet_config.params

 __UpperCamelCase      =	VQModel(**snake_case		).eval()
 vqvae.load_state_dict(snake_case		)

 __UpperCamelCase      =	UNetLDMModel(**snake_case		).eval()
 unet.load_state_dict(snake_case		)

 __UpperCamelCase      =	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    , )

 __UpperCamelCase      =	LDMPipeline(snake_case    , snake_case    , snake_case		)
 pipeline.save_pretrained(snake_case		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			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     :    Optional[int]       =			parser.parse_args()

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



