


'''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 				lowerCamelCase__				(	A       :      Dict				,       A       :      str					):




      '''simple docstring'''






      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(A					)
      }

      # 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(A					)
      }

      for i in range(A					):
            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(A					)
            UpperCAmelCase					    =     {'''old''': f"""down.{i}.block""", '''new''': f"""down_blocks.{i}.resnets"""}
            assign_to_checkpoint(A				,       A				,       A				,       additional_replacements=[meta_path]				,       config=A					)

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

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

      for i in range(A					):
            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(A					)
            UpperCAmelCase					    =     {'''old''': f"""up.{block_id}.block""", '''new''': f"""up_blocks.{i}.resnets"""}
            assign_to_checkpoint(A				,       A				,       A				,       additional_replacements=[meta_path]				,       config=A					)

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

      UpperCAmelCase					    =     [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
      UpperCAmelCase					    =     renew_vae_attention_paths(A					)
      UpperCAmelCase					    =     {'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
      assign_to_checkpoint(A				,       A				,       A				,       additional_replacements=[meta_path]				,       config=A					)
      conv_attn_to_linear(A					)
      return new_checkpoint


def 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       ):




      '''simple docstring'''






      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(A					)
      UpperCAmelCase					    =     5_12
      UpperCAmelCase					    =     '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if checkpoint_path.endswith('''safetensors'''					):
            from safetensors import safe_open

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

      # Convert the VAE model.
      UpperCAmelCase					    =     create_vae_diffusers_config(A				,       image_size=A					)
      UpperCAmelCase					    =     custom_convert_ldm_vae_checkpoint(A				,       A					)

      UpperCAmelCase					    =     AutoencoderKL(**A					)
      vae.load_state_dict(A					)
      vae.save_pretrained(A					)


if __name__ == "__main__":
 _lowercase      :      Tuple            =					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.""")

 _lowercase      :      Dict            =					parser.parse_args()

 vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









'''simple docstring'''



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="Translation"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def __call__(		self      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)





				def 					a__(		self      :			int				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Value

										return {k: Value('''string'''				) for k in sorted(self.languages				)}







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				Optional[List]  		=	None
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="TranslationVariableLanguages"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     sorted(set(self.languages				)				) if self.languages else None
										UpperCAmelCase					    =     len(self.languages				) if self.languages else None




				def __call__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return pa.struct({'''language''': pa.list_(pa.string()				), '''translation''': pa.list_(pa.string()				)}				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     set(self.languages				)
										if self.languages and set(lowerCAmelCase				) - lang_set:
																raise ValueError(
																    F"""Some languages in example ({", ".join(sorted(set(lowerCAmelCase				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCAmelCase				)})."""				)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										UpperCAmelCase					    =     []
										for lang, text in translation_dict.items():
																if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						translation_tuples.append((lang, text)				)
																else:
																						translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
										UpperCAmelCase    ,    UpperCAmelCase					    =     zip(*sorted(lowerCAmelCase				)				)

										return {"language": languages, "translation": translations}





				def 					a__(		self      :			Any				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Sequence, Value

										return {
										    "language": Sequence(Value('''string'''				)				),
										    "translation": Sequence(Value('''string'''				)				),
										}





