
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_		(       __lowercase   :	int       ,					__lowercase   :	List[str] ) ->  Tuple:
			_snake_case		=			checkpoint

			_snake_case		=			{}

			_snake_case		=			vae_state_dict['encoder.conv_in.weight']
			_snake_case		=			vae_state_dict['encoder.conv_in.bias']
			_snake_case		=			vae_state_dict['encoder.conv_out.weight']
			_snake_case		=			vae_state_dict['encoder.conv_out.bias']
			_snake_case		=			vae_state_dict['encoder.norm_out.weight']
			_snake_case		=			vae_state_dict['encoder.norm_out.bias']

			_snake_case		=			vae_state_dict['decoder.conv_in.weight']
			_snake_case		=			vae_state_dict['decoder.conv_in.bias']
			_snake_case		=			vae_state_dict['decoder.conv_out.weight']
			_snake_case		=			vae_state_dict['decoder.conv_out.bias']
			_snake_case		=			vae_state_dict['decoder.norm_out.weight']
			_snake_case		=			vae_state_dict['decoder.norm_out.bias']

			_snake_case		=			vae_state_dict['quant_conv.weight']
			_snake_case		=			vae_state_dict['quant_conv.bias']
			_snake_case		=			vae_state_dict['post_quant_conv.weight']
			_snake_case		=			vae_state_dict['post_quant_conv.bias']

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

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

			for i in range(__lowercase ):
						_snake_case		=			[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:
									_snake_case		=			vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.weight''' )
									_snake_case		=			vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.bias''' )

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

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

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

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

			for i in range(__lowercase ):
						_snake_case		=			num_up_blocks - 1 - i
						_snake_case		=			[
						    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:
									_snake_case		=			vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.weight'''
									]
									_snake_case		=			vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.bias'''
									]

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

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

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

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






def        a_		(       __lowercase   :	str       ,					__lowercase   :	str       ,					) ->  int:
			# Only support V1
			_snake_case		=			requests.get(
			    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml' )
			_snake_case		=			io.BytesIO(r.content )

			_snake_case		=			OmegaConf.load(__lowercase )
			_snake_case		=			512
			_snake_case		=			'cuda' if torch.cuda.is_available() else 'cpu'
			if checkpoint_path.endswith('safetensors' ):
						from safetensors import safe_open

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

			# Convert the VAE model.
			_snake_case		=			create_vae_diffusers_config(__lowercase       ,					image_size=__lowercase )
			_snake_case		=			custom_convert_ldm_vae_checkpoint(__lowercase       ,					__lowercase )

			_snake_case		=			AutoencoderKL(**__lowercase )
			vae.load_state_dict(__lowercase )
			vae.save_pretrained(__lowercase )


if __name__ == "__main__":
			_lowerCamelCase       :				Any       =			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       :				int       =			parser.parse_args()

			vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)

from collections.abc import Sequence






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			return sum(c * (x**i) for i, c in enumerate(__lowercase ) )






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	float ) ->  float:
			_snake_case		=			0.0
			for coeff in reversed(__lowercase ):
						_snake_case		=			result * x + coeff
			return result


if __name__ == "__main__":
			_lowerCamelCase       :				Optional[Any]       =			(0.0, 0.0, 5.0, 9.3, 7.0)
			_lowerCamelCase       :				Optional[int]       =			1_0.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))