

"""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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    Dict 	=			checkpoint

	__lowerCamelCase    :    Optional[int] 	=			{}

	__lowerCamelCase    :    Any 	=			vae_state_dict['encoder.conv_in.weight']
	__lowerCamelCase    :    Dict 	=			vae_state_dict['encoder.conv_in.bias']
	__lowerCamelCase    :    Union[str, Any] 	=			vae_state_dict['encoder.conv_out.weight']
	__lowerCamelCase    :    int 	=			vae_state_dict['encoder.conv_out.bias']
	__lowerCamelCase    :    str 	=			vae_state_dict['encoder.norm_out.weight']
	__lowerCamelCase    :    List[Any] 	=			vae_state_dict['encoder.norm_out.bias']

	__lowerCamelCase    :    Union[str, Any] 	=			vae_state_dict['decoder.conv_in.weight']
	__lowerCamelCase    :    Union[str, Any] 	=			vae_state_dict['decoder.conv_in.bias']
	__lowerCamelCase    :    Optional[int] 	=			vae_state_dict['decoder.conv_out.weight']
	__lowerCamelCase    :    Tuple 	=			vae_state_dict['decoder.conv_out.bias']
	__lowerCamelCase    :    Union[str, Any] 	=			vae_state_dict['decoder.norm_out.weight']
	__lowerCamelCase    :    List[Any] 	=			vae_state_dict['decoder.norm_out.bias']

	__lowerCamelCase    :    Dict 	=			vae_state_dict['quant_conv.weight']
	__lowerCamelCase    :    str 	=			vae_state_dict['quant_conv.bias']
	__lowerCamelCase    :    List[str] 	=			vae_state_dict['post_quant_conv.weight']
	__lowerCamelCase    :    List[Any] 	=			vae_state_dict['post_quant_conv.bias']

	# Retrieves the keys for the encoder down blocks only
	__lowerCamelCase    :    int 	=			len({'.'.join(layer.split('.'      )[:3]      ) for layer in vae_state_dict if 'encoder.down' in layer}      )
	__lowerCamelCase    :    int 	=			{
	    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
	__lowerCamelCase    :    Any 	=			len({'.'.join(layer.split('.'      )[:3]      ) for layer in vae_state_dict if 'decoder.up' in layer}      )
	__lowerCamelCase    :    List[Any] 	=			{
	    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__      ):
		__lowerCamelCase    :    List[str] 	=			[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:
			__lowerCamelCase    :    Tuple 	=			vae_state_dict.pop(
			    F"encoder.down.{i}.downsample.conv.weight"      )
			__lowerCamelCase    :    Union[str, Any] 	=			vae_state_dict.pop(
			    F"encoder.down.{i}.downsample.conv.bias"      )

		__lowerCamelCase    :    Optional[int] 	=			renew_vae_resnet_paths(lowerCamelCase__      )
		__lowerCamelCase    :    Dict 	=			{'old': F"down.{i}.block", 'new': F"down_blocks.{i}.resnets"}
		assign_to_checkpoint(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			additional_replacements=[meta_path]     ,			config=lowerCamelCase__      )

	__lowerCamelCase    :    List[Any] 	=			[key for key in vae_state_dict if 'encoder.mid.block' in key]
	__lowerCamelCase    :    Optional[int] 	=			2
	for i in range(1     ,			num_mid_res_blocks + 1      ):
		__lowerCamelCase    :    List[str] 	=			[key for key in mid_resnets if F"encoder.mid.block_{i}" in key]

		__lowerCamelCase    :    Dict 	=			renew_vae_resnet_paths(lowerCamelCase__      )
		__lowerCamelCase    :    Dict 	=			{'old': F"mid.block_{i}", 'new': F"mid_block.resnets.{i - 1}"}
		assign_to_checkpoint(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			additional_replacements=[meta_path]     ,			config=lowerCamelCase__      )

	__lowerCamelCase    :    Optional[int] 	=			[key for key in vae_state_dict if 'encoder.mid.attn' in key]
	__lowerCamelCase    :    Dict 	=			renew_vae_attention_paths(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			{'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__      ):
		__lowerCamelCase    :    Tuple 	=			num_up_blocks - 1 - i
		__lowerCamelCase    :    Dict 	=			[
		    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:
			__lowerCamelCase    :    Optional[int] 	=			vae_state_dict[
			    F"decoder.up.{block_id}.upsample.conv.weight"
			]
			__lowerCamelCase    :    Any 	=			vae_state_dict[
			    F"decoder.up.{block_id}.upsample.conv.bias"
			]

		__lowerCamelCase    :    str 	=			renew_vae_resnet_paths(lowerCamelCase__      )
		__lowerCamelCase    :    Optional[int] 	=			{'old': F"up.{block_id}.block", 'new': F"up_blocks.{i}.resnets"}
		assign_to_checkpoint(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			additional_replacements=[meta_path]     ,			config=lowerCamelCase__      )

	__lowerCamelCase    :    Optional[Any] 	=			[key for key in vae_state_dict if 'decoder.mid.block' in key]
	__lowerCamelCase    :    Dict 	=			2
	for i in range(1     ,			num_mid_res_blocks + 1      ):
		__lowerCamelCase    :    Tuple 	=			[key for key in mid_resnets if F"decoder.mid.block_{i}" in key]

		__lowerCamelCase    :    List[str] 	=			renew_vae_resnet_paths(lowerCamelCase__      )
		__lowerCamelCase    :    Dict 	=			{'old': F"mid.block_{i}", 'new': F"mid_block.resnets.{i - 1}"}
		assign_to_checkpoint(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			additional_replacements=[meta_path]     ,			config=lowerCamelCase__      )

	__lowerCamelCase    :    Any 	=			[key for key in vae_state_dict if 'decoder.mid.attn' in key]
	__lowerCamelCase    :    Optional[Any] 	=			renew_vae_attention_paths(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[Any] 	=			{'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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			)  ->					int:
	# Only support V1
	__lowerCamelCase    :    str 	=			requests.get(
	    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'      )
	__lowerCamelCase    :    Optional[Any] 	=			io.BytesIO(r.content      )

	__lowerCamelCase    :    Tuple 	=			OmegaConf.load(lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			5_1_2
	__lowerCamelCase    :    Optional[int] 	=			'cuda' if torch.cuda.is_available() else 'cpu'
	if checkpoint_path.endswith('safetensors'      ):
		from safetensors import safe_open

		__lowerCamelCase    :    Optional[Any] 	=			{}
		with safe_open(lowerCamelCase__     ,			framework='pt'     ,			device='cpu'      ) as f:
			for key in f.keys():
				__lowerCamelCase    :    Union[str, Any] 	=			f.get_tensor(lowerCamelCase__      )
	else:
		__lowerCamelCase    :    int 	=			torch.load(lowerCamelCase__     ,			map_location=lowerCamelCase__      )['state_dict']

	# Convert the VAE model.
	__lowerCamelCase    :    List[str] 	=			create_vae_diffusers_config(lowerCamelCase__     ,			image_size=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			custom_convert_ldm_vae_checkpoint(lowerCamelCase__     ,			lowerCamelCase__      )

	__lowerCamelCase    :    List[str] 	=			AutoencoderKL(**lowerCamelCase__      )
	vae.load_state_dict(lowerCamelCase__      )
	vae.save_pretrained(lowerCamelCase__      )


if __name__ == "__main__":
					a		=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.""")

					a		=parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)







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


a		=logging.get_logger(__name__)

a		={
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  str										=				'''vit_msn'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      str="gelu"       ,SCREAMING_SNAKE_CASE__		:      Any=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      Any=0.02       ,SCREAMING_SNAKE_CASE__		:      int=1E-06       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_2_4       ,SCREAMING_SNAKE_CASE__		:      Dict=1_6       ,SCREAMING_SNAKE_CASE__		:      int=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			hidden_size
								__lowerCamelCase    :    Tuple 	=			num_hidden_layers
								__lowerCamelCase    :    Union[str, Any] 	=			num_attention_heads
								__lowerCamelCase    :    Dict 	=			intermediate_size
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    str 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    List[str] 	=			initializer_range
								__lowerCamelCase    :    Optional[int] 	=			layer_norm_eps
								__lowerCamelCase    :    Tuple 	=			image_size
								__lowerCamelCase    :    Union[str, Any] 	=			patch_size
								__lowerCamelCase    :    Optional[Any] 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			qkv_bias




