



"""simple docstring"""





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


lowerCamelCase       	=						logging.get_logger(__name__)

lowerCamelCase       	=						{
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					'''gpt_neox'''



       def __init__(							self      :  Dict		,    _UpperCAmelCase      :  Optional[Any]=50432		,    _UpperCAmelCase      :  List[Any]=6144		,    _UpperCAmelCase      :  Dict=44		,    _UpperCAmelCase      :  Optional[Any]=64		,    _UpperCAmelCase      :  Any=24576		,    _UpperCAmelCase      :  Dict="gelu"		,    _UpperCAmelCase      :  Optional[int]=0.25		,    _UpperCAmelCase      :  Tuple=10000		,    _UpperCAmelCase      :  Union[str, Any]=0.0		,    _UpperCAmelCase      :  List[str]=0.0		,    _UpperCAmelCase      :  List[Any]=0.1		,    _UpperCAmelCase      :  Dict=2048		,    _UpperCAmelCase      :  Optional[Any]=0.02		,    _UpperCAmelCase      :  Dict=1e-5		,    _UpperCAmelCase      :  Union[str, Any]=True		,    _UpperCAmelCase      :  Dict=0		,    _UpperCAmelCase      :  str=2		,    _UpperCAmelCase      :  Optional[int]=False		,    _UpperCAmelCase      :  Optional[Any]=True		,    _UpperCAmelCase      :  Dict=None		,    **_UpperCAmelCase      :  str		,    )			->						Union[str, Any]:


             '''simple docstring'''




             super().__init__(bos_token_id=_UpperCAmelCase		,    eos_token_id=_UpperCAmelCase		,    **_UpperCAmelCase   )
             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							rotary_pct
             UpperCAmelCase_      			=							rotary_emb_base
             UpperCAmelCase_      			=							attention_dropout
             UpperCAmelCase_      			=							hidden_dropout
             UpperCAmelCase_      			=							classifier_dropout
             UpperCAmelCase_      			=							initializer_range
             UpperCAmelCase_      			=							layer_norm_eps
             UpperCAmelCase_      			=							use_cache
             UpperCAmelCase_      			=							tie_word_embeddings
             UpperCAmelCase_      			=							use_parallel_residual
             UpperCAmelCase_      			=							rope_scaling
             self._rope_scaling_validation()

             if self.hidden_size % self.num_attention_heads != 0:
                   raise ValueError(
                       "The hidden size is not divisble by the number of attention heads! Make sure to update them!"   )







       def 					lowercase__    (							self      :  Optional[int]   )			->						List[str]:


             '''simple docstring'''




             if self.rope_scaling is None:
                   return

             if not isinstance(self.rope_scaling		,    _UpperCAmelCase   ) or len(self.rope_scaling   ) != 2:
                   raise ValueError(
                       "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                       F"""got {self.rope_scaling}"""   )
             UpperCAmelCase_      			=							self.rope_scaling.get("type"		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							self.rope_scaling.get("factor"		,    _UpperCAmelCase   )
             if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                   raise ValueError(
                       F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""   )
             if rope_scaling_factor is None or not isinstance(_UpperCAmelCase		,    _UpperCAmelCase   ) or rope_scaling_factor <= 1.0:
                   raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""   )









"""simple docstring"""





import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      UpperCAmelCase_      			=							checkpoint

      UpperCAmelCase_      			=							{}

      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_in.weight"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_in.bias"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.conv_out.bias"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.norm_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["encoder.norm_out.bias"]

      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_in.weight"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_in.bias"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.conv_out.bias"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.norm_out.weight"]
      UpperCAmelCase_      			=							vae_state_dict["decoder.norm_out.bias"]

      UpperCAmelCase_      			=							vae_state_dict["quant_conv.weight"]
      UpperCAmelCase_      			=							vae_state_dict["quant_conv.bias"]
      UpperCAmelCase_      			=							vae_state_dict["post_quant_conv.weight"]
      UpperCAmelCase_      			=							vae_state_dict["post_quant_conv.bias"]

      # Retrieves the keys for the encoder down blocks only
      UpperCAmelCase_      			=							len({".".join(layer.split("."				)[:3]				) for layer in vae_state_dict if "encoder.down" in layer}				)
      UpperCAmelCase_      			=							{
          layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(lowerCAmelCase__				)
      }

      # Retrieves the keys for the decoder up blocks only
      UpperCAmelCase_      			=							len({".".join(layer.split("."				)[:3]				) for layer in vae_state_dict if "decoder.up" in layer}				)
      UpperCAmelCase_      			=							{
          layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(lowerCAmelCase__				)
      }

      for i in range(lowerCAmelCase__				):
            UpperCAmelCase_      			=							[key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

            if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
                  UpperCAmelCase_      			=							vae_state_dict.pop(
                      f"""encoder.down.{i}.downsample.conv.weight"""				)
                  UpperCAmelCase_      			=							vae_state_dict.pop(
                      f"""encoder.down.{i}.downsample.conv.bias"""				)

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "encoder.mid.block" in key]
      UpperCAmelCase_      			=							2
      for i in range(1     ,      num_mid_res_blocks + 1				):
            UpperCAmelCase_      			=							[key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "encoder.mid.attn" in key]
      UpperCAmelCase_      			=							renew_vae_attention_paths(lowerCAmelCase__				)
      UpperCAmelCase_      			=							{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
      assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)
      conv_attn_to_linear(lowerCAmelCase__				)

      for i in range(lowerCAmelCase__				):
            UpperCAmelCase_      			=							num_up_blocks - 1 - i
            UpperCAmelCase_      			=							[
                key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
            ]

            if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
                  UpperCAmelCase_      			=							vae_state_dict[
                      f"""decoder.up.{block_id}.upsample.conv.weight"""
                  ]
                  UpperCAmelCase_      			=							vae_state_dict[
                      f"""decoder.up.{block_id}.upsample.conv.bias"""
                  ]

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "decoder.mid.block" in key]
      UpperCAmelCase_      			=							2
      for i in range(1     ,      num_mid_res_blocks + 1				):
            UpperCAmelCase_      			=							[key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

            UpperCAmelCase_      			=							renew_vae_resnet_paths(lowerCAmelCase__				)
            UpperCAmelCase_      			=							{"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
            assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)

      UpperCAmelCase_      			=							[key for key in vae_state_dict if "decoder.mid.attn" in key]
      UpperCAmelCase_      			=							renew_vae_attention_paths(lowerCAmelCase__				)
      UpperCAmelCase_      			=							{"old": "mid.attn_1", "new": "mid_block.attentions.0"}
      assign_to_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__     ,      additional_replacements=[meta_path]     ,      config=lowerCAmelCase__				)
      conv_attn_to_linear(lowerCAmelCase__				)
      return new_checkpoint
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      ):
      # Only support V1
      UpperCAmelCase_      			=							requests.get(
          " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"				)
      UpperCAmelCase_      			=							io.BytesIO(r.content				)

      UpperCAmelCase_      			=							OmegaConf.load(lowerCAmelCase__				)
      UpperCAmelCase_      			=							512
      UpperCAmelCase_      			=							"cuda" if torch.cuda.is_available() else "cpu"
      if checkpoint_path.endswith("safetensors"				):
            from safetensors import safe_open

            UpperCAmelCase_      			=							{}
            with safe_open(lowerCAmelCase__     ,      framework="pt"     ,      device="cpu"				) as f:
                  for key in f.keys():
                        UpperCAmelCase_      			=							f.get_tensor(lowerCAmelCase__				)
      else:
            UpperCAmelCase_      			=							torch.load(lowerCAmelCase__     ,      map_location=lowerCAmelCase__				)["state_dict"]

      # Convert the VAE model.
      UpperCAmelCase_      			=							create_vae_diffusers_config(lowerCAmelCase__     ,      image_size=lowerCAmelCase__				)
      UpperCAmelCase_      			=							custom_convert_ldm_vae_checkpoint(lowerCAmelCase__     ,      lowerCAmelCase__				)

      UpperCAmelCase_      			=							AutoencoderKL(**lowerCAmelCase__				)
      vae.load_state_dict(lowerCAmelCase__				)
      vae.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
  lowerCamelCase       	=						argparse.ArgumentParser()

  parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")

  lowerCamelCase       	=						parser.parse_args()

  vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




