






'''simple docstring'''







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


_A	:			Tuple         =logging.get_logger(__name__)

_A	:			Optional[int]         ={
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}

class _lowercase	(				_lowercase		):
							a				      =       """gpt_bigcode"""
							a				      =       ["""past_key_values"""]
							a				      =       {
							    """hidden_size""": """n_embd""",
							    """max_position_embeddings""": """n_positions""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}




							def __init__(							self:				Dict			,							UpperCamelCase__:				Optional[Any]=50_257			,							UpperCamelCase__:				Union[str, Any]=1_024			,							UpperCamelCase__:				List[str]=768			,							UpperCamelCase__:				Tuple=12			,							UpperCamelCase__:				Any=12			,							UpperCamelCase__:				Dict=None			,							UpperCamelCase__:				Optional[int]="gelu_pytorch_tanh"			,							UpperCamelCase__:				Any=0.1			,							UpperCamelCase__:				Optional[Any]=0.1			,							UpperCamelCase__:				Union[str, Any]=0.1			,							UpperCamelCase__:				List[Any]=1e-5			,							UpperCamelCase__:				str=0.02			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Dict=50_256			,							UpperCamelCase__:				Tuple=50_256			,							UpperCamelCase__:				Optional[int]=True			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				int=True			,							**UpperCamelCase__:				Tuple			,							):
														lowerCamelCase__					:	str         =       vocab_size
														lowerCamelCase__					:	str         =       n_positions
														lowerCamelCase__					:	Union[str, Any]         =       n_embd
														lowerCamelCase__					:	Optional[Any]         =       n_layer
														lowerCamelCase__					:	int         =       n_head
														lowerCamelCase__					:	List[str]         =       n_inner
														lowerCamelCase__					:	Optional[Any]         =       activation_function
														lowerCamelCase__					:	int         =       resid_pdrop
														lowerCamelCase__					:	List[str]         =       embd_pdrop
														lowerCamelCase__					:	Union[str, Any]         =       attn_pdrop
														lowerCamelCase__					:	Optional[int]         =       layer_norm_epsilon
														lowerCamelCase__					:	Any         =       initializer_range
														lowerCamelCase__					:	List[Any]         =       scale_attn_weights
														lowerCamelCase__					:	Any         =       use_cache
														lowerCamelCase__					:	Union[str, Any]         =       attention_softmax_in_fpaa
														lowerCamelCase__					:	str         =       scale_attention_softmax_in_fpaa
														lowerCamelCase__					:	List[str]         =       multi_query

														lowerCamelCase__					:	List[str]         =       bos_token_id
														lowerCamelCase__					:	Union[str, Any]         =       eos_token_id

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



"""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)


