




"""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 						UpperCAmelCase				(			A__:    List[str]							,				A__:    List[Any]     )  ->     int:
			__lowerCamelCase					:	Tuple	       =				checkpoint

			__lowerCamelCase					:	Tuple	       =				{}

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

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

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

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

						__lowerCamelCase					:	str	       =				renew_vae_resnet_paths(__a     )
						__lowerCamelCase					:	int	       =				{'old': f'''down.{i}.block''', 'new': f'''down_blocks.{i}.resnets'''}
						assign_to_checkpoint(__a							,				__a							,				__a							,				additional_replacements=[meta_path]							,				config=__a     )

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

						__lowerCamelCase					:	Dict	       =				renew_vae_resnet_paths(__a     )
						__lowerCamelCase					:	Optional[int]	       =				{'old': f'''mid.block_{i}''', 'new': f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(__a							,				__a							,				__a							,				additional_replacements=[meta_path]							,				config=__a     )

			__lowerCamelCase					:	Dict	       =				[key for key in vae_state_dict if 'encoder.mid.attn' in key]
			__lowerCamelCase					:	List[Any]	       =				renew_vae_attention_paths(__a     )
			__lowerCamelCase					:	str	       =				{'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     ):
						__lowerCamelCase					:	Optional[int]	       =				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					:	Union[str, Any]	       =				vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.weight'''
									]
									__lowerCamelCase					:	Optional[int]	       =				vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.bias'''
									]

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

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

						__lowerCamelCase					:	Union[str, Any]	       =				renew_vae_resnet_paths(__a     )
						__lowerCamelCase					:	Union[str, Any]	       =				{'old': f'''mid.block_{i}''', 'new': f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(__a							,				__a							,				__a							,				additional_replacements=[meta_path]							,				config=__a     )

			__lowerCamelCase					:	Optional[int]	       =				[key for key in vae_state_dict if 'decoder.mid.attn' in key]
			__lowerCamelCase					:	Tuple	       =				renew_vae_attention_paths(__a     )
			__lowerCamelCase					:	List[str]	       =				{'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 						UpperCAmelCase				(			A__:    str							,				A__:    str							,				)  ->     str:
			__lowerCamelCase					:	str	       =				requests.get(
			    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'     )
			__lowerCamelCase					:	str	       =				io.BytesIO(r.content     )

			__lowerCamelCase					:	str	       =				OmegaConf.load(__a     )
			__lowerCamelCase					:	int	       =				512
			__lowerCamelCase					:	Optional[Any]	       =				'cuda' if torch.cuda.is_available() else 'cpu'
			if checkpoint_path.endswith('safetensors'     ):
						from safetensors import safe_open

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

			# Convert the VAE model.
			__lowerCamelCase					:	str	       =				create_vae_diffusers_config(__a							,				image_size=__a     )
			__lowerCamelCase					:	Dict	       =				custom_convert_ldm_vae_checkpoint(__a							,				__a     )

			__lowerCamelCase					:	Any	       =				AutoencoderKL(**__a     )
			vae.load_state_dict(__a     )
			vae.save_pretrained(__a     )


if __name__ == "__main__":
					a_				:    int										=     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_				:    Optional[Any]										=     parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









"""simple docstring"""
a_				:    Dict										=     {
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
a_				:    Optional[int]										=     {
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}





def 						UpperCAmelCase				(			A__:    float							,				A__:    str							,				A__:    str     )  ->     float:
			__lowerCamelCase					:	List[Any]	       =				from_type.lower().strip('s'     )
			__lowerCamelCase					:	Optional[int]	       =				to_type.lower().strip('s'     )

			__lowerCamelCase					:	int	       =				UNIT_SYMBOL.get(A__							,				A__     )
			__lowerCamelCase					:	Dict	       =				UNIT_SYMBOL.get(A__							,				A__     )

			if from_sanitized not in METRIC_CONVERSION:
						__lowerCamelCase					:	Dict	       =				(
						    f'''Invalid \'from_type\' value: {from_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(A__     )}'''
						)
						raise ValueError(A__     )
			if to_sanitized not in METRIC_CONVERSION:
						__lowerCamelCase					:	Any	       =				(
						    f'''Invalid \'to_type\' value: {to_type!r}.\n'''
						    f'''Conversion abbreviations are: {", ".join(A__     )}'''
						)
						raise ValueError(A__     )
			__lowerCamelCase					:	Any	       =				METRIC_CONVERSION[from_sanitized]
			__lowerCamelCase					:	Tuple	       =				METRIC_CONVERSION[to_sanitized]
			__lowerCamelCase					:	Any	       =				1

			if from_exponent > to_exponent:
						__lowerCamelCase					:	Union[str, Any]	       =				from_exponent - to_exponent
			else:
						__lowerCamelCase					:	List[Any]	       =				-(to_exponent - from_exponent)

			return value * pow(10							,				A__     )


if __name__ == "__main__":
					from doctest import testmod

					testmod()



